modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-06 00:36:47
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 540
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
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SanjiWatsuki/openchat-3.5-1210-starling-slerp
|
SanjiWatsuki
| 2023-12-23T09:27:55Z | 1,395 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"conversational",
"en",
"license:cc-by-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-22T21:50:03Z |
---
license: cc-by-4.0
language:
- en
tags:
- merge
---
<!-- header start -->
# Model Description
This model uses the `Slerp` merge method from 2 models:
1. [openchat/openchat-3.5-1210](https://huggingface.co/openchat/openchat-3.5-1210)
2. [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha)
- base model: [openchat/openchat-3.5-1210](https://huggingface.co/openchat/openchat-3.5-1210)
I SLERPed these two together because they're both OpenChat-ish models. Fundamentally, OpenChat-3.5-1210 appears to be trained similarly to OpenChat-3.5 but now with [Feedback-Collection](https://huggingface.co/datasets/kaist-ai/Feedback-Collection)
and [a de-contaminated Capybara](https://huggingface.co/datasets/LDJnr/Capybara). Starling is OpenChat-3.5 but trained with a novel training method on the Nectar set.
My hope is that a SLERP between the two retains the benefits of both.
The yaml config file for this model is here:
```yaml
slices:
- sources:
- model: openchat/openchat-3.5-1210
layer_range: [0, 32]
- model: berkeley-nest/Starling-LM-7B-alpha
layer_range: [0, 32]
merge_method: slerp
base_model: openchat/openchat-3.5-1210
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
|
adlumal/AusLegalQA-Mixtral-8x7B-Instruct-v0.1
|
adlumal
| 2023-12-23T09:25:55Z | 27 | 2 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"law",
"legal",
"australia",
"conversational",
"dataset:umarbutler/open-australian-legal-qa",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-22T00:45:05Z |
---
license: apache-2.0
datasets:
- umarbutler/open-australian-legal-qa
tags:
- law
- legal
- australia
---
# AusLegalQA
AusLegalQA is a fine-tune of [Mistral-8x7B-Instruct-0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) using PEFT techniques, trained on the [Open Australian Legal QA](https://huggingface.co/datasets/umarbutler/open-australian-legal-qa).
The model achieved an eval loss of 1.1391 on a subset of 100 prompts and answers from the original dataset.
The model was trained with the following hyperparameters for 3 epochs. The step with the lowest eval loss was selected (coinciding with end of epoch 2) and the resulting qLoRA (4 bits) was merged into the base model.
| Hyperparameter | Value |
| --- | --- |
| Sequence length | 1024 |
| Epochs | 2 |
| Optimiser | AdamW |
| Learning rate | 1e-4 |
| Learning rate scheduler | Cosine |
| Batch size | 1 |
| Weight decay | 0.01 |
| Warmup ratio | 0.05 |
| LoRA rank | 64 |
| LoRA alpha | 128 |
| LoRA dropout | 0.1 |
| LoRA target | q_proj,v_proj |
| NEFTune alpha | 5 |
| Flash Attention | on |
## Strengths
The model is strong at summarisation and short-form answers with the key details. It is more likely to provide responses which assume the user is located in Australia. Ideal use-case is in a LLamaIndex/LangChain environment.
## Limitations
Just as the base model it does not have any moderation mechanisms.
|
MaksKhramtsov/bert-base-cased-finetuned-wikitext2
|
MaksKhramtsov
| 2023-12-23T09:17:31Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bert",
"fill-mask",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-12-23T08:55:33Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: MaksKhramtsov/bert-base-cased-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MaksKhramtsov/bert-base-cased-finetuned-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 6.9564
- Validation Loss: 6.9197
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 7.4237 | 7.0224 | 0 |
| 6.9564 | 6.9197 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
qwebeklu4ik/bert-base-cased-finetuned-wikitext2
|
qwebeklu4ik
| 2023-12-23T08:58:24Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bert",
"fill-mask",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-12-23T08:38:14Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: qwebeklu4ik/bert-base-cased-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# qwebeklu4ik/bert-base-cased-finetuned-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 6.9707
- Validation Loss: 6.8940
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 7.4297 | 7.0645 | 0 |
| 6.9707 | 6.8940 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
olga-mi-2002/bert-base-cased-finetuned-wikitext2
|
olga-mi-2002
| 2023-12-23T08:54:50Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bert",
"fill-mask",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-12-23T08:32:43Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: olga-mi-2002/bert-base-cased-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# olga-mi-2002/bert-base-cased-finetuned-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 6.9575
- Validation Loss: 6.8966
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 7.4333 | 7.0572 | 0 |
| 6.9575 | 6.8966 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
carles-undergrad-thesis/st-indobert-mmarco-inbatch
|
carles-undergrad-thesis
| 2023-12-23T08:54:06Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-12-23T08:53:16Z |
---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# carles-undergrad-thesis/st-indobert-mmarco-inbatch
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('carles-undergrad-thesis/st-indobert-mmarco-inbatch')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('carles-undergrad-thesis/st-indobert-mmarco-inbatch')
model = AutoModel.from_pretrained('carles-undergrad-thesis/st-indobert-mmarco-inbatch')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=carles-undergrad-thesis/st-indobert-mmarco-inbatch)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 16649 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 1.0, 'similarity_fct': 'dot_score'}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 1000000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"correct_bias": false,
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 8324,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
MaksKhramtsov/gpt2-finetuned-wikitext2
|
MaksKhramtsov
| 2023-12-23T08:53:05Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-22T21:04:09Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_keras_callback
model-index:
- name: MaksKhramtsov/gpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MaksKhramtsov/gpt2-finetuned-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 6.4992
- Validation Loss: 6.3552
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 7.3172 | 6.7732 | 0 |
| 6.4992 | 6.3552 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
Deer8dog9/nm001
|
Deer8dog9
| 2023-12-23T08:49:33Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-23T01:39:23Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: nm001
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# nm001
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0568
- Matthews Correlation: 0.5400
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.2181 | 1.0 | 535 | 0.5151 | 0.5129 |
| 0.1866 | 2.0 | 1070 | 0.6990 | 0.5327 |
| 0.1425 | 3.0 | 1605 | 0.9239 | 0.5117 |
| 0.103 | 4.0 | 2140 | 1.0568 | 0.5400 |
| 0.0666 | 5.0 | 2675 | 1.0856 | 0.5328 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
qwebeklu4ik/gpt2-finetuned-wikitext2
|
qwebeklu4ik
| 2023-12-23T08:34:02Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-23T08:13:45Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_keras_callback
model-index:
- name: qwebeklu4ik/gpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# qwebeklu4ik/gpt2-finetuned-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 6.4972
- Validation Loss: 6.3533
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 7.3132 | 6.7642 | 0 |
| 6.4972 | 6.3533 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
Cloud1989/Taxi-v3-Cloud1989
|
Cloud1989
| 2023-12-23T08:22:56Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-23T08:22:53Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3-Cloud1989
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.67
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Cloud1989/Taxi-v3-Cloud1989", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
ewfian/xlm-roberta-ner-ja-v2
|
ewfian
| 2023-12-23T08:20:55Z | 4 | 1 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-23T08:11:51Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-ner-ja-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-ner-ja-v2
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0892
- F1: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 1961 | 0.0634 | 0.9949 |
| No log | 2.0 | 3922 | 0.0702 | 0.9968 |
| No log | 3.0 | 5883 | 0.0681 | 0.9968 |
| No log | 4.0 | 7844 | 0.0804 | 1.0 |
| No log | 5.0 | 9805 | 0.0892 | 1.0 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.1+cu118
- Datasets 2.16.0
- Tokenizers 0.15.0
|
Shriganesh/bert-finetuned-squad
|
Shriganesh
| 2023-12-23T08:16:04Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-12-23T07:25:27Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: Shriganesh/bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Shriganesh/bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6861
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1875, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.7625 | 0 |
| 0.9591 | 1 |
| 0.6861 | 2 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
Gummybear05/whisper-small-ko-E30_Y_freq_speed-SA
|
Gummybear05
| 2023-12-23T08:15:17Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"hi",
"dataset:aihub_elder",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-23T02:12:07Z |
---
language:
- hi
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- aihub_elder
model-index:
- name: whisper-small-ko-E50_Y_freq_speed-SA
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-ko-E50_Y_freq_speed-SA
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the aihub Y dialogue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1737
- Cer: 5.7155
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4988 | 0.13 | 100 | 0.2885 | 7.1840 |
| 0.3371 | 0.26 | 200 | 0.2180 | 5.7977 |
| 0.2889 | 0.39 | 300 | 0.2138 | 6.25 |
| 0.258 | 0.52 | 400 | 0.2019 | 5.7977 |
| 0.2357 | 0.64 | 500 | 0.1965 | 5.4688 |
| 0.219 | 0.77 | 600 | 0.1865 | 6.2852 |
| 0.2119 | 0.9 | 700 | 0.1832 | 5.3160 |
| 0.1416 | 1.03 | 800 | 0.1778 | 5.1692 |
| 0.126 | 1.16 | 900 | 0.1813 | 5.0576 |
| 0.1346 | 1.29 | 1000 | 0.1778 | 5.0047 |
| 0.1205 | 1.42 | 1100 | 0.1778 | 4.9518 |
| 0.1121 | 1.55 | 1200 | 0.1745 | 4.9283 |
| 0.1259 | 1.68 | 1300 | 0.1736 | 6.1149 |
| 0.1128 | 1.81 | 1400 | 0.1739 | 5.6978 |
| 0.1027 | 1.93 | 1500 | 0.1737 | 5.7155 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
GlebPS/gpt2-finetuned-wikitext2
|
GlebPS
| 2023-12-23T08:14:29Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-23T07:52:56Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_keras_callback
model-index:
- name: GlebPS/gpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# GlebPS/gpt2-finetuned-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 6.4884
- Validation Loss: 6.3422
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 7.3133 | 6.7565 | 0 |
| 6.4884 | 6.3422 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
Cloud1989/q-FrozenLake-v1-4x4-noSlippery
|
Cloud1989
| 2023-12-23T08:11:29Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-23T08:11:26Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Cloud1989/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
polina164/bert-base-cased-finetuned-wikitext2
|
polina164
| 2023-12-23T08:05:50Z | 1 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bert",
"fill-mask",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-12-23T07:45:18Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: polina164/bert-base-cased-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# polina164/bert-base-cased-finetuned-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 6.9651
- Validation Loss: 6.9192
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 7.4286 | 7.0417 | 0 |
| 6.9651 | 6.9192 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
nanami/gpt2-imdb-neg-peft-ppo-DibertaimdbReward
|
nanami
| 2023-12-23T08:00:41Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:lvwerra/gpt2-imdb",
"base_model:adapter:lvwerra/gpt2-imdb",
"region:us"
] | null | 2023-12-23T08:00:39Z |
---
library_name: peft
base_model: lvwerra/gpt2-imdb
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
Pongsaky/q-FrozenLake-v1-4x4-noSlippery
|
Pongsaky
| 2023-12-23T07:44:05Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-23T07:44:01Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Pongsaky/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
polina164/gpt2-finetuned-wikitext2
|
polina164
| 2023-12-23T07:38:38Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-23T07:17:56Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_keras_callback
model-index:
- name: polina164/gpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# polina164/gpt2-finetuned-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 6.4867
- Validation Loss: 6.3421
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 7.3108 | 6.7566 | 0 |
| 6.4867 | 6.3421 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
hfl/chinese-alpaca-2-lora-7b-16k
|
hfl
| 2023-12-23T07:29:21Z | 6 | 1 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"zh",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-31T09:00:45Z |
---
license: apache-2.0
language:
- zh
- en
---
# Chinese-Alpaca-2-LoRA-7B-16K
**This is the LoRA model for Chinese-Alpaca-2-7B-16K (context size 16K)，which should be merged with original Llama-2-7b-hf model before inference or training.**
**Related models👇**
* Long context base models (16K)
* [Chinese-LLaMA-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-7b-16k)
* [Chinese-LLaMA-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b-16k)
* [Chinese-LLaMA-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-13b-16k)
* [Chinese-LLaMA-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b-16k)
* Long context Instruction/Chat models
* [Chinese-Alpaca-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b-16k)
* [Chinese-Alpaca-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b-16k)
* [Chinese-Alpaca-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b-16k)
* [Chinese-Alpaca-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b-16k)
* Base models
* [Chinese-LLaMA-2-7B (full model)](https://huggingface.co/hfl/chinese-llama-2-7b)
* [Chinese-LLaMA-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b)
* [Chinese-LLaMA-2-13B (full model)](https://huggingface.co/hfl/chinese-llama-2-13b)
* [Chinese-LLaMA-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b)
* Instruction/Chat models
* [Chinese-Alpaca-2-7B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b)
* [Chinese-Alpaca-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b)
* [Chinese-Alpaca-2-13B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b)
* [Chinese-Alpaca-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b)
# Description of Chinese-LLaMA-Alpaca-2
This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.
The main contents of this project include:
* 🚀 New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs.
* 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data
* 🚀 Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC
* 🚀 Support for LLaMA ecosystems like 🤗transformers, llama.cpp, text-generation-webui, LangChain, vLLM etc.
Please refer to [https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/) for details.
|
hfl/chinese-alpaca-2-13b
|
hfl
| 2023-12-23T07:29:14Z | 1,633 | 83 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"zh",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-14T03:10:08Z |
---
license: apache-2.0
language:
- zh
- en
---
# Chinese-Alpaca-2-13B
**This is the full Chinese-Alpaca-2-13B model，which can be loaded directly for inference and full-parameter training.**
**Related models👇**
* Long context base models
* [Chinese-LLaMA-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-7b-16k)
* [Chinese-LLaMA-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b-16k)
* [Chinese-LLaMA-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-13b-16k)
* [Chinese-LLaMA-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b-16k)
* Base models
* [Chinese-LLaMA-2-7B (full model)](https://huggingface.co/hfl/chinese-llama-2-7b)
* [Chinese-LLaMA-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b)
* [Chinese-LLaMA-2-13B (full model)](https://huggingface.co/hfl/chinese-llama-2-13b)
* [Chinese-LLaMA-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b)
* Instruction/Chat models
* [Chinese-Alpaca-2-7B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b)
* [Chinese-Alpaca-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b)
* [Chinese-Alpaca-2-13B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b)
* [Chinese-Alpaca-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b)
# Description of Chinese-LLaMA-Alpaca-2
This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.
The main contents of this project include:
* 🚀 New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs.
* 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data
* 🚀 Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC
* 🚀 Support for LLaMA ecosystems like 🤗transformers, llama.cpp, text-generation-webui, LangChain, vLLM etc.
Please refer to [https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/) for details.
|
hfl/chinese-alpaca-2-lora-13b-16k
|
hfl
| 2023-12-23T07:27:51Z | 8 | 3 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"zh",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-31T09:00:57Z |
---
license: apache-2.0
language:
- zh
- en
---
# Chinese-Alpaca-2-LoRA-13B-16K
**This is the LoRA model for Chinese-Alpaca-2-13B-16K (context size 16K)，which should be merged with original Llama-2-13b-hf model before inference or training.**
**Related models👇**
* Long context base models (16K)
* [Chinese-LLaMA-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-7b-16k)
* [Chinese-LLaMA-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b-16k)
* [Chinese-LLaMA-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-13b-16k)
* [Chinese-LLaMA-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b-16k)
* Long context Instruction/Chat models
* [Chinese-Alpaca-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b-16k)
* [Chinese-Alpaca-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b-16k)
* [Chinese-Alpaca-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b-16k)
* [Chinese-Alpaca-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b-16k)
* Base models
* [Chinese-LLaMA-2-7B (full model)](https://huggingface.co/hfl/chinese-llama-2-7b)
* [Chinese-LLaMA-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b)
* [Chinese-LLaMA-2-13B (full model)](https://huggingface.co/hfl/chinese-llama-2-13b)
* [Chinese-LLaMA-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b)
* Instruction/Chat models
* [Chinese-Alpaca-2-7B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b)
* [Chinese-Alpaca-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b)
* [Chinese-Alpaca-2-13B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b)
* [Chinese-Alpaca-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b)
# Description of Chinese-LLaMA-Alpaca-2
This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.
The main contents of this project include:
* 🚀 New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs.
* 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data
* 🚀 Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC
* 🚀 Support for LLaMA ecosystems like 🤗transformers, llama.cpp, text-generation-webui, LangChain, vLLM etc.
Please refer to [https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/) for details.
|
hfl/chinese-alpaca-2-13b-16k
|
hfl
| 2023-12-23T07:27:41Z | 1,487 | 29 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"zh",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-31T13:47:47Z |
---
license: apache-2.0
language:
- zh
- en
---
# Chinese-LLaMA-2-13B-16K
**This is the full Chinese-LLaMA-2-13B-16K (context size 16K)，model，which can be loaded directly for inference and full-parameter training.**
**Related models👇**
* Long context base models (16K)
* [Chinese-LLaMA-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-7b-16k)
* [Chinese-LLaMA-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b-16k)
* [Chinese-LLaMA-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-13b-16k)
* [Chinese-LLaMA-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b-16k)
* Long context Instruction/Chat models
* [Chinese-Alpaca-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b-16k)
* [Chinese-Alpaca-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b-16k)
* [Chinese-Alpaca-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b-16k)
* [Chinese-Alpaca-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b-16k)
* Base models
* [Chinese-LLaMA-2-7B (full model)](https://huggingface.co/hfl/chinese-llama-2-7b)
* [Chinese-LLaMA-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b)
* [Chinese-LLaMA-2-13B (full model)](https://huggingface.co/hfl/chinese-llama-2-13b)
* [Chinese-LLaMA-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b)
* Instruction/Chat models
* [Chinese-Alpaca-2-7B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b)
* [Chinese-Alpaca-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b)
* [Chinese-Alpaca-2-13B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b)
* [Chinese-Alpaca-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b)
# Description of Chinese-LLaMA-Alpaca-2
This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.
The main contents of this project include:
* 🚀 New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs.
* 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data
* 🚀 Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC
* 🚀 Support for LLaMA ecosystems like 🤗transformers, llama.cpp, text-generation-webui, LangChain, vLLM etc.
Please refer to [https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/) for details.
|
hfl/chinese-llama-2-lora-13b-16k
|
hfl
| 2023-12-23T07:26:10Z | 9 | 3 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-25T00:40:27Z |
---
license: apache-2.0
language:
- zh
---
# Chinese-LLaMA-2-LoRA-13B-16K
**This is the LoRA model for Chinese-LLaMA-2-13B-16K (context size 16K)，which should be merged with original Llama-2-13b-hf model before inference or training.**
**Related models👇**
* Long context base models (16K)
* [Chinese-LLaMA-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-7b-16k)
* [Chinese-LLaMA-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b-16k)
* [Chinese-LLaMA-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-13b-16k)
* [Chinese-LLaMA-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b-16k)
* Base models
* [Chinese-LLaMA-2-7B (full model)](https://huggingface.co/hfl/chinese-llama-2-7b)
* [Chinese-LLaMA-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b)
* [Chinese-LLaMA-2-13B (full model)](https://huggingface.co/hfl/chinese-llama-2-13b)
* [Chinese-LLaMA-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b)
* Instruction/Chat models
* [Chinese-Alpaca-2-7B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b)
* [Chinese-Alpaca-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b)
* [Chinese-Alpaca-2-13B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b)
* [Chinese-Alpaca-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b)
# Description of Chinese-LLaMA-Alpaca-2
This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.
The main contents of this project include:
* 🚀 New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs.
* 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data
* 🚀 Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC
* 🚀 Support for LLaMA ecosystems like 🤗transformers, llama.cpp, text-generation-webui, LangChain, vLLM etc.
Please refer to [https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/) for details.
|
hfl/chinese-llama-2-7b-16k
|
hfl
| 2023-12-23T07:25:41Z | 14 | 11 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"zh",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-25T01:12:21Z |
---
license: apache-2.0
language:
- zh
- en
---
# Chinese-LLaMA-2-7B-16K
**This is the full Chinese-LLaMA-2-7B-16K (context size 16K)，model，which can be loaded directly for inference and full-parameter training.**
**Related models👇**
* Long context base models (16K)
* [Chinese-LLaMA-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-7b-16k)
* [Chinese-LLaMA-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b-16k)
* [Chinese-LLaMA-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-13b-16k)
* [Chinese-LLaMA-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b-16k)
* Long context Instruction/Chat models
* [Chinese-Alpaca-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b-16k)
* [Chinese-Alpaca-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b-16k)
* [Chinese-Alpaca-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b-16k)
* [Chinese-Alpaca-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b-16k)
* Base models
* [Chinese-LLaMA-2-7B (full model)](https://huggingface.co/hfl/chinese-llama-2-7b)
* [Chinese-LLaMA-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b)
* [Chinese-LLaMA-2-13B (full model)](https://huggingface.co/hfl/chinese-llama-2-13b)
* [Chinese-LLaMA-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b)
* Instruction/Chat models
* [Chinese-Alpaca-2-7B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b)
* [Chinese-Alpaca-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b)
* [Chinese-Alpaca-2-13B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b)
* [Chinese-Alpaca-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b)
# Description of Chinese-LLaMA-Alpaca-2
This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.
The main contents of this project include:
* 🚀 New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs.
* 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data
* 🚀 Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC
* 🚀 Support for LLaMA ecosystems like 🤗transformers, llama.cpp, text-generation-webui, LangChain, vLLM etc.
Please refer to [https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/) for details.
|
hfl/chinese-llama-2-lora-13b
|
hfl
| 2023-12-23T07:25:22Z | 0 | 4 | null |
[
"zh",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-08-11T04:41:01Z |
---
license: apache-2.0
language:
- zh
- en
---
# Chinese-LLaMA-2-LoRA-13B
**This is the LoRA model for Chinese-LLaMA-2-13B，which should be merged with original Llama-2-13b-hf model before inference or training.**
**Related models👇**
* Long context base models
* [Chinese-LLaMA-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-7b-16k)
* [Chinese-LLaMA-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b-16k)
* [Chinese-LLaMA-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-13b-16k)
* [Chinese-LLaMA-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b-16k)
* Base models
* [Chinese-LLaMA-2-7B (full model)](https://huggingface.co/hfl/chinese-llama-2-7b)
* [Chinese-LLaMA-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b)
* [Chinese-LLaMA-2-13B (full model)](https://huggingface.co/hfl/chinese-llama-2-13b)
* [Chinese-LLaMA-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b)
* Instruction/Chat models
* [Chinese-Alpaca-2-7B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b)
* [Chinese-Alpaca-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b)
* [Chinese-Alpaca-2-13B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b)
* [Chinese-Alpaca-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b)
# Description of Chinese-LLaMA-Alpaca-2
This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.
The main contents of this project include:
* 🚀 New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs.
* 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data
* 🚀 Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC
* 🚀 Support for LLaMA ecosystems like 🤗transformers, llama.cpp, text-generation-webui, LangChain, vLLM etc.
Please refer to [https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/) for details.
|
themanas021/llama2-themanas-MATH_aLgEbRa
|
themanas021
| 2023-12-23T07:24:46Z | 1 | 1 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"dataset:themanas021/MATH-Algebra",
"base_model:togethercomputer/Llama-2-7B-32K-Instruct",
"base_model:finetune:togethercomputer/Llama-2-7B-32K-Instruct",
"license:llama2",
"region:us"
] | null | 2023-12-19T01:36:38Z |
---
license: llama2
base_model: togethercomputer/Llama-2-7B-32K-Instruct
tags:
- generated_from_trainer
model-index:
- name: llama2-themanas-MATH_aLgEbRa
results: []
datasets:
- themanas021/MATH-Algebra
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama2-themanas-MATH_aLgEbRa
This model is a fine-tuned version of [togethercomputer/Llama-2-7B-32K-Instruct](https://huggingface.co/togethercomputer/Llama-2-7B-32K-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2047
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7178 | 0.48 | 3 | 1.5652 |
| 1.4869 | 0.96 | 6 | 1.3622 |
| 1.2911 | 1.44 | 9 | 1.2362 |
| 1.2598 | 1.92 | 12 | 1.2047 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
marty1885/streaming-piper
|
marty1885
| 2023-12-23T07:21:28Z | 0 | 1 | null |
[
"onnx",
"license:mit",
"region:us"
] | null | 2023-12-23T07:15:24Z |
---
license: mit
---
# Streaming piper
A collection of legal TTS [Piper](https://github.com/rhasspy/piper) modles to use in [Paroli](https://github.com/marty1885/paroli). Including RKNN
|
hfl/chinese-llama-2-7b
|
hfl
| 2023-12-23T07:20:49Z | 705 | 100 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"zh",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-27T06:54:32Z |
---
license: apache-2.0
language:
- zh
- en
---
# Chinese-LLaMA-2-7B
**This is the full Chinese-LLaMA-2-7B model，which can be loaded directly for inference and full-parameter training.**
**Related models👇**
* Long context base models
* [Chinese-LLaMA-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-7b-16k)
* [Chinese-LLaMA-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b-16k)
* [Chinese-LLaMA-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-13b-16k)
* [Chinese-LLaMA-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b-16k)
* Base models
* [Chinese-LLaMA-2-7B (full model)](https://huggingface.co/hfl/chinese-llama-2-7b)
* [Chinese-LLaMA-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b)
* [Chinese-LLaMA-2-13B (full model)](https://huggingface.co/hfl/chinese-llama-2-13b)
* [Chinese-LLaMA-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b)
* Instruction/Chat models
* [Chinese-Alpaca-2-7B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b)
* [Chinese-Alpaca-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b)
* [Chinese-Alpaca-2-13B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b)
* [Chinese-Alpaca-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b)
# Description of Chinese-LLaMA-Alpaca-2
This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.
The main contents of this project include:
* 🚀 New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs.
* 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data
* 🚀 Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC
* 🚀 Support for LLaMA ecosystems like 🤗transformers, llama.cpp, text-generation-webui, LangChain, vLLM etc.
Please refer to [https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/) for details.
|
LoneStriker/SAM-5.0bpw-h6-exl2
|
LoneStriker
| 2023-12-23T06:55:33Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-23T06:53:32Z |
---
license: apache-2.0
language:
- en
---
# Model Card
SAM (Small Agentic Model), a 7B model that demonstrates impressive reasoning abilities despite its smaller size. SAM-7B has outperformed existing SoTA models on various reasoning benchmarks, including GSM8k and ARC-C.
For full details of this model please read our [release blog post](https://superagi.com/introducing-sam-small-agentic-model/).
# Key Contributions
- SAM-7B outperforms GPT 3.5, Orca, and several other 70B models on multiple reasoning benchmarks, including ARC-C and GSM8k.
- Interestingly, despite being trained on a 97% smaller dataset, SAM-7B surpasses Orca-13B on GSM8k.
- All responses in our fine-tuning dataset are generated by open-source models without any assistance from state-of-the-art models like GPT-3.5 or GPT-4.
## Training
- Trained by: SuperAGI Team
- Hardware: NVIDIA 6 x H100 SxM (80GB)
- Model used: Mistral 7B
- Duration of finetuning: 4 hours
- Number of epochs: 1
- Batch size: 16
- Learning Rate: 2e-5
- Warmup Ratio: 0.1
- Optmizer: AdamW
- Scheduler: Cosine
## Example Prompt
The template used to build a prompt for the Instruct model is defined as follows:
```
<s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST]
```
Note that `<s>` and `</s>` are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings.
## Evaluation
These benchmarks show that our model has improved reasoning as compared to orca 2-7b, orca 2-13b and GPT-3.5.
Despite being smaller in size, we show better multi-hop reasoning, as shown below:
<img src = "https://superagi.com/wp-content/uploads/2023/12/image-932.png" alt="Reasoning Benchmark Performance" width="700">
Note: Temperature=0.3 is the suggested for optimal performance
## Run the model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "SuperAGI/SAM"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
text = "Can elephants fly?"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Limitations
SAM is a demonstration that better reasoning can be induced using less but high-quality data generated using OpenSource LLMs.
The model is not suitable for conversations and simple Q&A, it performs better in task breakdown and reasoning only.
It does not have any moderation mechanisms. Therefore, the model is not suitable for production usage as it doesn't have guardrails for toxicity, societal bias, and language limitations. We would love to collaborate with the community to build safer and better models.
## The SuperAGI AI Team
Anmol Gautam, Arkajit Datta, Rajat Chawla, Ayush Vatsal, Sukrit Chatterjee, Adarsh Jha, Abhijeet Sinha, Rakesh Krishna, Adarsh Deep, Ishaan Bhola, Mukunda NS, Nishant Gaurav.
|
ohwi/japanese-stablelm-instruct-gamma-7b-dpo-uf-v0
|
ohwi
| 2023-12-23T06:53:31Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"japanese-stablelm",
"causal-lm",
"conversational",
"ja",
"dataset:argilla/ultrafeedback-binarized-preferences-cleaned",
"arxiv:2310.06825",
"base_model:stabilityai/japanese-stablelm-instruct-gamma-7b",
"base_model:finetune:stabilityai/japanese-stablelm-instruct-gamma-7b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-21T14:10:58Z |
---
language:
- ja
tags:
- japanese-stablelm
- causal-lm
pipeline_tag: text-generation
base_model: stabilityai/japanese-stablelm-instruct-gamma-7b
datasets: argilla/ultrafeedback-binarized-preferences-cleaned
license: apache-2.0
extra_gated_fields:
Name: text
Email: text
Country: text
Organization or Affiliation: text
I allow Stability AI to contact me about information related to its models and research: checkbox
---
# Japanese Stable LM Instruct Gamma 7B + DPO
## Model Description
This is a 7B-parameter decoder-only Japanese language model fine-tuned on preference datasets, built on top of the STF model [Japanese Stable LM Instruct Gamma 7B](https://huggingface.co/stabilityai/japanese-stablelm-instruct-gamma-7b).
This model is trained with [notus](https://github.com/argilla-io/notus) code base.
### Training Datasets
- Machine Translated [Ultrafeedback dataset](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned)
The dataset is machine translated version of Ultrafeedback. Some samples are missing because of API request failure.
Will redeem the dataset and train again.
### Benchmarks
| Model | Average | jcommonsenseqa | jnli | marc_ja | jsquad | jaqket_v2 | xlsum_ja | xwinograd_ja | mgsm |
|-------------------------------------|-----------|----------------|-----------|-----------|-----------|-----------|-----------|--------------|-----------|
| japanese-stablelm-instruct-gamma-7b | 59.86 | 83.47 | 18.65 | **95.79** | **76.29** | **82.13** | 21.47 | 81.44 | 19.60 |
| this model | **63.28** | **87.04** | **43.84** | 95.65 | 75.30 | 80.24 | **22.25** | **81.54** | **20.40** |
These benchmark performances are evaluated by [JP Language Model Evaluation Harness](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable).
⚠️ *Please note that benchmark performances of `japanese-stablelm-instruct-gamma-7b` are not official. These results are evaluated in this work unoffically.*
---
( Below is the original readme of `Japanese Stable LM Instruct Gamma 7B` )
<br>
# Japanese Stable LM Instruct Gamma 7B
## Model Description
This is a 7B-parameter decoder-only Japanese language model fine-tuned on instruction-following datasets, built on top of the base model [Japanese Stable LM Base Gamma 7B](https://huggingface.co/stabilityai/japanese-stablelm-base-gamma-7b).
*If you are in search of a smaller model, please check [Japanese StableLM-3B-4E1T Instruct](https://huggingface.co/stabilityai/japanese-stablelm-3b-4e1t-base/blob/main/README.md).*
## Usage
Ensure you are using Transformers 4.34.0 or newer.
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("stabilityai/japanese-stablelm-instruct-gamma-7b")
model = AutoModelForCausalLM.from_pretrained(
"stabilityai/japanese-stablelm-instruct-gamma-7b",
torch_dtype="auto",
)
model.eval()
if torch.cuda.is_available():
model = model.to("cuda")
def build_prompt(user_query, inputs="", sep="\n\n### "):
sys_msg = "以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさい。"
p = sys_msg
roles = ["指示", "応答"]
msgs = [": \n" + user_query, ": \n"]
if inputs:
roles.insert(1, "入力")
msgs.insert(1, ": \n" + inputs)
for role, msg in zip(roles, msgs):
p += sep + role + msg
return p
# Infer with prompt without any additional input
user_inputs = {
"user_query": "与えられたことわざの意味を小学生でも分かるように教えてください。",
"inputs": "情けは人のためならず"
}
prompt = build_prompt(**user_inputs)
input_ids = tokenizer.encode(
prompt,
add_special_tokens=True,
return_tensors="pt"
)
tokens = model.generate(
input_ids.to(device=model.device),
max_new_tokens=256,
temperature=1,
top_p=0.95,
do_sample=True,
)
out = tokenizer.decode(tokens[0][input_ids.shape[1]:], skip_special_tokens=True).strip()
print(out)
```
## Model Details
* **Developed by**: [Stability AI](https://stability.ai/)
* **Model type**: `Japanese Stable LM Instruct Gamma 7B` model is an auto-regressive language model based on the transformer decoder architecture.
* **Language(s)**: Japanese
* **License**: This model is licensed under [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
* **Contact**: For questions and comments about the model, please join [Stable Community Japan](https://discord.gg/StableJP). For future announcements / information about Stability AI models, research, and events, please follow https://twitter.com/StabilityAI_JP.
### Model Architecture
For details, please see Mistral AI's [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/).
### Training Datasets
- [Japanese translation of the Databricks Dolly-15k dataset](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja)
- [Japanese translation of the subset of the Anthropic HH dataset](https://huggingface.co/datasets/fujiki/japanese_hh-rlhf-49k)
- [Wikinews](https://ja.wikinews.org/wi) [subset](https://huggingface.co/datasets/fujiki/llm-japanese-dataset_wikinews) of the [izumi-lab/llm-japanese-dataset](https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset)
## Use and Limitations
### Intended Use
The model is intended to be used by all individuals as a foundational model for application-specific fine-tuning without strict limitations on commercial use.
### Limitations and bias
The pre-training dataset may have contained offensive or inappropriate content even after applying data cleansing filters which can be reflected in the model-generated text. We recommend users exercise reasonable caution when using these models in production systems. Do not use the model for any applications that may cause harm or distress to individuals or groups.
## Credits
The fine-tuning was carried out by [Fujiki Nakamura](https://huggingface.co/fujiki).
Other aspects, including data preparation and evaluation, were handled by the Language Team of Stability AI Japan, notably [Meng Lee](https://huggingface.co/leemeng), [Makoto Shing](https://huggingface.co/mkshing), [Paul McCann](https://huggingface.co/polm-stability), [Naoki Orii](https://huggingface.co/mrorii), and [Takuya Akiba](https://huggingface.co/iwiwi).
## Acknowledgements
This model is based on Mistral-7B-v0.1 released by the Mistral AI team. We are grateful to the Mistral AI team for providing such an excellent base model.
We are grateful for the contributions of the EleutherAI Polyglot-JA team in helping us to collect a large amount of pre-training data in Japanese. Polyglot-JA members includes Hyunwoong Ko (Project Lead), Fujiki Nakamura (originally started this project when he commited to the Polyglot team), Yunho Mo, Minji Jung, KeunSeok Im, and Su-Kyeong Jang.
We are also appreciative of [AI Novelist/Sta (Bit192, Inc.)](https://ai-novel.com/index.php) and the numerous contributors from [Stable Community Japan](https://discord.gg/VPrcE475HB) for assisting us in gathering a large amount of high-quality Japanese textual data for model training.
|
mithlesh/llama2_finetuned_chatbot
|
mithlesh
| 2023-12-23T06:50:23Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"region:us"
] | null | 2023-12-23T06:45:00Z |
---
tags:
- generated_from_trainer
model-index:
- name: llama2_finetuned_chatbot
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama2_finetuned_chatbot
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 20
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.13.3
|
allspace/distilbert-base-uncased-finetuned-emotion
|
allspace
| 2023-12-23T06:50:13Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-15T12:42:03Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9265
- name: F1
type: f1
value: 0.9264148990589147
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2191
- Accuracy: 0.9265
- F1: 0.9264
## Model description
| Label_0 | Label_1 | Label_2 | Label_3 | Label_4 | Label_5 |
|:-------:|:-------:|:-------:|:-------:|:-------:|:--------:|
| SADNESS | JOY | LOVE | ANGER | FEAR | SURPRISE |
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 250 | 0.3143 | 0.907 | 0.9060 |
| No log | 2.0 | 500 | 0.2191 | 0.9265 | 0.9264 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
tcyuan1017/HW02
|
tcyuan1017
| 2023-12-23T06:50:13Z | 1 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-12-23T06:13:52Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
lorenzreyes/ppo-Pyramids
|
lorenzreyes
| 2023-12-23T06:49:48Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-12-23T06:49:45Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: lorenzreyes/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
LoneStriker/SAM-3.0bpw-h6-exl2
|
LoneStriker
| 2023-12-23T06:44:07Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-23T06:42:49Z |
---
license: apache-2.0
language:
- en
---
# Model Card
SAM (Small Agentic Model), a 7B model that demonstrates impressive reasoning abilities despite its smaller size. SAM-7B has outperformed existing SoTA models on various reasoning benchmarks, including GSM8k and ARC-C.
For full details of this model please read our [release blog post](https://superagi.com/introducing-sam-small-agentic-model/).
# Key Contributions
- SAM-7B outperforms GPT 3.5, Orca, and several other 70B models on multiple reasoning benchmarks, including ARC-C and GSM8k.
- Interestingly, despite being trained on a 97% smaller dataset, SAM-7B surpasses Orca-13B on GSM8k.
- All responses in our fine-tuning dataset are generated by open-source models without any assistance from state-of-the-art models like GPT-3.5 or GPT-4.
## Training
- Trained by: SuperAGI Team
- Hardware: NVIDIA 6 x H100 SxM (80GB)
- Model used: Mistral 7B
- Duration of finetuning: 4 hours
- Number of epochs: 1
- Batch size: 16
- Learning Rate: 2e-5
- Warmup Ratio: 0.1
- Optmizer: AdamW
- Scheduler: Cosine
## Example Prompt
The template used to build a prompt for the Instruct model is defined as follows:
```
<s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST]
```
Note that `<s>` and `</s>` are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings.
## Evaluation
These benchmarks show that our model has improved reasoning as compared to orca 2-7b, orca 2-13b and GPT-3.5.
Despite being smaller in size, we show better multi-hop reasoning, as shown below:
<img src = "https://superagi.com/wp-content/uploads/2023/12/image-932.png" alt="Reasoning Benchmark Performance" width="700">
Note: Temperature=0.3 is the suggested for optimal performance
## Run the model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "SuperAGI/SAM"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
text = "Can elephants fly?"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Limitations
SAM is a demonstration that better reasoning can be induced using less but high-quality data generated using OpenSource LLMs.
The model is not suitable for conversations and simple Q&A, it performs better in task breakdown and reasoning only.
It does not have any moderation mechanisms. Therefore, the model is not suitable for production usage as it doesn't have guardrails for toxicity, societal bias, and language limitations. We would love to collaborate with the community to build safer and better models.
## The SuperAGI AI Team
Anmol Gautam, Arkajit Datta, Rajat Chawla, Ayush Vatsal, Sukrit Chatterjee, Adarsh Jha, Abhijeet Sinha, Rakesh Krishna, Adarsh Deep, Ishaan Bhola, Mukunda NS, Nishant Gaurav.
|
bartowski/SAM-exl2
|
bartowski
| 2023-12-23T06:30:54Z | 1 | 0 | null |
[
"text-generation",
"en",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-12-23T05:00:15Z |
---
license: apache-2.0
language:
- en
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of SAM
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.11">turboderp's ExLlamaV2 v0.0.11</a> for quantization.
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using the default calibration dataset.
Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
Original model: https://huggingface.co/SuperAGI/SAM
<a href="https://huggingface.co/bartowski/SAM-exl2/tree/4_0">4.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/SAM-exl2/tree/5_0">5.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/SAM-exl2/tree/6_0">6.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/SAM-exl2/tree/8_0">8.0 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 4_0 https://huggingface.co/bartowski/SAM-exl2
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `SAM-exl2`:
```shell
mkdir SAM-exl2
huggingface-cli download bartowski/SAM-exl2 --local-dir SAM-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir SAM-exl2
huggingface-cli download bartowski/SAM-exl2 --revision 4_0 --local-dir SAM-exl2 --local-dir-use-symlinks False
```
|
timotewb/pokemon-lora
|
timotewb
| 2023-12-23T06:18:01Z | 1 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-12-23T04:01:34Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - timotewb/pokemon-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
|
Sayan1997/test
|
Sayan1997
| 2023-12-23T05:57:40Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:TinyPixel/Llama-2-7B-bf16-sharded",
"base_model:adapter:TinyPixel/Llama-2-7B-bf16-sharded",
"region:us"
] | null | 2023-12-23T05:57:32Z |
---
library_name: peft
base_model: TinyPixel/Llama-2-7B-bf16-sharded
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
leochenwj/HW001
|
leochenwj
| 2023-12-23T05:28:48Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-23T01:36:18Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: HW001
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HW001
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6698
- Matthews Correlation: 0.5049
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6.761900423264489e-06
- train_batch_size: 8
- eval_batch_size: 16
- seed: 15
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5001 | 1.0 | 1069 | 0.5301 | 0.4185 |
| 0.4071 | 2.0 | 2138 | 0.5218 | 0.4921 |
| 0.3302 | 3.0 | 3207 | 0.6424 | 0.4914 |
| 0.3009 | 4.0 | 4276 | 0.6698 | 0.5049 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
codewithaman/vit-base-patch16-224-in21k-finetuned-brain-ich
|
codewithaman
| 2023-12-23T05:10:50Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_keras_callback",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-23T05:01:45Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: dwiedarioo/vit-base-patch16-224-in21k-brainmri
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# winwithaman/vit-base-patch16-224-in21k-finetuned-brain-ich
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an brain hemorrhage dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2848
- Train Accuracy: 0.9969
- Train Top-3-accuracy: 0.9992
- Validation Loss: 0.3786
- Validation Accuracy: 0.9590
- Validation Top-3-accuracy: 0.9892
- Epoch: 7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1230, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 2.2199 | 0.4215 | 0.6564 | 1.8634 | 0.5702 | 0.8099 | 0 |
| 1.5448 | 0.6976 | 0.8797 | 1.3110 | 0.7603 | 0.9028 | 1 |
| 1.0494 | 0.8694 | 0.9519 | 0.9507 | 0.8855 | 0.9590 | 2 |
| 0.7408 | 0.9381 | 0.9824 | 0.7499 | 0.9114 | 0.9806 | 3 |
| 0.5428 | 0.9756 | 0.9939 | 0.5831 | 0.9460 | 0.9849 | 4 |
| 0.4169 | 0.9901 | 0.9977 | 0.4895 | 0.9525 | 0.9914 | 5 |
| 0.3371 | 0.9947 | 0.9977 | 0.4194 | 0.9611 | 0.9892 | 6 |
| 0.2848 | 0.9969 | 0.9992 | 0.3786 | 0.9590 | 0.9892 | 7 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Ivylin0725/HW01
|
Ivylin0725
| 2023-12-23T05:05:41Z | 14 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-23T01:38:54Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: HW01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HW01
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4777
- Matthews Correlation: 0.5233
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5152 | 1.0 | 535 | 0.4589 | 0.4572 |
| 0.3389 | 2.0 | 1070 | 0.4777 | 0.5233 |
| 0.2317 | 3.0 | 1605 | 0.6887 | 0.5032 |
| 0.1579 | 4.0 | 2140 | 0.7623 | 0.5207 |
| 0.1236 | 5.0 | 2675 | 0.8693 | 0.5199 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
LiamLi1991/HW01
|
LiamLi1991
| 2023-12-23T04:59:00Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-23T01:35:44Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: HW01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HW01
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7590
- Matthews Correlation: 0.5475
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5168 | 1.0 | 535 | 0.4544 | 0.4535 |
| 0.3414 | 2.0 | 1070 | 0.4683 | 0.5277 |
| 0.2331 | 3.0 | 1605 | 0.6640 | 0.5162 |
| 0.1657 | 4.0 | 2140 | 0.7590 | 0.5475 |
| 0.1236 | 5.0 | 2675 | 0.8733 | 0.5256 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
dcaustin33/llama_friends
|
dcaustin33
| 2023-12-23T04:51:14Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2023-12-23T02:37:28Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
DaRkSpyro/JewelTheMacaw
|
DaRkSpyro
| 2023-12-23T04:41:24Z | 0 | 0 |
flair
|
[
"flair",
"music",
"en",
"dataset:HuggingFaceH4/no_robots",
"license:apache-2.0",
"region:us"
] | null | 2023-12-23T04:25:09Z |
---
license: apache-2.0
language:
- en
metrics:
- accuracy
tags:
- music
datasets:
- HuggingFaceH4/no_robots
library_name: flair
---
|
Maxx0/mistral_instruct_generation
|
Maxx0
| 2023-12-23T04:21:07Z | 1 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2023-12-23T04:20:58Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
base_model: mistralai/Mistral-7B-Instruct-v0.1
model-index:
- name: mistral_instruct_generation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral_instruct_generation
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0138
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3071 | 20.0 | 20 | 0.0966 |
| 0.0239 | 40.0 | 40 | 0.0214 |
| 0.0192 | 60.0 | 60 | 0.0189 |
| 0.0179 | 80.0 | 80 | 0.0173 |
| 0.0149 | 100.0 | 100 | 0.0138 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
dthseemsbttr/gpt2-finetuned-wikitext2-copy
|
dthseemsbttr
| 2023-12-23T04:13:03Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-22T17:33:10Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_keras_callback
model-index:
- name: gpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# gpt2-finetuned-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.36.2
- TensorFlow 2.13.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
adandu/dreambooth_output
|
adandu
| 2023-12-23T04:02:14Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-12-23T02:03:01Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of AESARNAV person
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - adandu/dreambooth_output
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of AESARNAV person using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
|
Gummybear05/whisper-small-ko-E30_Y_freq_speed
|
Gummybear05
| 2023-12-23T03:59:40Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"hi",
"dataset:aihub_elder",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-23T01:53:20Z |
---
language:
- hi
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- aihub_elder
model-index:
- name: whisper-small-ko-E30_Y_freq_speed
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-ko-E30_Y_freq_speed
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the aihub Y dialogue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1876
- Cer: 5.2573
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4514 | 0.13 | 100 | 0.2782 | 6.3910 |
| 0.2636 | 0.26 | 200 | 0.2298 | 6.1913 |
| 0.2355 | 0.39 | 300 | 0.2313 | 6.5789 |
| 0.2075 | 0.52 | 400 | 0.2121 | 6.1149 |
| 0.1899 | 0.64 | 500 | 0.2107 | 5.9622 |
| 0.1746 | 0.77 | 600 | 0.2040 | 5.8212 |
| 0.1791 | 0.9 | 700 | 0.1974 | 5.6685 |
| 0.0826 | 1.03 | 800 | 0.1924 | 5.4335 |
| 0.0725 | 1.16 | 900 | 0.1959 | 5.4570 |
| 0.072 | 1.29 | 1000 | 0.1942 | 5.2749 |
| 0.0658 | 1.42 | 1100 | 0.1935 | 5.4746 |
| 0.0639 | 1.55 | 1200 | 0.1894 | 5.2867 |
| 0.0658 | 1.68 | 1300 | 0.1891 | 5.3043 |
| 0.0606 | 1.81 | 1400 | 0.1876 | 5.1985 |
| 0.0648 | 1.93 | 1500 | 0.1876 | 5.2573 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
ljvmiranda921/xx_isl_sigtyp_trf
|
ljvmiranda921
| 2023-12-23T03:51:12Z | 1 | 0 |
spacy
|
[
"spacy",
"token-classification",
"multilingual",
"model-index",
"region:us"
] |
token-classification
| 2023-11-30T06:14:10Z |
---
tags:
- spacy
- token-classification
language:
- multilingual
model-index:
- name: xx_isl_sigtyp_trf
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.8484209631
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9628502448
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9012080149
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9486362207
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.8288867214
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.7770595885
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9772685943
---
| Feature | Description |
| --- | --- |
| **Name** | `xx_isl_sigtyp_trf` |
| **Version** | `0.1.0` |
| **spaCy** | `>=3.6.1,<3.7.0` |
| **Default Pipeline** | `transformer`, `parser`, `trainable_lemmatizer`, `tagger`, `morphologizer` |
| **Components** | `transformer`, `parser`, `trainable_lemmatizer`, `tagger`, `morphologizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (7120 labels for 3 components)</summary>
| Component | Labels |
| --- | --- |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `dep`, `det`, `discourse`, `expl`, `fixed`, `flat:foreign`, `flat:name`, `iobj`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`tagger`** | `"`, `"__Case=Acc\|Gender=Neut\|Number=Sing`, `"__Case=Gen\|Number=Sing\|Person=1\|PronType=Prs`, `"__Case=Gen\|Number=Sing\|Person=2\|PronType=Prs`, `"__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `"__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `"__NumType=Card`, `"__NumType=Frac`, `"__VerbForm=Sup\|Voice=Mid`, `,`, `.`, `:`, `;`, `ADJ`, `ADJ-A`, `ADJ-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJ-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJ-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-A__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur`, `ADJ-A__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJ-A__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur`, `ADJ-A__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJ-A__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJ-A__Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur`, `ADJ-A__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing`, `ADJ-A__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `ADJ-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-A__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur`, `ADJ-A__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJ-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `ADJ-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJ-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADJ-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJ-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `ADJ-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADJ-A__Case=Acc\|Degree=Pos`, `ADJ-A__Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-A__Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-A__Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-A__Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-A__Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-A__Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-A__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Dem`, `ADJ-A__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Dem`, `ADJ-A__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Ind`, `ADJ-A__Case=Acc\|Gender=Masc\|Number=Plur\|NumType=Card`, `ADJ-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Dem`, `ADJ-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Dem`, `ADJ-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Ind`, `ADJ-A__Case=Acc\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `ADJ-A__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Dem`, `ADJ-A__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Prs`, `ADJ-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Dem`, `ADJ-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Ind`, `ADJ-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Prs`, `ADJ-A__Case=Acc\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `ADJ-A__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJ-A__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-A__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-A__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-A__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJ-A__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJ-A__Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing`, `ADJ-A__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-A__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-A__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJ-A__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJ-A__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADJ-A__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-A__Case=Dat\|Gender=Fem\|Number=Plur\|PronType=Ind`, `ADJ-A__Case=Dat\|Gender=Fem\|Number=Sing\|PronType=Dem`, `ADJ-A__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Dem`, `ADJ-A__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-A__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-A__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-A__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-A__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJ-A__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-A__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-A__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `ADJ-A__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJ-A__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADJ-A__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJ-A__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `ADJ-A__Case=Gen\|Gender=Fem\|Number=Plur\|NumType=Card`, `ADJ-A__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-A__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-A__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-A__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-A__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-A__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJ-A__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJ-A__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `ADJ-A__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing`, `ADJ-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `ADJ-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJ-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADJ-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJ-A__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADJ-A__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-A__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-A__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-A__Case=Nom\|Gender=Fem\|Number=Plur\|VerbForm=Part\|Voice=Act`, `ADJ-A__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Dem`, `ADJ-A__Case=Nom\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `ADJ-A__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Dem`, `ADJ-A__Case=Nom\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `ADJ-A__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Dem`, `ADJ-A__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `ADJ-A__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `ADJ-A__Degree=Cmp`, `ADJ-A__Degree=Sup`, `ADJ-A__Foreign=Yes`, `ADJ-A__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `ADJ-A__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADJ-A__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `ADJ-A__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADJ-A__NumType=Card`, `ADJ-A__VerbForm=Inf\|Voice=Act`, `ADJ-A__VerbForm=Sup\|Voice=Act`, `ADJ-D`, `ADJ-D__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADJ-D__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-D__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-D__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-D__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-D__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-D__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-D__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-D__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJ-D__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJ-D__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADJ-D__Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-D__Case=Acc\|Gender=Fem\|Number=Plur\|NumType=Card`, `ADJ-D__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Dem`, `ADJ-D__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Dem`, `ADJ-D__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Dem`, `ADJ-D__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Dem`, `ADJ-D__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADJ-D__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJ-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-D__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJ-D__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur`, `ADJ-D__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJ-D__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJ-D__Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing`, `ADJ-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `ADJ-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJ-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADJ-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJ-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `ADJ-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADJ-D__Case=Dat\|Degree=Pos`, `ADJ-D__Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-D__Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-D__Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-D__Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-D__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-D__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-D__Case=Dat\|Gender=Fem\|Number=Sing\|PronType=Dem`, `ADJ-D__Case=Dat\|Gender=Masc\|Number=Plur\|NumType=Card`, `ADJ-D__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Dem`, `ADJ-D__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Dem`, `ADJ-D__Case=Dat\|Gender=Neut\|Number=Plur\|NumType=Card`, `ADJ-D__Case=Dat\|Gender=Neut\|Number=Plur\|PronType=Dem`, `ADJ-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Dem`, `ADJ-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Ind`, `ADJ-D__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADJ-D__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-D__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-D__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-D__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJ-D__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJ-D__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `ADJ-D__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADJ-D__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADJ-D__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJ-D__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-D__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-D__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-D__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-D__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-D__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-D__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-D__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJ-D__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJ-D__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `ADJ-D__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADJ-D__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-D__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-D__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-D__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Dem`, `ADJ-D__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `ADJ-D__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADJ-D__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `ADJ-D__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `ADJ-D__Mood=Sub\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `ADJ-D__NumType=Card`, `ADJ-D__VerbForm=Inf\|Voice=Act`, `ADJ-G`, `ADJ-G__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJ-G__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-G__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-G__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-G__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-G__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-G__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-G__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-G__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-G__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJ-G__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADJ-G__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJ-G__Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-G__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Dem`, `ADJ-G__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Dem`, `ADJ-G__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJ-G__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJ-G__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-G__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-G__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-G__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-G__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-G__Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur`, `ADJ-G__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJ-G__Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-G__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-G__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-G__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Dem`, `ADJ-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-G__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJ-G__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJ-G__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJ-G__Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing`, `ADJ-G__Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur`, `ADJ-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-G__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `ADJ-G__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJ-G__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADJ-G__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJ-G__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `ADJ-G__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADJ-G__Case=Gen\|Degree=Pos`, `ADJ-G__Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-G__Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-G__Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-G__Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-G__Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-G__Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-G__Case=Gen\|Gender=Fem\|Number=Sing\|PronType=Ind`, `ADJ-G__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Dem`, `ADJ-G__Case=Gen\|Gender=Masc\|Number=Sing\|NumType=Card`, `ADJ-G__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Dem`, `ADJ-G__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Ind`, `ADJ-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Dem`, `ADJ-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Ind`, `ADJ-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Prs`, `ADJ-G__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJ-G__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJ-G__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-G__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-G__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-G__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-G__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-G__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-G__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJ-G__Degree=Cmp`, `ADJ-G__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `ADJ-G__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADJ-G__NumType=Card`, `ADJ-G__VerbForm=Inf\|Voice=Act`, `ADJ-N`, `ADJ-N__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJ-N__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-N__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-N__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-N__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-N__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJ-N__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing`, `ADJ-N__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `ADJ-N__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJ-N__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADJ-N__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJ-N__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `ADJ-N__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-N__Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-N__Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Acc\|Gender=Fem\|Number=Plur\|NumType=Card`, `ADJ-N__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Prs`, `ADJ-N__Case=Acc\|Gender=Fem\|Number=Plur\|VerbForm=Part\|Voice=Act`, `ADJ-N__Case=Acc\|Gender=Masc\|Number=Sing\|NumType=Card`, `ADJ-N__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Ind`, `ADJ-N__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Dem`, `ADJ-N__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Prs`, `ADJ-N__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-N__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-N__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-N__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-N__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJ-N__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJ-N__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-N__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Dat\|Gender=Fem\|Number=Plur\|PronType=Ind`, `ADJ-N__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Dem`, `ADJ-N__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Prs`, `ADJ-N__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-N__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-N__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-N__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-N__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-N__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-N__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJ-N__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJ-N__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `ADJ-N__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Dem`, `ADJ-N__Case=Gen\|Gender=Neut\|Number=Plur\|NumType=Card`, `ADJ-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `ADJ-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur`, `ADJ-N__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur`, `ADJ-N__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur`, `ADJ-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur`, `ADJ-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `ADJ-N__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADJ-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `ADJ-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Nom\|Definite=Ind\|Number=Sing`, `ADJ-N__Case=Nom\|Degree=Pos`, `ADJ-N__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ-N__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ-N__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ-N__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ-N__Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJ-N__Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ-N__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Dem`, `ADJ-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Dem`, `ADJ-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Ind`, `ADJ-N__Case=Nom\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `ADJ-N__Case=Nom\|Gender=Masc\|Number=Plur\|NumType=Card`, `ADJ-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Dem`, `ADJ-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Ind`, `ADJ-N__Case=Nom\|Gender=Masc\|Number=Plur\|VerbForm=Part\|Voice=Act`, `ADJ-N__Case=Nom\|Gender=Masc\|Number=Sing\|NumType=Card`, `ADJ-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Dem`, `ADJ-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `ADJ-N__Case=Nom\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `ADJ-N__Case=Nom\|Gender=Neut\|Number=Plur\|NumType=Card`, `ADJ-N__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Dem`, `ADJ-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Dem`, `ADJ-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `ADJ-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Prs`, `ADJ-N__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `ADJ-N__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Mid`, `ADJ-N__Degree=Cmp`, `ADJ-N__Degree=Sup`, `ADJ-N__Foreign=Yes`, `ADJ-N__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADJ-N__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `ADJ-N__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADJ-N__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `ADJ-N__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADJ-N__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADJ-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `ADJ-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `ADJ-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADJ-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `ADJ-N__Mood=Sub\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADJ-N__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `ADJ-N__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADJ-N__NumType=Card`, `ADJ-N__NumType=Frac`, `ADJ-N__VerbForm=Inf\|Voice=Act`, `ADJ-N__VerbForm=Inf\|Voice=Mid`, `ADJ-N__VerbForm=Part\|Voice=Act`, `ADJ-N__VerbForm=Sup\|Voice=Act`, `ADJ-N__VerbForm=Sup\|Voice=Mid`, `ADJP-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADJP__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJR`, `ADJR-A`, `ADJR-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur`, `ADJR-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADJR-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `ADJR-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJR-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `ADJR-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJR-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJR-A__Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJR-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJR-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJR-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJR-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJR-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJR-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJR-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJR-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADJR-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJR-A__Case=Acc\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `ADJR-A__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADJR-A__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJR-A__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJR-A__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJR-A__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJR-A__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJR-A__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur`, `ADJR-A__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJR-A__Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing`, `ADJR-A__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJR-A__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADJR-A__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur`, `ADJR-A__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADJR-A__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `ADJR-A__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJR-A__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJR-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJR-A__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJR-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `ADJR-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJR-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADJR-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJR-A__Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJR-A__Degree=Cmp`, `ADJR-A__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADJR-A__VerbForm=Inf\|Voice=Act`, `ADJR-D`, `ADJR-D__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADJR-D__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJR-D__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJR-D__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJR-D__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADJR-D__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `ADJR-D__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJR-D__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `ADJR-D__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJR-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJR-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJR-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJR-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJR-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADJR-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJR-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `ADJR-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADJR-D__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADJR-D__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `ADJR-D__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADJR-D__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur`, `ADJR-D__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADJR-D__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `ADJR-D__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJR-D__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJR-D__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJR-D__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJR-D__Degree=Cmp`, `ADJR-G`, `ADJR-G__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `ADJR-G__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJR-G__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJR-G__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJR-G__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJR-G__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur`, `ADJR-G__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADJR-G__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `ADJR-G__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJR-G__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `ADJR-G__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJR-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJR-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJR-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJR-G__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADJR-G__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJR-G__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJR-G__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJR-G__Degree=Cmp`, `ADJR-N`, `ADJR-N__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur`, `ADJR-N__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADJR-N__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `ADJR-N__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJR-N__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `ADJR-N__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJR-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJR-N__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADJR-N__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJR-N__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJR-N__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `ADJR-N__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJR-N__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJR-N__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJR-N__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJR-N__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADJR-N__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJR-N__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADJR-N__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `ADJR-N__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `ADJR-N__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJR-N__Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur`, `ADJR-N__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJR-N__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJR-N__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJR-N__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADJR-N__Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJR-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur`, `ADJR-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADJR-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `ADJR-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJR-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `ADJR-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJR-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJR-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJR-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJR-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJR-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJR-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJR-N__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJR-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADJR-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJR-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `ADJR-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADJR-N__Case=Nom\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `ADJR-N__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `ADJR-N__Degree=Cmp`, `ADJR-N__Foreign=Yes`, `ADJR-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADJR-N__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADJR-N__VerbForm=Inf\|Voice=Act`, `ADJR__Degree=Cmp`, `ADJS`, `ADJS-A`, `ADJS-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJS-A__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur`, `ADJS-A__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJS-A__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur`, `ADJS-A__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJS-A__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur`, `ADJS-A__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJS-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJS-A__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur`, `ADJS-A__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJS-A__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJS-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `ADJS-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJS-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJS-A__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJS-A__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJS-A__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJS-A__Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJS-A__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur`, `ADJS-A__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJS-A__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur`, `ADJS-A__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJS-A__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `ADJS-A__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJS-A__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur`, `ADJS-A__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJS-A__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur`, `ADJS-A__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur`, `ADJS-A__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJS-A__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `ADJS-A__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur`, `ADJS-A__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJS-A__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJS-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJS-A__Degree=Sup`, `ADJS-A__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADJS-A__VerbForm=Inf\|Voice=Act`, `ADJS-D`, `ADJS-D__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur`, `ADJS-D__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJS-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJS-D__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur`, `ADJS-D__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJS-D__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur`, `ADJS-D__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJS-D__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur`, `ADJS-D__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJS-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJS-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJS-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADJS-D__Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur`, `ADJS-D__Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur`, `ADJS-D__Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJS-D__Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJS-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `ADJS-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJS-D__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `ADJS-D__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJS-D__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJS-D__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJS-D__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJS-G__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJS-G__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur`, `ADJS-G__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJS-G__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur`, `ADJS-G__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJS-G__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur`, `ADJS-G__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJS-G__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur`, `ADJS-G__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJS-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJS-G__Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJS-G__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `ADJS-G__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJS-G__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJS-G__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJS-G__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJS-N`, `ADJS-N__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJS-N__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJS-N__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJS-N__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur`, `ADJS-N__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJS-N__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJS-N__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADJS-N__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJS-N__Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJS-N__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJS-N__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADJS-N__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJS-N__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `ADJS-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJS-N__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur`, `ADJS-N__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJS-N__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur`, `ADJS-N__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJS-N__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur`, `ADJS-N__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJS-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJS-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJS-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJS-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur`, `ADJS-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADJS-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur`, `ADJS-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADJS-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur`, `ADJS-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJS-N__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `ADJS-N__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADJS-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADJS-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADJS-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADJS-N__Case=Nom\|Definite=Ind\|Number=Sing`, `ADJS-N__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Mid`, `ADJS-N__Degree=Cmp`, `ADJS-N__Degree=Sup`, `ADJS-N__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `ADJS-N__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `ADJS-N__Mood=Ind\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `ADJS-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `ADJS-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `ADJS-N__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `ADJS-N__VerbForm=Inf\|Voice=Act`, `ADJS-N__VerbForm=Inf\|Voice=Mid`, `ADJS__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADJS__Degree=Sup`, `ADJ__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADJ__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `ADJ__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADJ__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADJ__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADJ__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADJ__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADJ__Degree=Cmp`, `ADJ__Degree=Pos`, `ADV`, `ADV-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADVP`, `ADVR`, `ADVR-1`, `ADVR__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADVR__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `ADVR__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADVR__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `ADVR__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADVR__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADVR__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADVR__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADVR__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `ADVR__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADVR__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADVR__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADVR__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADVR__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADVR__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADVR__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `ADVR__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADVR__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADVR__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `ADVR__Case=Gen\|Gender=Masc\|Number=Sing\|NumType=Card`, `ADVR__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADVR__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `ADVR__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADVR__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `ADVR__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADVR__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADVR__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADVR__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADVR__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADVR__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADVR__Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADVR__Case=Nom\|Gender=Fem\|Number=Plur\|VerbForm=Part\|Voice=Act`, `ADVR__Case=Nom\|Gender=Masc\|Number=Plur\|VerbForm=Part\|Voice=Act`, `ADVR__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Int`, `ADVR__Degree=Cmp`, `ADVR__Degree=Sup`, `ADVR__Foreign=Yes`, `ADVR__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADVR__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADVR__VerbForm=Inf\|Voice=Mid`, `ADVR__VerbForm=Sup\|Voice=Act`, `ADVS`, `ADVS__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADVS__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADVS__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur`, `ADVS__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADVS__Case=Acc\|Definite=Ind\|Number=Sing`, `ADVS__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADVS__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADVS__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADVS__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADVS__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADVS__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADVS__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADVS__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADVS__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADVS__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADVS__Case=Nom\|Definite=Ind\|Number=Sing`, `ADVS__Degree=Cmp`, `ADVS__Degree=Sup`, `ADVS__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `ADVS__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `ADVS__VerbForm=Inf\|Voice=Mid`, `ADVS__VerbForm=Sup\|Voice=Mid`, `ADV__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ADV__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADV__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADV__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADV__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADV__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADV__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADV__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADV__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADV__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADV__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADV__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADV__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `ADV__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADV__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADV__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADV__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `ADV__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADV__Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADV__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Dem`, `ADV__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Dem`, `ADV__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Prs`, `ADV__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Ind`, `ADV__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Prs`, `ADV__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Ind`, `ADV__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Dem`, `ADV__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Ind`, `ADV__Case=Acc\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `ADV__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADV__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADV__Case=Dat\|Definite=Def\|Gender=Neut\|Number=Sing`, `ADV__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADV__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADV__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADV__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADV__Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `ADV__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `ADV__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADV__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADV__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADV__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADV__Case=Dat\|Definite=Ind\|Number=Sing`, `ADV__Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADV__Case=Dat\|Gender=Fem\|Number=Plur\|PronType=Prs`, `ADV__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Prs`, `ADV__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Prs`, `ADV__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur`, `ADV__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADV__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur`, `ADV__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADV__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADV__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `ADV__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADV__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADV__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADV__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `ADV__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADV__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Ind`, `ADV__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Prs`, `ADV__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur`, `ADV__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `ADV__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `ADV__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADV__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADV__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADV__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADV__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADV__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `ADV__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing`, `ADV__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur`, `ADV__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADV__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ADV__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADV__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ADV__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ADV__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing`, `ADV__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `ADV__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ADV__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `ADV__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ADV__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `ADV__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ADV__Case=Nom\|Definite=Ind\|Number=Sing`, `ADV__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ADV__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ADV__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ADV__Case=Nom\|Gender=Fem\|Number=Plur\|VerbForm=Part\|Voice=Act`, `ADV__Case=Nom\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `ADV__Case=Nom\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `ADV__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `ADV__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Prs`, `ADV__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `ADV__Degree=Cmp`, `ADV__Degree=Pos`, `ADV__Degree=Sup`, `ADV__Foreign=Yes`, `ADV__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADV__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `ADV__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `ADV__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADV__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `ADV__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADV__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ADV__VerbForm=Inf\|Voice=Act`, `ADV__VerbForm=Sup\|Voice=Act`, `ALSO`, `ALSO__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur`, `ALSO__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ALSO__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ALSO__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ALSO__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `ALSO__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur`, `ALSO__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ALSO__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ALSO__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ALSO__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ALSO__Case=Nom\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `ALSO__Foreign=Yes`, `ALSO__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `ALSO__VerbForm=Sup\|Voice=Act`, `BAG__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur`, `BAG__VerbForm=Part\|Voice=Act`, `BE`, `BEDI`, `BEDI__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `BEDI__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `BEDI__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `BEDI__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `BEDI__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `BEDI__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `BEDI__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `BEDI__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing`, `BEDI__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Prs`, `BEDI__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `BEDI__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `BEDI__Case=Gen\|Gender=Fem\|Number=Sing\|PronType=Dem`, `BEDI__Case=Gen\|Gender=Fem\|Number=Sing\|PronType=Ind`, `BEDI__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur`, `BEDI__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `BEDI__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `BEDI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `BEDI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `BEDI__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur`, `BEDI__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `BEDI__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `BEDI__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Dem`, `BEDI__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Dem`, `BEDI__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Prs`, `BEDI__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `BEDI__Mood=Ind\|Number=Plur\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `BEDI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `BEDI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEDI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `BEDI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `BEDI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `BEDI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEDI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `BEDI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `BEDI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEDI__Mood=Sub\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `BEDI__VerbForm=Inf\|Voice=Act`, `BEDS__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `BEDS__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `BEDS__Case=Nom\|Gender=Masc\|Number=Plur\|VerbForm=Part\|Voice=Act`, `BEDS__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `BEDS__Mood=Sub\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `BEDS__Mood=Sub\|Number=Plur\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `BEDS__Mood=Sub\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `BEDS__Mood=Sub\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `BEDS__Mood=Sub\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `BEDS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `BEI__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `BEI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `BEI__Degree=Cmp`, `BEI__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEI__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEI__Mood=Imp\|VerbForm=Inf`, `BEI__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEI__Mood=Sub\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEI__VerbForm=Sup\|Voice=Act`, `BEN`, `BEN__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `BEN__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `BEN__VerbForm=Sup\|Voice=Act`, `BEPI`, `BEPI__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `BEPI__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `BEPI__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `BEPI__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `BEPI__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `BEPI__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `BEPI__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPI__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPI__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `BEPI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `BEPI__Mood=Ind\|Tense=Pres`, `BEPI__Mood=Sub\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPS`, `BEPS__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `BEPS__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Prs`, `BEPS__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Prs`, `BEPS__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `BEPS__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `BEPS__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPS__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPS__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPS__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPS__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPS__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `BEPS__Mood=Sub\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPS__Mood=Sub\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPS__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPS__Mood=Sub\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPS__Mood=Sub\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `BEPS__VerbForm=Sup\|Voice=Act`, `BE__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `BE__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `BE__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `BE__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `BE__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `BE__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `BE__VerbForm=Inf\|Voice=Act`, `BE__VerbForm=Sup\|Voice=Act`, `C`, `CONJ`, `CONJ-1`, `CONJ-1__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `CONJ-1__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `CONJ-1__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Ind`, `CONJ-1__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `CONJ-1__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `CONJ-1__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `CONJ-1__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `CONJ-1__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Ind`, `CONJ-2`, `CONJ-2__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `CONJ-2__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Ind`, `CONJ-3`, `CONJ-3__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `CONJ-3__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Ind`, `CONJ-4`, `CONJ-4__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `CONJ-4__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `CONJ-5__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Ind`, `CONJ-6__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `CONJ-7`, `CONJ-8`, `CONJ-9`, `CONJ__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `CONJ__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `CONJ__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `CONJ__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Ind`, `CONJ__Case=Acc\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `CONJ__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `CONJ__Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing`, `CONJ__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `CONJ__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `CONJ__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `CONJ__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `CONJ__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `CONJ__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `CONJ__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `CONJ__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Ind`, `CONJ__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `CONJ__Foreign=Yes`, `CONJ__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `CONJ__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `C__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Dem`, `C__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Prs`, `C__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing`, `C__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `C__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `C__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Prs`, `C__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `C__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `D-A`, `D-A__Case=Acc`, `D-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `D-A__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur`, `D-A__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing`, `D-A__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `D-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `D-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `D-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `D-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `D-A__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `D-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `D-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `D-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `D-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `D-A__Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing`, `D-A__Case=Acc\|Gender=Fem\|Number=Plur`, `D-A__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Dem`, `D-A__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Prs`, `D-A__Case=Acc\|Gender=Fem\|Number=Sing`, `D-A__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Dem`, `D-A__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Ind`, `D-A__Case=Acc\|Gender=Masc\|Number=Plur`, `D-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Dem`, `D-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Ind`, `D-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Prs`, `D-A__Case=Acc\|Gender=Masc\|Number=Sing`, `D-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Dem`, `D-A__Case=Acc\|Gender=Neut\|Number=Plur`, `D-A__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Dem`, `D-A__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Prs`, `D-A__Case=Acc\|Gender=Neut\|Number=Sing`, `D-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Dem`, `D-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Prs`, `D-A__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `D-A__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `D-A__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `D-A__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Ind`, `D-A__Case=Dat\|Gender=Neut\|Number=Sing`, `D-A__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `D-A__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `D-A__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `D-A__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `D-A__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `D-A__Case=Gen\|Gender=Masc\|Number=Plur`, `D-A__Case=Gen\|Gender=Masc\|Number=Plur\|NumType=Card`, `D-A__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Dem`, `D-A__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Dem`, `D-A__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Dem`, `D-A__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `D-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `D-A__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `D-A__Case=Nom\|Gender=Fem\|Number=Plur`, `D-A__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Dem`, `D-A__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Prs`, `D-A__Case=Nom\|Gender=Fem\|Number=Sing`, `D-A__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Dem`, `D-A__Case=Nom\|Gender=Masc\|Number=Sing`, `D-A__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Dem`, `D-A__Case=Nom\|Gender=Neut\|Number=Plur`, `D-A__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Dem`, `D-A__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Prs`, `D-A__Case=Nom\|Gender=Neut\|Number=Sing`, `D-A__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Dem`, `D-A__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Int`, `D-A__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Prs`, `D-A__Degree=Sup`, `D-A__Foreign=Yes`, `D-A__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `D-A__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `D-A__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `D-A__VerbForm=Inf\|Voice=Act`, `D-D`, `D-D__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Prs`, `D-D__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Dem`, `D-D__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Dem`, `D-D__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Prs`, `D-D__Case=Dat`, `D-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `D-D__Case=Dat\|Definite=Def\|Gender=Neut\|Number=Sing`, `D-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `D-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `D-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `D-D__Case=Dat\|Gender=Fem\|Number=Plur`, `D-D__Case=Dat\|Gender=Fem\|Number=Plur\|PronType=Dem`, `D-D__Case=Dat\|Gender=Fem\|Number=Plur\|PronType=Prs`, `D-D__Case=Dat\|Gender=Fem\|Number=Sing`, `D-D__Case=Dat\|Gender=Fem\|Number=Sing\|PronType=Dem`, `D-D__Case=Dat\|Gender=Masc\|Number=Plur`, `D-D__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Dem`, `D-D__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Prs`, `D-D__Case=Dat\|Gender=Masc\|Number=Sing`, `D-D__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Dem`, `D-D__Case=Dat\|Gender=Neut\|Number=Plur`, `D-D__Case=Dat\|Gender=Neut\|Number=Plur\|PronType=Dem`, `D-D__Case=Dat\|Gender=Neut\|Number=Plur\|PronType=Prs`, `D-D__Case=Dat\|Gender=Neut\|Number=Sing`, `D-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Dem`, `D-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Prs`, `D-D__Case=Dat\|Number=Sing\|Person=2\|PronType=Prs`, `D-D__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `D-D__Case=Gen\|Number=Plur\|Person=1\|PronType=Prs`, `D-D__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Dem`, `D-D__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Dem`, `D-D__Foreign=Yes`, `D-G`, `D-G__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Dem`, `D-G__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Dem`, `D-G__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Dem`, `D-G__Case=Gen`, `D-G__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `D-G__Case=Gen\|Gender=Fem\|Number=Plur`, `D-G__Case=Gen\|Gender=Fem\|Number=Plur\|PronType=Dem`, `D-G__Case=Gen\|Gender=Fem\|Number=Plur\|PronType=Prs`, `D-G__Case=Gen\|Gender=Fem\|Number=Sing`, `D-G__Case=Gen\|Gender=Fem\|Number=Sing\|PronType=Dem`, `D-G__Case=Gen\|Gender=Masc\|Number=Plur`, `D-G__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Dem`, `D-G__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Prs`, `D-G__Case=Gen\|Gender=Masc\|Number=Sing`, `D-G__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Dem`, `D-G__Case=Gen\|Gender=Neut\|Number=Plur`, `D-G__Case=Gen\|Gender=Neut\|Number=Plur\|PronType=Dem`, `D-G__Case=Gen\|Gender=Neut\|Number=Plur\|PronType=Prs`, `D-G__Case=Gen\|Gender=Neut\|Number=Sing`, `D-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Dem`, `D-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Prs`, `D-G__Case=Nom\|Gender=Fem\|Number=Plur`, `D-G__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Dem`, `D-G__Degree=Cmp`, `D-G__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `D-G__VerbForm=Inf\|Voice=Act`, `D-N`, `D-N__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `D-N__Case=Acc\|Gender=Fem\|Number=Plur`, `D-N__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Dem`, `D-N__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Prs`, `D-N__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Dem`, `D-N__Case=Acc\|Gender=Masc\|Number=Sing`, `D-N__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Dem`, `D-N__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Dem`, `D-N__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Prs`, `D-N__Case=Acc\|Gender=Neut\|Number=Sing`, `D-N__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Dem`, `D-N__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Prs`, `D-N__Case=Dat\|Gender=Neut\|Number=Sing`, `D-N__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `D-N__Case=Gen\|Gender=Fem\|Number=Plur\|PronType=Dem`, `D-N__Case=Nom`, `D-N__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `D-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `D-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `D-N__Case=Nom\|Gender=Fem\|Number=Plur`, `D-N__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Dem`, `D-N__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Prs`, `D-N__Case=Nom\|Gender=Fem\|Number=Sing`, `D-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Dem`, `D-N__Case=Nom\|Gender=Masc\|Number=Plur`, `D-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Dem`, `D-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Prs`, `D-N__Case=Nom\|Gender=Masc\|Number=Sing`, `D-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Dem`, `D-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `D-N__Case=Nom\|Gender=Neut\|Number=Plur`, `D-N__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Dem`, `D-N__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Prs`, `D-N__Case=Nom\|Gender=Neut\|Number=Sing`, `D-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Dem`, `D-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Int`, `D-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Prs`, `D-N__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `D-N__Foreign=Yes`, `D-N__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `D-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `D-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `D-N__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `D-N__VerbForm=Inf\|Voice=Act`, `DAG__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `DAG__VerbForm=Part\|Voice=Act`, `DAN`, `DAN-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `DAN-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `DAN-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `DAN-A__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `DAN-A__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `DAN-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `DAN-A__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `DAN-A__VerbForm=Sup\|Voice=Act`, `DAN-D`, `DAN-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `DAN-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `DAN-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `DAN-D__Foreign=Yes`, `DAN__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `DAN__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `DAN__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `DAN__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `DAN__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `DAN__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `DAN__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `DAN__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `DAN__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `DAN__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `DAN__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `DAN__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `DAN__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `DAN__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `DAN__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `DAN__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `DAN__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `DAN__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `DAN__Case=Nom\|Gender=Fem\|Number=Plur\|VerbForm=Part\|Voice=Act`, `DAN__Case=Nom\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `DAN__Case=Nom\|Gender=Masc\|Number=Plur\|VerbForm=Part\|Voice=Act`, `DAN__Case=Nom\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `DAN__Case=Nom\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `DAN__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `DAN__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `DAN__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DAN__Tense=Past\|VerbForm=Part`, `DAN__VerbForm=Sup\|Voice=Act`, `DODI__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `DODI__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `DODI__Case=Acc\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `DODI__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `DODI__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `DODI__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `DODI__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `DODI__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `DODI__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DODI__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `DODI__Mood=Ind\|Number=Plur\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `DODI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `DODI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `DODI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `DODI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `DODI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DODI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `DODI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `DODI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DODI__Mood=Sub\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `DODI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `DODI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `DODS__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Prs`, `DODS__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `DODS__Mood=Ind\|Number=Plur\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `DODS__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `DODS__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `DODS__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DODS__Mood=Sub\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `DODS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `DOG__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `DOI`, `DOI__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `DOI__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `DOI__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `DOI__Case=Nom\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `DOI__Case=Nom\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `DOI__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOI__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOI__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOI__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `DOI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOI__VerbForm=Inf\|Voice=Act`, `DOI__VerbForm=Sup\|Voice=Act`, `DON__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `DON__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `DON__Case=Acc\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `DON__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `DON__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `DON__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `DON__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `DON__Case=Nom\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `DON__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `DON__VerbForm=Sup\|Voice=Act`, `DON__VerbForm=Sup\|Voice=Mid`, `DOPI__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `DOPI__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `DOPI__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `DOPI__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `DOPI__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `DOPI__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `DOPI__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `DOPI__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `DOPI__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `DOPI__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `DOPI__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `DOPI__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPI__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPI__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `DOPI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `DOPI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `DOPI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `DOPI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `DOPI__Mood=Sub\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPI__Mood=Sub\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPI__Mood=Sub\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `DOPI__VerbForm=Inf\|Voice=Act`, `DOPI__VerbForm=Inf\|Voice=Mid`, `DOPS__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `DOPS__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPS__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPS__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPS__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPS__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPS__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `DOPS__Mood=Sub\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPS__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPS__Mood=Sub\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPS__Mood=Sub\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DOPS__VerbForm=Inf\|Voice=Act`, `DO__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DO__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `DO__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `DO__VerbForm=Inf\|Voice=Act`, `DO__VerbForm=Inf\|Voice=Mid`, `DO__VerbForm=Sup\|Voice=Mid`, `ES__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Dem`, `ES__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Prs`, `ES__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Dem`, `ES__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Prs`, `FOREIGN__Foreign=Yes`, `FP`, `FP-1`, `FP-A`, `FP-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `FP-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `FP-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `FP-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `FP-A__Case=Acc\|Gender=Fem\|Number=Sing\|NumType=Card`, `FP-A__Case=Acc\|Gender=Neut\|Number=Sing\|NumType=Card`, `FP-A__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `FP-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `FP-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `FP-A__Case=Nom\|Gender=Masc\|Number=Sing\|NumType=Card`, `FP-D`, `FP-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `FP-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `FP-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `FP-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `FP-D__Case=Dat\|Gender=Masc\|Number=Sing\|NumType=Card`, `FP-D__Case=Dat\|Gender=Neut\|Number=Sing\|NumType=Card`, `FP-D__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `FP-G__Case=Gen\|Gender=Masc\|Number=Sing\|NumType=Card`, `FP-N`, `FP-N-6__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `FP-N__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Ind`, `FP-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `FP-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `FP-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `FP-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `FP-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `FP-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `FP-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `FP-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `FP-N__Case=Nom\|Gender=Fem\|Number=Plur\|NumType=Card`, `FP-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Ind`, `FP-N__Case=Nom\|Gender=Masc\|Number=Sing\|NumType=Card`, `FP-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `FP__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `FP__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `FP__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `FP__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `FP__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `FP__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `FP__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing`, `FP__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `FP__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `FW`, `FW-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `FW__Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing`, `FW__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `FW__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `FW__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `FW__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `FW__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `FW__Case=Acc\|Definite=Ind\|Number=Sing`, `FW__Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur`, `FW__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `FW__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `FW__Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing`, `FW__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `FW__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `FW__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `FW__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `FW__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `FW__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `FW__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `FW__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `FW__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `FW__Case=Dat\|Definite=Ind\|Number=Sing`, `FW__Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur`, `FW__Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur`, `FW__Case=Dat\|Gender=Fem\|Number=Plur\|PronType=Ind`, `FW__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Ind`, `FW__Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing`, `FW__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `FW__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `FW__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `FW__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `FW__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `FW__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `FW__Case=Gen\|Definite=Ind\|Number=Sing`, `FW__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Prs`, `FW__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `FW__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `FW__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `FW__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `FW__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `FW__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `FW__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `FW__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `FW__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `FW__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `FW__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `FW__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `FW__Case=Nom\|Definite=Ind\|Number=Sing`, `FW__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `FW__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur`, `FW__Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing`, `FW__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `FW__Foreign=Yes`, `FW__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `FW__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `FW__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `FW__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `FW__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `FW__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `FW__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `FW__NumType=Card`, `FW__VerbForm=Inf\|Voice=Act`, `G__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `HAG__VerbForm=Part\|Voice=Act`, `HAN`, `HAN__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `HAN__Case=Nom\|Gender=Fem\|Number=Plur\|VerbForm=Part\|Voice=Act`, `HAN__Case=Nom\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `HAN__Case=Nom\|Gender=Masc\|Number=Plur\|VerbForm=Part\|Voice=Act`, `HAN__Case=Nom\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `HAN__Case=Nom\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `HAN__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `HAN__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `HAN__VerbForm=Sup\|Voice=Act`, `HV`, `HVDI__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `HVDI__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `HVDI__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `HVDI__Case=Nom\|Gender=Masc\|Number=Plur\|VerbForm=Part\|Voice=Act`, `HVDI__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `HVDI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `HVDI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `HVDI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `HVDI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVDI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `HVDI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `HVDI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `HVDI__Mood=Sub\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `HVDI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `HVDI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVDI__VerbForm=Inf\|Voice=Act`, `HVDS__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `HVDS__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `HVDS__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVDS__Mood=Sub\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `HVDS__Mood=Sub\|Number=Plur\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `HVDS__Mood=Sub\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `HVDS__Mood=Sub\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `HVDS__Mood=Sub\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `HVDS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `HVDS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVDS__VerbForm=Inf\|Voice=Act`, `HVI`, `HVI__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `HVI__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `HVI__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVI__VerbForm=Sup\|Voice=Act`, `HVN__Case=Acc\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `HVN__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `HVN__VerbForm=Sup\|Voice=Act`, `HVPI`, `HVPI__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `HVPI__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `HVPI__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `HVPI__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `HVPI__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `HVPI__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `HVPI__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `HVPI__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `HVPI__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `HVPI__Case=Nom\|Definite=Ind\|Number=Sing`, `HVPI__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `HVPI__Foreign=Yes`, `HVPI__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPI__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPI__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `HVPI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPI__Mood=Sub\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPI__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPI__Mood=Sub\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `HVPI__Mood=Sub\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPI__Mood=Sub\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPI__VerbForm=Inf\|Voice=Act`, `HVPI__VerbForm=Sup\|Voice=Act`, `HVPS`, `HVPS__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `HVPS__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `HVPS__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPS__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPS__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPS__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPS__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPS__Mood=Sub\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPS__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPS__Mood=Sub\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPS__Mood=Sub\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HVPS__VerbForm=Inf\|Voice=Act`, `HV__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `HV__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `HV__VerbForm=Inf\|Voice=Act`, `HV__VerbForm=Inf\|Voice=Mid`, `INTJ`, `INTJ__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `INTJ__Case=Nom\|Definite=Ind\|Number=Sing`, `INTJ__Foreign=Yes`, `INTJ__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `INTJ__VerbForm=Sup\|Voice=Act`, `IP-INF__VerbForm=Inf\|Voice=Act`, `LB`, `M-D`, `MAG`, `MAG__VerbForm=Part\|Voice=Act`, `MD`, `MDDI`, `MDDI__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `MDDI__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `MDDI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `MDDI__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `MDDI__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `MDDI__Case=Nom\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `MDDI__Case=Nom\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `MDDI__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDDI__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDI__Mood=Ind\|Number=Plur\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDDI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDDI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDDI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `MDDI__Mood=Ind\|Tense=Past`, `MDDI__Mood=Sub\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDI__Mood=Sub\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDI__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDDI__Mood=Sub\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDI__VerbForm=Inf\|Voice=Act`, `MDDI__VerbForm=Sup\|Voice=Act`, `MDDS`, `MDDS__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `MDDS__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `MDDS__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `MDDS__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `MDDS__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur`, `MDDS__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `MDDS__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `MDDS__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `MDDS__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `MDDS__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `MDDS__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `MDDS__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `MDDS__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `MDDS__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `MDDS__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `MDDS__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `MDDS__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `MDDS__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `MDDS__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `MDDS__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `MDDS__Case=Nom\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `MDDS__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `MDDS__Foreign=Yes`, `MDDS__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDDS__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDS__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDDS__Mood=Ind\|Number=Plur\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDS__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDS__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDDS__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDS__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDDS__Mood=Ind\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDS__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDDS__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDS__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `MDDS__Mood=Sub\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDS__Mood=Sub\|Number=Plur\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDS__Mood=Sub\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDS__Mood=Sub\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDS__Mood=Sub\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDS__Mood=Sub\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDDS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDDS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDDS__Mood=Sub\|Tense=Past`, `MDDS__VerbForm=Inf\|Voice=Act`, `MDDS__VerbForm=Sup\|Voice=Act`, `MDI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDN__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `MDN__VerbForm=Sup\|Voice=Act`, `MDPI`, `MDPI__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `MDPI__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `MDPI__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `MDPI__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `MDPI__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `MDPI__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `MDPI__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `MDPI__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `MDPI__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `MDPI__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `MDPI__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `MDPI__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `MDPI__Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing`, `MDPI__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `MDPI__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `MDPI__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `MDPI__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `MDPI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `MDPI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `MDPI__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `MDPI__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `MDPI__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `MDPI__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `MDPI__Case=Nom\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `MDPI__Case=Nom\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `MDPI__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `MDPI__Foreign=Yes`, `MDPI__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPI__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPI__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPI__Mood=Ind\|Number=Plur\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDPI__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDPI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `MDPI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDPI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `MDPI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `MDPI__Mood=Ind\|Tense=Pres`, `MDPI__Mood=Sub\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPI__Mood=Sub\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPI__Mood=Sub\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDPI__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPI__Mood=Sub\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDPI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPI__VerbForm=Inf\|Voice=Act`, `MDPI__VerbForm=Sup\|Voice=Act`, `MDPS`, `MDPS__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `MDPS__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `MDPS__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `MDPS__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `MDPS__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `MDPS__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `MDPS__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `MDPS__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `MDPS__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `MDPS__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `MDPS__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `MDPS__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `MDPS__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `MDPS__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `MDPS__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `MDPS__Foreign=Yes`, `MDPS__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPS__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPS__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPS__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPS__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPS__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPS__Mood=Sub\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDPS__Mood=Sub\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPS__Mood=Sub\|Number=Plur\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDPS__Mood=Sub\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPS__Mood=Sub\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDPS__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPS__Mood=Sub\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPS__Mood=Sub\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDPS__Mood=Sub\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MDPS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MDPS__VerbForm=Inf\|Voice=Act`, `MD__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `MD__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `MD__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `MD__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `MD__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `MD__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `MD__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `MD__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `MD__Foreign=Yes`, `MD__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MD__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MD__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MD__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MD__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MD__Mood=Sub\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MD__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `MD__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `MD__VerbForm=Inf\|Voice=Act`, `MD__VerbForm=Part\|Voice=Act`, `MS-N__Degree=Sup`, `N`, `N-A`, `N-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `N-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `N-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `N-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `N-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-A__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur`, `N-A__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur`, `N-A__Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing`, `N-A__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur`, `N-A__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing`, `N-A__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur`, `N-A__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `N-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `N-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `N-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `N-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `N-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `N-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `N-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `N-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `N-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `N-A__Case=Acc\|Definite=Ind\|Number=Sing`, `N-A__Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-A__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Ind`, `N-A__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Dem`, `N-A__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Prs`, `N-A__Case=Acc\|Gender=Masc\|Number=Plur\|NumType=Card`, `N-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Prs`, `N-A__Case=Acc\|Gender=Neut\|Number=Plur\|NumType=Card`, `N-A__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Prs`, `N-A__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-A__Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing`, `N-A__Case=Dat\|Definite=Def\|Gender=Neut\|Number=Sing`, `N-A__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-A__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-A__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `N-A__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `N-A__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `N-A__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `N-A__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `N-A__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `N-A__Case=Dat\|Definite=Ind\|Number=Sing`, `N-A__Case=Dat\|Gender=Fem\|Number=Sing\|PronType=Prs`, `N-A__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Prs`, `N-A__Case=Dat\|Gender=Neut\|Number=Plur\|NumType=Card`, `N-A__Case=Dat\|Number=Sing\|Person=2\|PronType=Prs`, `N-A__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-A__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-A__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-A__Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur`, `N-A__Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing`, `N-A__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `N-A__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `N-A__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `N-A__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `N-A__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `N-A__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `N-A__Case=Gen\|Definite=Ind\|Number=Sing`, `N-A__Case=Gen\|Gender=Masc\|Number=Plur\|NumType=Card`, `N-A__Case=Gen\|Gender=Neut\|Number=Plur\|NumType=Card`, `N-A__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `N-A__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-A__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-A__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `N-A__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing`, `N-A__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `N-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `N-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-A__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `N-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `N-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `N-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `N-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `N-A__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `N-A__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `N-A__Case=Nom\|Definite=Ind\|Number=Sing`, `N-A__Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-A__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Ind`, `N-A__Case=Nom\|Gender=Masc\|Number=Plur\|NumType=Card`, `N-A__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `N-A__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Prs`, `N-A__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `N-A__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `N-A__Foreign=Yes`, `N-A__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `N-A__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `N-A__Mood=Sub\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `N-A__NumType=Card`, `N-A__VerbForm=Inf\|Voice=Act`, `N-A__VerbForm=Inf\|Voice=Mid`, `N-A__VerbForm=Sup\|Voice=Act`, `N-D`, `N-D__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing`, `N-D__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `N-D__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-D__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-D__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `N-D__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-D__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `N-D__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `N-D__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `N-D__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `N-D__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `N-D__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `N-D__Case=Acc\|Definite=Ind\|Number=Sing`, `N-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `N-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `N-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-D__Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing`, `N-D__Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing`, `N-D__Case=Dat\|Definite=Def\|Gender=Neut\|Number=Plur`, `N-D__Case=Dat\|Definite=Def\|Gender=Neut\|Number=Sing`, `N-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `N-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `N-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `N-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `N-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `N-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `N-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `N-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `N-D__Case=Dat\|Definite=Ind\|Number=Sing`, `N-D__Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur`, `N-D__Case=Dat\|Gender=Fem\|Number=Plur\|NumType=Card`, `N-D__Case=Dat\|Gender=Fem\|Number=Sing\|PronType=Prs`, `N-D__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Ind`, `N-D__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Prs`, `N-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Ind`, `N-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Prs`, `N-D__Case=Dat\|Number=Sing\|Person=1\|PronType=Prs`, `N-D__Case=Dat\|Number=Sing\|Person=2\|PronType=Prs`, `N-D__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-D__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-D__Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing`, `N-D__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `N-D__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `N-D__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `N-D__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `N-D__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `N-D__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `N-D__Case=Gen\|Definite=Ind\|Number=Sing`, `N-D__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Prs`, `N-D__Case=Gen\|Gender=Neut\|Number=Plur\|NumType=Card`, `N-D__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `N-D__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-D__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `N-D__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `N-D__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-D__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-D__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `N-D__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-D__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `N-D__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `N-D__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `N-D__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `N-D__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `N-D__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `N-D__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-D__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Ind`, `N-D__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Ind`, `N-D__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Int`, `N-D__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `N-D__Degree=Cmp`, `N-D__Foreign=Yes`, `N-D__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `N-D__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `N-D__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `N-D__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `N-D__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `N-D__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `N-D__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `N-D__Mood=Sub\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `N-D__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `N-D__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `N-D__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `N-D__VerbForm=Inf\|Voice=Act`, `N-D__VerbForm=Inf\|Voice=Mid`, `N-D__VerbForm=Part\|Voice=Act`, `N-D__VerbForm=Sup\|Voice=Act`, `N-G`, `N-G__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur`, `N-G__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `N-G__Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur`, `N-G__Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing`, `N-G__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `N-G__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-G__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `N-G__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-G__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `N-G__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `N-G__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `N-G__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `N-G__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `N-G__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `N-G__Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur`, `N-G__Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-G__Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-G__Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur`, `N-G__Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-G__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Prs`, `N-G__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur`, `N-G__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-G__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-G__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `N-G__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-G__Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `N-G__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `N-G__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `N-G__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `N-G__Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur`, `N-G__Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-G__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur`, `N-G__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `N-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `N-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-G__Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing`, `N-G__Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur`, `N-G__Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing`, `N-G__Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur`, `N-G__Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing`, `N-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `N-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-G__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `N-G__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `N-G__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `N-G__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `N-G__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `N-G__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `N-G__Case=Gen\|Definite=Ind\|Number=Sing`, `N-G__Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-G__Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur`, `N-G__Case=Gen\|Gender=Fem\|Number=Plur\|PronType=Prs`, `N-G__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Prs`, `N-G__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Prs`, `N-G__Case=Gen\|Gender=Neut\|Number=Plur\|PronType=Ind`, `N-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Prs`, `N-G__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-G__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `N-G__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `N-G__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `N-G__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-G__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-G__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `N-G__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `N-G__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `N-G__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `N-G__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `N-G__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `N-G__Case=Nom\|Definite=Ind\|Number=Sing`, `N-G__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur`, `N-G__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-G__Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-G__Case=Nom\|Gender=Fem\|Number=Plur\|VerbForm=Part\|Voice=Act`, `N-G__Foreign=Yes`, `N-G__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `N-G__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `N-G__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `N-G__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `N-G__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `N-G__Mood=Sub\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `N-G__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `N-G__VerbForm=Inf\|Voice=Act`, `N-N`, `N-N__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing`, `N-N__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `N-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `N-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `N-N__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `N-N__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `N-N__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `N-N__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `N-N__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `N-N__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `N-N__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Ind`, `N-N__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Dem`, `N-N__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Ind`, `N-N__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `N-N__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `N-N__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `N-N__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `N-N__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `N-N__Case=Dat\|Number=Sing\|Person=1\|PronType=Prs`, `N-N__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `N-N__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `N-N__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `N-N__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `N-N__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `N-N__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `N-N__Case=Gen\|Definite=Ind\|Number=Sing`, `N-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `N-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `N-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-N__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `N-N__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur`, `N-N__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing`, `N-N__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur`, `N-N__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `N-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `N-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `N-N__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `N-N__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `N-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `N-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `N-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `N-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `N-N__Case=Nom\|Definite=Ind\|Number=Sing`, `N-N__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `N-N__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing`, `N-N__Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing`, `N-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Prs`, `N-N__Case=Nom\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `N-N__Case=Nom\|Gender=Masc\|Number=Plur\|NumType=Card`, `N-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Dem`, `N-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Dem`, `N-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `N-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Prs`, `N-N__Case=Nom\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `N-N__Case=Nom\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `N-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `N-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Int`, `N-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Prs`, `N-N__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `N-N__Case=Nom\|Number=Plur\|Person=1\|PronType=Prs`, `N-N__Case=Nom\|Number=Plur\|Person=2\|PronType=Prs`, `N-N__Case=Nom\|Number=Sing\|Person=1\|PronType=Prs`, `N-N__Case=Nom\|Number=Sing\|Person=2\|PronType=Prs`, `N-N__Foreign=Yes`, `N-N__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `N-N__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `N-N__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `N-N__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `N-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `N-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `N-N__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `N-N__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `N-N__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `N-N__NumType=Frac`, `N-N__VerbForm=Inf\|Voice=Act`, `N-N__VerbForm=Part\|Voice=Act`, `N-N__VerbForm=Sup\|Voice=Act`, `NEG`, `NEG-1`, `NEG-2`, `NEG-3__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `NEG__Foreign=Yes`, `NP-NPR__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NP-SBJ-1`, `NPR-1__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPR-A`, `NPR-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `NPR-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NPR-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-A__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur`, `NPR-A__Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing`, `NPR-A__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing`, `NPR-A__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur`, `NPR-A__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `NPR-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NPR-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `NPR-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NPR-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NPR-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPR-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPR-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPR-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NPR-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPR-A__Case=Acc\|Definite=Ind\|Number=Sing`, `NPR-A__Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Ind`, `NPR-A__Case=Acc\|Gender=Neut\|Number=Sing\|NumType=Card`, `NPR-A__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-A__Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing`, `NPR-A__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-A__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NPR-A__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPR-A__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPR-A__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPR-A__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NPR-A__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPR-A__Case=Dat\|Definite=Ind\|Number=Sing`, `NPR-A__Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur`, `NPR-A__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NPR-A__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Ind`, `NPR-A__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NPR-A__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPR-A__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPR-A__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPR-A__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPR-A__Case=Gen\|Definite=Ind\|Number=Sing`, `NPR-A__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Prs`, `NPR-A__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-A__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NPR-A__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `NPR-A__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing`, `NPR-A__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `NPR-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NPR-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPR-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPR-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPR-A__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NPR-A__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPR-A__Case=Nom\|Definite=Ind\|Number=Sing`, `NPR-A__Case=Nom\|Number=Sing\|Person=1\|PronType=Prs`, `NPR-A__Foreign=Yes`, `NPR-A__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `NPR-A__NumType=Ord`, `NPR-A__VerbForm=Inf\|Voice=Act`, `NPR-A__VerbForm=Sup\|Voice=Act`, `NPR-D`, `NPR-D__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NPR-D__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing`, `NPR-D__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur`, `NPR-D__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `NPR-D__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `NPR-D__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NPR-D__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPR-D__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPR-D__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPR-D__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NPR-D__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPR-D__Case=Acc\|Definite=Ind\|Number=Sing`, `NPR-D__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Prs`, `NPR-D__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `NPR-D__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `NPR-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NPR-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-D__Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing`, `NPR-D__Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing`, `NPR-D__Case=Dat\|Definite=Def\|Gender=Neut\|Number=Sing`, `NPR-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `NPR-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NPR-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPR-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPR-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPR-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NPR-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPR-D__Case=Dat\|Definite=Ind\|Number=Sing`, `NPR-D__Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-D__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NPR-D__Case=Dat\|Number=Sing\|Person=1\|PronType=Prs`, `NPR-D__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NPR-D__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPR-D__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPR-D__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPR-D__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPR-D__Case=Gen\|Definite=Ind\|Number=Sing`, `NPR-D__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-D__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NPR-D__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `NPR-D__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing`, `NPR-D__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `NPR-D__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPR-D__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPR-D__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPR-D__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPR-D__Case=Nom\|Definite=Ind\|Number=Sing`, `NPR-D__Case=Nom\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `NPR-D__Foreign=Yes`, `NPR-D__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `NPR-D__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `NPR-D__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `NPR-G`, `NPR-G__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur`, `NPR-G__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NPR-G__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPR-G__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPR-G__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPR-G__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPR-G__Case=Acc\|Definite=Ind\|Number=Sing`, `NPR-G__Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NPR-G__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `NPR-G__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NPR-G__Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing`, `NPR-G__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `NPR-G__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NPR-G__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPR-G__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPR-G__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPR-G__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPR-G__Case=Dat\|Definite=Ind\|Number=Sing`, `NPR-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NPR-G__Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur`, `NPR-G__Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing`, `NPR-G__Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur`, `NPR-G__Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing`, `NPR-G__Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing`, `NPR-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NPR-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-G__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NPR-G__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPR-G__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPR-G__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPR-G__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NPR-G__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPR-G__Case=Gen\|Definite=Ind\|Number=Sing`, `NPR-G__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `NPR-G__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-G__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `NPR-G__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `NPR-G__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NPR-G__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-G__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `NPR-G__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `NPR-G__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPR-G__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPR-G__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPR-G__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPR-G__Case=Nom\|Definite=Ind\|Number=Sing`, `NPR-G__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NPR-G__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-G__Case=Nom\|Gender=Masc\|Number=Sing`, `NPR-G__Foreign=Yes`, `NPR-G__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `NPR-G__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `NPR-G__VerbForm=Inf\|Voice=Act`, `NPR-N`, `NPR-N__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `NPR-N__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-N__Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing`, `NPR-N__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing`, `NPR-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NPR-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-N__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NPR-N__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPR-N__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPR-N__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPR-N__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPR-N__Case=Acc\|Definite=Ind\|Number=Sing`, `NPR-N__Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NPR-N__Case=Acc\|Gender=Masc\|Number=Sing`, `NPR-N__Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing`, `NPR-N__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NPR-N__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPR-N__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPR-N__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPR-N__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NPR-N__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPR-N__Case=Dat\|Definite=Ind\|Number=Sing`, `NPR-N__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Prs`, `NPR-N__Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur`, `NPR-N__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPR-N__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPR-N__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPR-N__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPR-N__Case=Gen\|Definite=Ind\|Number=Sing`, `NPR-N__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Prs`, `NPR-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `NPR-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `NPR-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NPR-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `NPR-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NPR-N__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `NPR-N__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `NPR-N__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing`, `NPR-N__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur`, `NPR-N__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `NPR-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NPR-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `NPR-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NPR-N__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NPR-N__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPR-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPR-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPR-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NPR-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPR-N__Case=Nom\|Definite=Ind\|Number=Sing`, `NPR-N__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NPR-N__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPR-N__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Ind`, `NPR-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Dem`, `NPR-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Prs`, `NPR-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Dem`, `NPR-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Dem`, `NPR-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `NPR-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Prs`, `NPR-N__Case=Nom\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `NPR-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Dem`, `NPR-N__Case=Nom\|Number=Plur\|Person=2\|PronType=Prs`, `NPR-N__Case=Nom\|Number=Sing\|Person=1\|PronType=Prs`, `NPR-N__Case=Nom\|Number=Sing\|Person=2\|PronType=Prs`, `NPR-N__Foreign=Yes`, `NPR-N__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `NPR-N__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `NPR-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `NPR-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `NPR-N__Mood=Sub\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `NPR-N__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `NPR-N__NumType=Card`, `NPR-N__VerbForm=Inf\|Voice=Act`, `NPR-N__VerbForm=Sup\|Voice=Act`, `NPR-S__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPR-V__Foreign=Yes`, `NPRO-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPRS-A`, `NPRS-A__Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur`, `NPRS-A__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur`, `NPRS-A__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur`, `NPRS-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `NPRS-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NPRS-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPRS-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPRS-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPRS-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NPRS-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPRS-A__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPRS-A__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPRS-A__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPRS-A__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPRS-A__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPRS-A__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPRS-A__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPRS-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NPRS-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPRS-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPRS-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPRS-A__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NPRS-A__Case=Nom\|Definite=Ind\|Number=Sing`, `NPRS-A__Foreign=Yes`, `NPRS-D`, `NPRS-D__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPRS-D__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPRS-D__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPRS-D__Case=Dat\|Definite=Def\|Gender=Masc\|Number=Plur`, `NPRS-D__Case=Dat\|Definite=Def\|Gender=Neut\|Number=Plur`, `NPRS-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NPRS-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPRS-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPRS-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NPRS-D__Case=Dat\|Definite=Ind\|Number=Sing`, `NPRS-D__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPRS-D__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPRS-D__Foreign=Yes`, `NPRS-G`, `NPRS-G__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing`, `NPRS-G__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NPRS-G__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NPRS-G__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NPRS-G__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPRS-G__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPRS-G__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPRS-G__Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur`, `NPRS-G__Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing`, `NPRS-G__Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur`, `NPRS-G__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NPRS-G__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPRS-G__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPRS-G__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPRS-G__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NPRS-G__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPRS-G__Case=Gen\|Definite=Ind\|Number=Sing`, `NPRS-G__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPRS-G__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPRS-G__Foreign=Yes`, `NPRS-N`, `NPRS-N__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NPRS-N__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPRS-N__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPRS-N__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NPRS-N__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPRS-N__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur`, `NPRS-N__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur`, `NPRS-N__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur`, `NPRS-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `NPRS-N__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NPRS-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NPRS-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPRS-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NPRS-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NPRS-N__Foreign=Yes`, `NPRS-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `NPR__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NPR__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NS-A`, `NS-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NS-A__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur`, `NS-A__Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur`, `NS-A__Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing`, `NS-A__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur`, `NS-A__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing`, `NS-A__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur`, `NS-A__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `NS-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `NS-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `NS-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `NS-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NS-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NS-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NS-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NS-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NS-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NS-A__Case=Acc\|Definite=Ind\|Number=Plur`, `NS-A__Case=Acc\|Definite=Ind\|Number=Sing`, `NS-A__Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NS-A__Case=Acc\|Gender=Fem\|Number=Plur\|NumType=Card`, `NS-A__Case=Acc\|Gender=Masc\|Number=Plur\|NumType=Card`, `NS-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Prs`, `NS-A__Case=Acc\|Gender=Neut\|Number=Plur\|NumType=Card`, `NS-A__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `NS-A__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NS-A__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NS-A__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NS-A__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NS-A__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NS-A__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NS-A__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NS-A__Case=Dat\|Definite=Ind\|Number=Sing`, `NS-A__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NS-A__Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur`, `NS-A__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NS-A__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NS-A__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NS-A__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NS-A__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NS-A__Case=Gen\|Gender=Masc\|Number=Plur\|NumType=Card`, `NS-A__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur`, `NS-A__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `NS-A__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur`, `NS-A__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur`, `NS-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NS-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `NS-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NS-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NS-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NS-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NS-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NS-A__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NS-A__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NS-A__Case=Nom\|Definite=Ind\|Number=Sing`, `NS-A__Case=Nom\|Gender=Masc\|Number=Plur\|NumType=Card`, `NS-A__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `NS-A__Degree=Cmp`, `NS-A__Foreign=Yes`, `NS-A__NumType=Ord`, `NS-A__VerbForm=Inf\|Voice=Act`, `NS-D`, `NS-D__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NS-D__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NS-D__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NS-D__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NS-D__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NS-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NS-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NS-D__Case=Dat\|Definite=Def\|Gender=Fem\|Number=Plur`, `NS-D__Case=Dat\|Definite=Def\|Gender=Masc\|Number=Plur`, `NS-D__Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing`, `NS-D__Case=Dat\|Definite=Def\|Gender=Neut\|Number=Plur`, `NS-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `NS-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `NS-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NS-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `NS-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NS-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NS-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NS-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NS-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NS-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NS-D__Case=Dat\|Definite=Ind\|Number=Sing`, `NS-D__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Ind`, `NS-D__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Ind`, `NS-D__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NS-D__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NS-D__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NS-D__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NS-D__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NS-D__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NS-D__Foreign=Yes`, `NS-D__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `NS-D__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `NS-D__Mood=Sub\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `NS-D__VerbForm=Sup\|Voice=Act`, `NS-G`, `NS-G__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `NS-G__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NS-G__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NS-G__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `NS-G__Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing`, `NS-G__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur`, `NS-G__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NS-G__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `NS-G__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NS-G__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NS-G__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NS-G__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NS-G__Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NS-G__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NS-G__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NS-G__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NS-G__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NS-G__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur`, `NS-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NS-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NS-G__Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur`, `NS-G__Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur`, `NS-G__Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur`, `NS-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `NS-G__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NS-G__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NS-G__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NS-G__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NS-G__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NS-G__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NS-G__Case=Gen\|Definite=Ind\|Number=Plur`, `NS-G__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Prs`, `NS-G__Case=Gen\|Gender=Neut\|Number=Plur\|PronType=Prs`, `NS-G__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `NS-G__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `NS-G__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NS-G__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `NS-G__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NS-G__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NS-G__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NS-G__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NS-G__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NS-G__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur`, `NS-G__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NS-G__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NS-G__Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur`, `NS-G__Case=Nom\|Gender=Neut\|Number=Plur\|NumType=Card`, `NS-G__Foreign=Yes`, `NS-G__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `NS-G__VerbForm=Inf\|Voice=Act`, `NS-N`, `NS-N__Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur`, `NS-N__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur`, `NS-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NS-N__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NS-N__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NS-N__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NS-N__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NS-N__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NS-N__Case=Acc\|Gender=Masc\|Number=Plur\|NumType=Card`, `NS-N__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NS-N__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NS-N__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NS-N__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NS-N__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NS-N__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NS-N__Case=Gen\|Gender=Neut\|Number=Plur\|NumType=Card`, `NS-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NS-N__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur`, `NS-N__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `NS-N__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur`, `NS-N__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing`, `NS-N__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur`, `NS-N__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `NS-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `NS-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NS-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `NS-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NS-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `NS-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NS-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur`, `NS-N__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NS-N__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NS-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NS-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NS-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NS-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NS-N__Case=Nom\|Definite=Ind\|Number=Plur`, `NS-N__Case=Nom\|Definite=Ind\|Number=Sing`, `NS-N__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NS-N__Case=Nom\|Gender=Masc\|Number=Plur\|NumType=Card`, `NS-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Prs`, `NS-N__Case=Nom\|Gender=Masc\|Number=Plur\|VerbForm=Part\|Voice=Act`, `NS-N__Case=Nom\|Gender=Neut\|Number=Plur\|NumType=Card`, `NS-N__Case=Nom\|Number=Plur\|Person=2\|PronType=Prs`, `NS-N__Foreign=Yes`, `NS-N__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `NS-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `NS-N__NumType=Ord`, `NS__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NS__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NS__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur`, `NUM`, `NUM-1__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `NUM-A`, `NUM-A__Case=Acc`, `NUM-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NUM-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NUM-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NUM-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `NUM-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NUM-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `NUM-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NUM-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `NUM-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NUM-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NUM-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NUM-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NUM-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NUM-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NUM-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NUM-A__Case=Acc\|Gender=Fem\|Number=Plur\|NumType=Card`, `NUM-A__Case=Acc\|Gender=Masc\|Number=Plur\|NumType=Card`, `NUM-A__Case=Acc\|Gender=Masc\|Number=Sing\|NumType=Card`, `NUM-A__Case=Acc\|Gender=Neut\|Number=Plur\|NumType=Card`, `NUM-A__Case=Acc\|Gender=Neut\|Number=Sing\|NumType=Card`, `NUM-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Ind`, `NUM-A__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NUM-A__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NUM-A__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NUM-A__Case=Dat\|Definite=Ind\|Number=Sing`, `NUM-A__Case=Dat\|Gender=Fem\|Number=Sing\|NumType=Card`, `NUM-A__Case=Dat\|Gender=Masc\|Number=Plur\|NumType=Card`, `NUM-A__Case=Dat\|Gender=Neut\|Number=Plur\|NumType=Card`, `NUM-A__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NUM-A__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NUM-A__Case=Gen\|Gender=Fem\|Number=Plur\|NumType=Card`, `NUM-A__Case=Gen\|Gender=Masc\|Number=Plur\|NumType=Card`, `NUM-A__Case=Gen\|Gender=Neut\|Number=Plur\|NumType=Card`, `NUM-A__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NUM-A__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NUM-A__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `NUM-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `NUM-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NUM-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NUM-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NUM-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NUM-A__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NUM-A__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NUM-A__Case=Nom\|Gender=Fem\|Number=Plur\|NumType=Card`, `NUM-A__Case=Nom\|Gender=Masc\|Number=Plur\|NumType=Card`, `NUM-A__Case=Nom\|Gender=Neut\|Number=Plur\|NumType=Card`, `NUM-A__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `NUM-A__Foreign=Yes`, `NUM-A__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `NUM-A__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `NUM-A__NumType=Card`, `NUM-A__NumType=Ord`, `NUM-D`, `NUM-D__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NUM-D__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NUM-D__Case=Acc\|Gender=Fem\|Number=Plur\|NumType=Card`, `NUM-D__Case=Acc\|Gender=Masc\|Number=Plur\|NumType=Card`, `NUM-D__Case=Acc\|Gender=Neut\|Number=Plur\|NumType=Card`, `NUM-D__Case=Dat`, `NUM-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `NUM-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NUM-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `NUM-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NUM-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NUM-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NUM-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NUM-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NUM-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NUM-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `NUM-D__Case=Dat\|Gender=Fem\|Number=Plur\|NumType=Card`, `NUM-D__Case=Dat\|Gender=Masc\|Number=Plur\|NumType=Card`, `NUM-D__Case=Dat\|Gender=Neut\|Number=Plur\|NumType=Card`, `NUM-D__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NUM-D__Case=Gen\|Gender=Masc\|Number=Plur\|NumType=Card`, `NUM-D__Case=Gen\|Gender=Neut\|Number=Plur\|NumType=Card`, `NUM-D__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `NUM-D__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NUM-D__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NUM-D__Case=Nom\|Gender=Masc\|Number=Plur\|NumType=Card`, `NUM-D__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `NUM-D__Foreign=Yes`, `NUM-D__NumType=Card`, `NUM-G`, `NUM-G__Case=Acc\|Gender=Masc\|Number=Plur\|NumType=Card`, `NUM-G__Case=Acc\|Gender=Neut\|Number=Plur\|NumType=Card`, `NUM-G__Case=Acc\|Gender=Neut\|Number=Sing\|NumType=Card`, `NUM-G__Case=Dat\|Gender=Masc\|Number=Plur\|NumType=Card`, `NUM-G__Case=Gen`, `NUM-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NUM-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `NUM-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NUM-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NUM-G__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NUM-G__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NUM-G__Case=Gen\|Gender=Fem\|Number=Plur\|NumType=Card`, `NUM-G__Case=Gen\|Gender=Masc\|Number=Plur\|NumType=Card`, `NUM-G__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Ind`, `NUM-G__Case=Gen\|Gender=Neut\|Number=Plur\|NumType=Card`, `NUM-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Ind`, `NUM-G__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NUM-G__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NUM-G__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NUM-G__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NUM-G__Case=Nom\|Gender=Masc\|Number=Plur\|NumType=Card`, `NUM-G__Foreign=Yes`, `NUM-G__NumType=Card`, `NUM-N`, `NUM-N__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NUM-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NUM-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NUM-N__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NUM-N__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NUM-N__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NUM-N__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NUM-N__Case=Acc\|Gender=Fem\|Number=Plur\|NumType=Card`, `NUM-N__Case=Acc\|Gender=Masc\|Number=Plur\|NumType=Card`, `NUM-N__Case=Acc\|Gender=Neut\|Number=Plur\|NumType=Card`, `NUM-N__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NUM-N__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NUM-N__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NUM-N__Case=Dat\|Gender=Fem\|Number=Plur\|NumType=Card`, `NUM-N__Case=Dat\|Gender=Masc\|Number=Plur\|NumType=Card`, `NUM-N__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NUM-N__Case=Gen\|Gender=Fem\|Number=Plur\|NumType=Card`, `NUM-N__Case=Gen\|Gender=Masc\|Number=Sing\|NumType=Card`, `NUM-N__Case=Gen\|Gender=Neut\|Number=Plur\|NumType=Card`, `NUM-N__Case=Nom`, `NUM-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `NUM-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `NUM-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `NUM-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NUM-N__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `NUM-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `NUM-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `NUM-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `NUM-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `NUM-N__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `NUM-N__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `NUM-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `NUM-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NUM-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `NUM-N__Case=Nom\|Definite=Ind\|Number=Sing`, `NUM-N__Case=Nom\|Gender=Fem\|Number=Plur\|NumType=Card`, `NUM-N__Case=Nom\|Gender=Masc\|Number=Plur\|NumType=Card`, `NUM-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Int`, `NUM-N__Case=Nom\|Gender=Neut\|Number=Plur\|NumType=Card`, `NUM-N__Case=Nom\|Gender=Neut\|Number=Sing\|NumType=Card`, `NUM-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `NUM-N__Foreign=Yes`, `NUM-N__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `NUM-N__NumType=Card`, `NUM-N__NumType=Ord`, `NUM__Case=Gen\|Gender=Masc\|Number=Plur\|NumType=Card`, `NUM__Case=Gen\|Gender=Neut\|Number=Plur\|NumType=Card`, `NUM__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `NUM__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `NUM__Case=Nom\|Definite=Ind\|Number=Sing`, `NUM__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `N__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `N__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `N__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `N__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `N__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `N__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `N__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `N__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `N__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `N__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ONE-A`, `ONE-A__Case=Acc`, `ONE-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ONE-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ONE-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ONE-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ONE-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ONE-A__Case=Acc\|Gender=Fem\|Number=Sing\|NumType=Card`, `ONE-A__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Ind`, `ONE-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Ind`, `ONE-A__Case=Acc\|Gender=Masc\|Number=Sing\|NumType=Card`, `ONE-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Dem`, `ONE-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Ind`, `ONE-A__Case=Acc\|Gender=Neut\|Number=Sing\|NumType=Card`, `ONE-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Ind`, `ONE-A__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ONE-A__Case=Dat\|Gender=Fem\|Number=Sing\|NumType=Card`, `ONE-A__Case=Gen\|Gender=Neut\|Number=Plur\|PronType=Ind`, `ONE-A__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ONE-A__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ONE-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ONE-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ONE-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ONE-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ONE-A__Case=Nom\|Gender=Fem\|Number=Plur\|NumType=Card`, `ONE-A__Case=Nom\|Gender=Fem\|Number=Sing\|NumType=Card`, `ONE-A__Case=Nom\|Gender=Masc\|Number=Sing\|NumType=Card`, `ONE-A__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `ONE-A__Case=Nom\|Gender=Neut\|Number=Sing\|NumType=Card`, `ONE-A__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `ONE-A__NumType=Card`, `ONE-D`, `ONE-D__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Ind`, `ONE-D__Case=Dat`, `ONE-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ONE-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ONE-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `ONE-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ONE-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ONE-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ONE-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ONE-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ONE-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ONE-D__Case=Dat\|Gender=Fem\|Number=Plur\|PronType=Ind`, `ONE-D__Case=Dat\|Gender=Fem\|Number=Sing\|NumType=Card`, `ONE-D__Case=Dat\|Gender=Fem\|Number=Sing\|PronType=Ind`, `ONE-D__Case=Dat\|Gender=Masc\|Number=Sing\|NumType=Card`, `ONE-D__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Ind`, `ONE-D__Case=Dat\|Gender=Neut\|Number=Sing\|NumType=Card`, `ONE-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Ind`, `ONE-D__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `ONE-D__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Ind`, `ONE-D__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `ONE-G`, `ONE-G__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Ind`, `ONE-G__Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing`, `ONE-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ONE-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ONE-G__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `ONE-G__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `ONE-G__Case=Gen\|Gender=Fem\|Number=Sing\|NumType=Card`, `ONE-G__Case=Gen\|Gender=Masc\|Number=Sing\|NumType=Card`, `ONE-G__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Ind`, `ONE-G__Case=Gen\|Gender=Neut\|Number=Plur\|PronType=Ind`, `ONE-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Ind`, `ONE-N`, `ONE-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ONE-N__Case=Acc\|Gender=Masc\|Number=Sing\|NumType=Card`, `ONE-N__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Ind`, `ONE-N__Case=Acc\|Gender=Neut\|Number=Sing\|NumType=Card`, `ONE-N__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Ind`, `ONE-N__Case=Gen\|Gender=Fem\|Number=Plur\|PronType=Ind`, `ONE-N__Case=Nom`, `ONE-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ONE-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ONE-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `ONE-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `ONE-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `ONE-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `ONE-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `ONE-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `ONE-N__Case=Nom\|Gender=Fem\|Number=Sing\|NumType=Card`, `ONE-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Ind`, `ONE-N__Case=Nom\|Gender=Masc\|Number=Sing\|NumType=Card`, `ONE-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `ONE-N__Case=Nom\|Gender=Neut\|Number=Plur\|NumType=Card`, `ONE-N__Case=Nom\|Gender=Neut\|Number=Sing\|NumType=Card`, `ONE-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `ONE-N__NumType=Card`, `ONES-G__Case=Gen\|Gender=Neut\|Number=Plur\|PronType=Ind`, `OTHER-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `OTHER-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `OTHER-A__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Ind`, `OTHER-A__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Ind`, `OTHER-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Ind`, `OTHER-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Ind`, `OTHER-A__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Ind`, `OTHER-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Ind`, `OTHER-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `OTHER-A__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Ind`, `OTHER-A__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `OTHER-D`, `OTHER-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `OTHER-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `OTHER-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `OTHER-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `OTHER-D__Case=Dat\|Gender=Fem\|Number=Plur\|PronType=Ind`, `OTHER-D__Case=Dat\|Gender=Fem\|Number=Sing\|PronType=Ind`, `OTHER-D__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Ind`, `OTHER-D__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Ind`, `OTHER-D__Case=Dat\|Gender=Neut\|Number=Plur\|PronType=Ind`, `OTHER-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Ind`, `OTHER-D__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Ind`, `OTHER-G`, `OTHER-G__Case=Gen\|Gender=Fem\|Number=Sing\|PronType=Ind`, `OTHER-G__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Ind`, `OTHER-G__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Ind`, `OTHER-G__Case=Gen\|Gender=Neut\|Number=Plur\|PronType=Ind`, `OTHER-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Ind`, `OTHER-N__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Ind`, `OTHER-N__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Ind`, `OTHER-N__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Ind`, `OTHER-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `OTHER-N__Case=Nom\|Definite=Ind\|Number=Sing`, `OTHER-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Ind`, `OTHER-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Ind`, `OTHER-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `OTHER-N__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Ind`, `OTHER-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `OTHER-WPRO__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `OTHERS-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `OTHERS-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `OTHERS-A__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Ind`, `OTHERS-A__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Ind`, `OTHERS-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Dem`, `OTHERS-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Ind`, `OTHERS-A__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Ind`, `OTHERS-A__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Ind`, `OTHERS-A__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Ind`, `OTHERS-A__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Ind`, `OTHERS-D__Case=Dat\|Gender=Fem\|Number=Plur\|PronType=Ind`, `OTHERS-D__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Ind`, `OTHERS-D__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Ind`, `OTHERS-D__Case=Dat\|Gender=Neut\|Number=Plur\|PronType=Ind`, `OTHERS-D__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `OTHERS-G__Case=Gen\|Gender=Fem\|Number=Plur\|PronType=Ind`, `OTHERS-G__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Ind`, `OTHERS-G__Case=Gen\|Gender=Neut\|Number=Plur\|PronType=Ind`, `OTHERS-N__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Ind`, `OTHERS-N__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Ind`, `OTHERS-N__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Ind`, `OTHERS-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Ind`, `OTHERS-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Ind`, `OTHERS-N__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Ind`, `OTHER__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Ind`, `P`, `POR-A`, `POR-D__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS-D__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `PRO-A`, `PRO-A__Case=Acc`, `PRO-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur`, `PRO-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `PRO-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `PRO-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `PRO-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `PRO-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `PRO-A__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur`, `PRO-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `PRO-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `PRO-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `PRO-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `PRO-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `PRO-A__Case=Acc\|Definite=Ind\|Number=Sing`, `PRO-A__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Dem`, `PRO-A__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Ind`, `PRO-A__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Prs`, `PRO-A__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Dem`, `PRO-A__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Prs`, `PRO-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Dem`, `PRO-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Ind`, `PRO-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Prs`, `PRO-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Dem`, `PRO-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Ind`, `PRO-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Prs`, `PRO-A__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Dem`, `PRO-A__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Prs`, `PRO-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Dem`, `PRO-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Ind`, `PRO-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Int`, `PRO-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Prs`, `PRO-A__Case=Acc\|Number=Plur\|Person=1\|PronType=Prs`, `PRO-A__Case=Acc\|Number=Plur\|Person=2\|PronType=Prs`, `PRO-A__Case=Acc\|Number=Sing\|Person=1\|PronType=Prs`, `PRO-A__Case=Acc\|Number=Sing\|Person=2\|PronType=Prs`, `PRO-A__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `PRO-A__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `PRO-A__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `PRO-A__Case=Dat\|Gender=Fem\|Number=Sing\|PronType=Prs`, `PRO-A__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Prs`, `PRO-A__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Prs`, `PRO-A__Case=Dat\|Number=Plur\|Person=1\|PronType=Prs`, `PRO-A__Case=Dat\|Number=Plur\|Person=2\|PronType=Prs`, `PRO-A__Case=Dat\|Number=Sing\|Person=2\|PronType=Prs`, `PRO-A__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `PRO-A__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `PRO-A__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `PRO-A__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `PRO-A__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `PRO-A__Case=Gen\|Gender=Fem\|Number=Plur\|PronType=Prs`, `PRO-A__Case=Gen\|Gender=Fem\|Number=Sing\|PronType=Ind`, `PRO-A__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Dem`, `PRO-A__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Prs`, `PRO-A__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Prs`, `PRO-A__Case=Gen\|Gender=Neut\|Number=Plur\|PronType=Prs`, `PRO-A__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Prs`, `PRO-A__Case=Gen\|Number=Plur\|Person=2\|PronType=Prs`, `PRO-A__Case=Gen\|Number=Sing\|Person=1\|PronType=Prs`, `PRO-A__Case=Gen\|Number=Sing\|Person=2\|PronType=Prs`, `PRO-A__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur`, `PRO-A__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `PRO-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `PRO-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `PRO-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `PRO-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `PRO-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `PRO-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `PRO-A__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `PRO-A__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur`, `PRO-A__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Dem`, `PRO-A__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Prs`, `PRO-A__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Dem`, `PRO-A__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Prs`, `PRO-A__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Prs`, `PRO-A__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `PRO-A__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Prs`, `PRO-A__Case=Nom\|Gender=Neut\|Number=Plur\|NumType=Card`, `PRO-A__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Dem`, `PRO-A__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Prs`, `PRO-A__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Dem`, `PRO-A__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `PRO-A__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Int`, `PRO-A__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Prs`, `PRO-A__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `PRO-A__Foreign=Yes`, `PRO-A__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `PRO-A__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `PRO-A__VerbForm=Inf\|Voice=Act`, `PRO-D`, `PRO-D__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `PRO-D__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `PRO-D__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `PRO-D__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `PRO-D__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `PRO-D__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Prs`, `PRO-D__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Prs`, `PRO-D__Case=Acc\|Number=Plur\|Person=1\|PronType=Prs`, `PRO-D__Case=Acc\|Number=Plur\|Person=2\|PronType=Prs`, `PRO-D__Case=Dat`, `PRO-D__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `PRO-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `PRO-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `PRO-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `PRO-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `PRO-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `PRO-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `PRO-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `PRO-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `PRO-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `PRO-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `PRO-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `PRO-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `PRO-D__Case=Dat\|Gender=Fem\|Number=Plur\|PronType=Dem`, `PRO-D__Case=Dat\|Gender=Fem\|Number=Plur\|PronType=Ind`, `PRO-D__Case=Dat\|Gender=Fem\|Number=Plur\|PronType=Prs`, `PRO-D__Case=Dat\|Gender=Fem\|Number=Sing\|PronType=Dem`, `PRO-D__Case=Dat\|Gender=Fem\|Number=Sing\|PronType=Ind`, `PRO-D__Case=Dat\|Gender=Fem\|Number=Sing\|PronType=Prs`, `PRO-D__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Dem`, `PRO-D__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Ind`, `PRO-D__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Prs`, `PRO-D__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Dem`, `PRO-D__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Ind`, `PRO-D__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Prs`, `PRO-D__Case=Dat\|Gender=Neut\|Number=Plur\|PronType=Dem`, `PRO-D__Case=Dat\|Gender=Neut\|Number=Plur\|PronType=Prs`, `PRO-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Dem`, `PRO-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Ind`, `PRO-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Prs`, `PRO-D__Case=Dat\|Number=Plur\|Person=1\|PronType=Prs`, `PRO-D__Case=Dat\|Number=Plur\|Person=2\|PronType=Prs`, `PRO-D__Case=Dat\|Number=Sing\|Person=1\|PronType=Prs`, `PRO-D__Case=Dat\|Number=Sing\|Person=2\|PronType=Prs`, `PRO-D__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `PRO-D__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `PRO-D__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `PRO-D__Case=Gen\|Gender=Fem\|Number=Plur\|PronType=Dem`, `PRO-D__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Ind`, `PRO-D__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Prs`, `PRO-D__Case=Gen\|Gender=Neut\|Number=Plur\|PronType=Prs`, `PRO-D__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `PRO-D__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `PRO-D__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur`, `PRO-D__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `PRO-D__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `PRO-D__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing`, `PRO-D__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Ind`, `PRO-D__Case=Nom\|Gender=Masc\|Number=Plur\|VerbForm=Part\|Voice=Act`, `PRO-D__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Prs`, `PRO-D__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `PRO-D__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Prs`, `PRO-D__Case=Nom\|Number=Plur\|Person=2\|PronType=Prs`, `PRO-D__Foreign=Yes`, `PRO-D__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `PRO-D__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `PRO-D__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `PRO-D__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `PRO-D__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `PRO-D__Mood=Sub\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `PRO-D__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `PRO-G`, `PRO-G__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `PRO-G__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `PRO-G__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `PRO-G__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `PRO-G__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `PRO-G__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `PRO-G__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `PRO-G__Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing`, `PRO-G__Case=Acc\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `PRO-G__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Prs`, `PRO-G__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Prs`, `PRO-G__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `PRO-G__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `PRO-G__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `PRO-G__Case=Dat\|Definite=Ind\|Number=Sing`, `PRO-G__Case=Dat\|Gender=Fem\|Number=Sing\|PronType=Dem`, `PRO-G__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Ind`, `PRO-G__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Prs`, `PRO-G__Case=Dat\|Number=Plur\|Person=1\|PronType=Prs`, `PRO-G__Case=Dat\|Number=Sing\|Person=2\|PronType=Prs`, `PRO-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `PRO-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `PRO-G__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `PRO-G__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `PRO-G__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `PRO-G__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `PRO-G__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `PRO-G__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `PRO-G__Case=Gen\|Gender=Fem\|Number=Plur\|PronType=Dem`, `PRO-G__Case=Gen\|Gender=Fem\|Number=Plur\|PronType=Ind`, `PRO-G__Case=Gen\|Gender=Fem\|Number=Plur\|PronType=Prs`, `PRO-G__Case=Gen\|Gender=Fem\|Number=Sing\|PronType=Dem`, `PRO-G__Case=Gen\|Gender=Fem\|Number=Sing\|PronType=Ind`, `PRO-G__Case=Gen\|Gender=Fem\|Number=Sing\|PronType=Prs`, `PRO-G__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Dem`, `PRO-G__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Prs`, `PRO-G__Case=Gen\|Gender=Masc\|Number=Sing`, `PRO-G__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Dem`, `PRO-G__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Ind`, `PRO-G__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Prs`, `PRO-G__Case=Gen\|Gender=Neut\|Number=Plur\|PronType=Dem`, `PRO-G__Case=Gen\|Gender=Neut\|Number=Plur\|PronType=Prs`, `PRO-G__Case=Gen\|Gender=Neut\|Number=Sing`, `PRO-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Dem`, `PRO-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Ind`, `PRO-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Int`, `PRO-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Prs`, `PRO-G__Case=Gen\|Number=Plur\|Person=1\|PronType=Prs`, `PRO-G__Case=Gen\|Number=Plur\|Person=2\|PronType=Prs`, `PRO-G__Case=Gen\|Number=Sing\|Person=1\|PronType=Prs`, `PRO-G__Case=Gen\|Number=Sing\|Person=2\|PronType=Prs`, `PRO-G__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `PRO-G__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `PRO-G__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `PRO-G__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `PRO-G__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `PRO-G__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `PRO-G__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `PRO-G__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `PRO-G__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `PRO-G__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Ind`, `PRO-G__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Prs`, `PRO-G__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Prs`, `PRO-G__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Prs`, `PRO-G__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Dem`, `PRO-G__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `PRO-G__Degree=Cmp`, `PRO-G__Foreign=Yes`, `PRO-G__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `PRO-G__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `PRO-G__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `PRO-G__VerbForm=Inf\|Voice=Act`, `PRO-N`, `PRO-N-YYY__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Dem`, `PRO-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `PRO-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `PRO-N__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `PRO-N__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `PRO-N__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `PRO-N__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Dem`, `PRO-N__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Ind`, `PRO-N__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Prs`, `PRO-N__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Prs`, `PRO-N__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Dem`, `PRO-N__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Prs`, `PRO-N__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Dem`, `PRO-N__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Prs`, `PRO-N__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Dem`, `PRO-N__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Ind`, `PRO-N__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Int`, `PRO-N__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Prs`, `PRO-N__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Prs`, `PRO-N__Case=Dat\|Number=Plur\|Person=1\|PronType=Prs`, `PRO-N__Case=Dat\|Number=Plur\|Person=2\|PronType=Prs`, `PRO-N__Case=Dat\|Number=Sing\|Person=1\|PronType=Prs`, `PRO-N__Case=Dat\|Number=Sing\|Person=2\|PronType=Prs`, `PRO-N__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `PRO-N__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `PRO-N__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `PRO-N__Case=Gen\|Gender=Fem\|Number=Plur\|PronType=Prs`, `PRO-N__Case=Gen\|Gender=Fem\|Number=Sing\|PronType=Prs`, `PRO-N__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Prs`, `PRO-N__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Prs`, `PRO-N__Case=Gen\|Number=Plur\|Person=2\|PronType=Prs`, `PRO-N__Case=Gen\|Number=Sing\|Person=1\|PronType=Prs`, `PRO-N__Case=Gen\|Number=Sing\|Person=2\|PronType=Prs`, `PRO-N__Case=Nom`, `PRO-N__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing`, `PRO-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `PRO-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `PRO-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `PRO-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `PRO-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `PRO-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `PRO-N__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `PRO-N__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `PRO-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `PRO-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `PRO-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `PRO-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `PRO-N__Case=Nom\|Definite=Ind\|Number=Sing`, `PRO-N__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Dem`, `PRO-N__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Ind`, `PRO-N__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Int`, `PRO-N__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Prs`, `PRO-N__Case=Nom\|Gender=Fem\|Number=Sing`, `PRO-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Dem`, `PRO-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Ind`, `PRO-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Prs`, `PRO-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Dem`, `PRO-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Ind`, `PRO-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Prs`, `PRO-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Dem`, `PRO-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `PRO-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Int`, `PRO-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Prs`, `PRO-N__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Dem`, `PRO-N__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Prs`, `PRO-N__Case=Nom\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `PRO-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Dem`, `PRO-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `PRO-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Int`, `PRO-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Prs`, `PRO-N__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `PRO-N__Case=Nom\|Number=Plur\|Person=1\|PronType=Prs`, `PRO-N__Case=Nom\|Number=Plur\|Person=2\|PronType=Prs`, `PRO-N__Case=Nom\|Number=Sing\|Person=1\|PronType=Prs`, `PRO-N__Case=Nom\|Number=Sing\|Person=2\|PronType=Prs`, `PRO-N__Foreign=Yes`, `PRO-N__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `PRO-N__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `PRO-N__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `PRO-N__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `PRO-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `PRO-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `PRO-TTT-A__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Prs`, `PRO__Case=Nom\|Number=Sing\|Person=2\|PronType=Prs`, `P__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `P__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `P__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `P__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `P__Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing`, `P__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Dem`, `P__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Dem`, `P__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Prs`, `P__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Ind`, `P__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Dem`, `P__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Ind`, `P__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `P__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `P__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `P__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `P__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `P__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `P__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `P__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `P__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Prs`, `P__Case=Dat\|Gender=Neut\|Number=Plur\|PronType=Ind`, `P__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Ind`, `P__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Prs`, `P__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `P__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `P__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `P__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `P__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `P__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Dem`, `P__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Ind`, `P__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `P__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `P__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `P__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `P__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `P__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `P__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `P__Case=Nom\|Definite=Ind\|Number=Sing`, `P__Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing`, `P__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `P__Case=Nom\|Number=Plur\|Person=1\|PronType=Prs`, `P__Degree=Cmp`, `P__Degree=Sup`, `P__Foreign=Yes`, `P__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `P__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `P__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `P__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `P__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `P__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `P__VerbForm=Inf\|Voice=Act`, `P__VerbForm=Sup\|Voice=Act`, `Q`, `Q-A`, `Q-A__Case=Acc`, `Q-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `Q-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `Q-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `Q-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `Q-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `Q-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `Q-A__Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing`, `Q-A__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing`, `Q-A__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur`, `Q-A__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `Q-A__Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `Q-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `Q-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `Q-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `Q-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `Q-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `Q-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `Q-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `Q-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `Q-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `Q-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `Q-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `Q-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `Q-A__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Ind`, `Q-A__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Ind`, `Q-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Dem`, `Q-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Ind`, `Q-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Dem`, `Q-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Ind`, `Q-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Int`, `Q-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Prs`, `Q-A__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Ind`, `Q-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Ind`, `Q-A__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `Q-A__Case=Dat\|Definite=Def\|Gender=Neut\|Number=Sing`, `Q-A__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `Q-A__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `Q-A__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `Q-A__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Ind`, `Q-A__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Int`, `Q-A__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `Q-A__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `Q-A__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `Q-A__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `Q-A__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `Q-A__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `Q-A__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `Q-A__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `Q-A__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `Q-A__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `Q-A__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `Q-A__Case=Gen\|Gender=Fem\|Number=Plur\|PronType=Ind`, `Q-A__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Ind`, `Q-A__Case=Gen\|Gender=Neut\|Number=Plur\|PronType=Ind`, `Q-A__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Ind`, `Q-A__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `Q-A__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `Q-A__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `Q-A__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing`, `Q-A__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `Q-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `Q-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `Q-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `Q-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `Q-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `Q-A__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `Q-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `Q-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `Q-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `Q-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `Q-A__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `Q-A__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `Q-A__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Ind`, `Q-A__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Ind`, `Q-A__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Int`, `Q-A__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `Q-A__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Ind`, `Q-A__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `Q-A__Degree=Cmp`, `Q-A__Degree=Sup`, `Q-A__Foreign=Yes`, `Q-A__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Q-A__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Q-A__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Q-A__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Q-A__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Q-A__NumType=Card`, `Q-A__VerbForm=Inf\|Voice=Act`, `Q-D`, `Q-D__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `Q-D__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `Q-D__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `Q-D__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `Q-D__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `Q-D__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `Q-D__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `Q-D__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Ind`, `Q-D__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Ind`, `Q-D__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Ind`, `Q-D__Case=Dat`, `Q-D__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur`, `Q-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `Q-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `Q-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `Q-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `Q-D__Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `Q-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `Q-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `Q-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `Q-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `Q-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `Q-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `Q-D__Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur`, `Q-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `Q-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `Q-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `Q-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `Q-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `Q-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `Q-D__Case=Dat\|Gender=Fem\|Number=Plur\|PronType=Ind`, `Q-D__Case=Dat\|Gender=Fem\|Number=Sing\|PronType=Ind`, `Q-D__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Ind`, `Q-D__Case=Dat\|Gender=Masc\|Number=Sing\|NumType=Card`, `Q-D__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Ind`, `Q-D__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Prs`, `Q-D__Case=Dat\|Gender=Neut\|Number=Plur\|PronType=Dem`, `Q-D__Case=Dat\|Gender=Neut\|Number=Plur\|PronType=Ind`, `Q-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Ind`, `Q-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Int`, `Q-D__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `Q-D__Case=Gen\|Gender=Fem\|Number=Plur\|PronType=Ind`, `Q-D__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Ind`, `Q-D__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `Q-D__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur`, `Q-D__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `Q-D__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `Q-D__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `Q-D__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Dem`, `Q-D__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Ind`, `Q-D__Degree=Sup`, `Q-D__Foreign=Yes`, `Q-D__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Q-D__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Q-D__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Q-D__Mood=Sub\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Q-G`, `Q-G__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `Q-G__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `Q-G__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `Q-G__Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur`, `Q-G__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Ind`, `Q-G__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Dem`, `Q-G__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Ind`, `Q-G__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `Q-G__Case=Gen`, `Q-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `Q-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `Q-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `Q-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `Q-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `Q-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `Q-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `Q-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `Q-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `Q-G__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `Q-G__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `Q-G__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `Q-G__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `Q-G__Case=Gen\|Gender=Fem\|Number=Plur\|PronType=Ind`, `Q-G__Case=Gen\|Gender=Fem\|Number=Sing\|PronType=Ind`, `Q-G__Case=Gen\|Gender=Fem\|Number=Sing\|PronType=Int`, `Q-G__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Ind`, `Q-G__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Ind`, `Q-G__Case=Gen\|Gender=Neut\|Number=Plur\|PronType=Ind`, `Q-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Ind`, `Q-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Int`, `Q-G__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `Q-G__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `Q-G__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `Q-G__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `Q-G__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `Q-G__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `Q-G__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `Q-G__Foreign=Yes`, `Q-G__Mood=Sub\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Q-G__VerbForm=Inf\|Voice=Act`, `Q-N`, `Q-N__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `Q-N__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `Q-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `Q-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `Q-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `Q-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `Q-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `Q-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `Q-N__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur`, `Q-N__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `Q-N__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `Q-N__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `Q-N__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `Q-N__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `Q-N__Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing`, `Q-N__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Ind`, `Q-N__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Ind`, `Q-N__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Ind`, `Q-N__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Ind`, `Q-N__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Ind`, `Q-N__Case=Acc\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `Q-N__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `Q-N__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `Q-N__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `Q-N__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `Q-N__Case=Dat\|Gender=Masc\|Number=Plur\|NumType=Card`, `Q-N__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Ind`, `Q-N__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Ind`, `Q-N__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `Q-N__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `Q-N__Case=Gen\|Gender=Fem\|Number=Plur\|PronType=Ind`, `Q-N__Case=Nom`, `Q-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `Q-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `Q-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `Q-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `Q-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `Q-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `Q-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `Q-N__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `Q-N__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `Q-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `Q-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `Q-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `Q-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `Q-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `Q-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `Q-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing`, `Q-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur`, `Q-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur`, `Q-N__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `Q-N__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `Q-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `Q-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `Q-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `Q-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `Q-N__Case=Nom\|Definite=Ind\|Number=Sing`, `Q-N__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `Q-N__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing`, `Q-N__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Ind`, `Q-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Dem`, `Q-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Ind`, `Q-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Int`, `Q-N__Case=Nom\|Gender=Masc\|Number=Plur`, `Q-N__Case=Nom\|Gender=Masc\|Number=Plur\|NumType=Card`, `Q-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Ind`, `Q-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Int`, `Q-N__Case=Nom\|Gender=Masc\|Number=Sing\|NumType=Card`, `Q-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `Q-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Int`, `Q-N__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Ind`, `Q-N__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Int`, `Q-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `Q-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Int`, `Q-N__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `Q-N__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Mid`, `Q-N__Degree=Cmp`, `Q-N__Degree=Sup`, `Q-N__Foreign=Yes`, `Q-N__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Q-N__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Q-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Q-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Q-N__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Q-N__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Q-N__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Q-N__NumType=Card`, `Q-N__VerbForm=Inf\|Voice=Act`, `Q-N__VerbForm=Part\|Voice=Act`, `Q-N__VerbForm=Sup\|Voice=Act`, `QR`, `QR-A`, `QR-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur`, `QR-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `QR-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `QR-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `QR-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `QR-A__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `QR-A__Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `QR-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `QR-A__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `QR-A__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `QR-A__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `QR-A__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `QR-A__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur`, `QR-A__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `QR-A__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `QR-A__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `QR-A__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `QR-A__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur`, `QR-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `QR-A__Degree=Cmp`, `QR-A__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `QR-A__VerbForm=Inf\|Voice=Mid`, `QR-D`, `QR-D__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `QR-D__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `QR-D__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur`, `QR-D__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `QR-D__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `QR-D__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `QR-D__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `QR-D__Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `QR-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `QR-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `QR-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `QR-D__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Ind`, `QR-D__Case=Dat\|Gender=Neut\|Number=Plur\|PronType=Ind`, `QR-D__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur`, `QR-D__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `QR-D__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `QR-D__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `QR-D__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `QR-D__Degree=Cmp`, `QR-D__Foreign=Yes`, `QR-G__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `QR-G__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `QR-G__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `QR-G__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `QR-G__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `QR-G__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `QR-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `QR-G__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `QR-G__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `QR-G__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `QR-N`, `QR-N__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `QR-N__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `QR-N__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `QR-N__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `QR-N__Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `QR-N__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `QR-N__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `QR-N__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `QR-N__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `QR-N__Case=Dat\|Gender=Fem\|Number=Sing\|PronType=Prs`, `QR-N__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `QR-N__Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `QR-N__Case=Gen\|Gender=Neut\|Number=Plur\|PronType=Prs`, `QR-N__Case=Nom`, `QR-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur`, `QR-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `QR-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `QR-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `QR-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `QR-N__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `QR-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `QR-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `QR-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `QR-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Ind`, `QR-N__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Mid`, `QR-N__Degree=Cmp`, `QR-N__Foreign=Yes`, `QR-N__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `QR-N__VerbForm=Inf\|Voice=Act`, `QR__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `QR__Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `QR__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `QR__Degree=Cmp`, `QS-A__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur`, `QS-A__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `QS-A__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `QS-A__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `QS-A__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur`, `QS-A__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing`, `QS-A__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur`, `QS-A__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `QS-A__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur`, `QS-A__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `QS-A__Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `QS-A__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `QS-A__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `QS-A__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing`, `QS-A__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur`, `QS-A__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `QS-A__Degree=Sup`, `QS-D__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `QS-D__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `QS-D__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `QS-D__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `QS-D__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `QS-D__Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur`, `QS-D__Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing`, `QS-D__Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur`, `QS-D__Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `QS-D__Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur`, `QS-D__Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `QS-D__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `QS-D__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `QS-D__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `QS-G__Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `QS-G__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `QS-G__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `QS-G__Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing`, `QS-G__Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur`, `QS-G__Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur`, `QS-G__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `QS-G__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `QS-N`, `QS-N__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `QS-N__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur`, `QS-N__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur`, `QS-N__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `QS-N__Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `QS-N__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `QS-N__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur`, `QS-N__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `QS-N__Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing`, `QS-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `QS-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur`, `QS-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing`, `QS-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur`, `QS-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `QS-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur`, `QS-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `QS-N__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing`, `QS-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Ind`, `QS-N__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Mid`, `QS-N__Degree=Sup`, `QS__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `QS__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `QS__Degree=Sup`, `Q__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Ind`, `Q__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `Q__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `Q__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing`, `Q__Case=Nom\|Gender=Masc\|Number=Sing\|NumType=Card`, `Q__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `RAN__Case=Nom\|Gender=Masc\|Number=Plur\|VerbForm=Part\|Voice=Act`, `RDDI`, `RDDI__Case=Nom\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `RDDI__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `RDDI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `RDDI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `RDDI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `RDDS__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `RDDS__Mood=Sub\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `RDDS__Mood=Sub\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `RDDS__Mood=Sub\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `RDDS__Mood=Sub\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `RDDS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `RDDS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `RDI`, `RDI__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `RDN__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `RDN__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing`, `RDN__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur`, `RDN__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `RDN__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `RDN__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `RDN__Case=Nom\|Gender=Fem\|Number=Plur\|VerbForm=Part\|Voice=Act`, `RDN__Case=Nom\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `RDN__Case=Nom\|Gender=Masc\|Number=Plur\|VerbForm=Part\|Voice=Act`, `RDN__Case=Nom\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `RDN__Case=Nom\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `RDN__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `RDN__VerbForm=Sup\|Voice=Act`, `RDPI__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `RDPI__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `RDPI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `RDPI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `RDPI__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `RDPI__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `RDPI__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `RDPI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `RDPI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `RDPI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `RDPI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `RDPI__Mood=Sub\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `RDPI__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `RDPI__VerbForm=Inf\|Voice=Act`, `RDPS__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `RDPS__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `RDPS__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `RDPS__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `RDPS__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `RDPS__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `RDPS__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `RDPS__Mood=Sub\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `RDPS__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `RDPS__Mood=Sub\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `RDPS__Mood=Sub\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `RDPS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `RD__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `RD__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `RD__VerbForm=Inf\|Voice=Act`, `REP`, `RP`, `RP-2`, `RP-3`, `RPO-D__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Dem`, `RPX`, `RPX-3`, `RPX__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `RPX__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `RPX__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `RP__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `RP__Case=Nom\|Number=Plur\|Person=1\|PronType=Prs`, `RP__Degree=Cmp`, `RP__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `RX`, `SUCH-A`, `SUCH-A__Case=Acc`, `SUCH-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `SUCH-A__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing`, `SUCH-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `SUCH-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `SUCH-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `SUCH-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `SUCH-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `SUCH-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `SUCH-A__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Dem`, `SUCH-A__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Dem`, `SUCH-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Dem`, `SUCH-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Dem`, `SUCH-A__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Dem`, `SUCH-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Dem`, `SUCH-A__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur`, `SUCH-A__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Ind`, `SUCH-A__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `SUCH-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `SUCH-A__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `SUCH-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `SUCH-A__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Dem`, `SUCH-A__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Dem`, `SUCH-A__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Dem`, `SUCH-D__Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing`, `SUCH-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `SUCH-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `SUCH-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `SUCH-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `SUCH-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `SUCH-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `SUCH-D__Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur`, `SUCH-D__Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing`, `SUCH-D__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing`, `SUCH-D__Case=Dat\|Gender=Fem\|Number=Plur\|PronType=Dem`, `SUCH-D__Case=Dat\|Gender=Fem\|Number=Sing\|PronType=Dem`, `SUCH-D__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Dem`, `SUCH-D__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Dem`, `SUCH-D__Case=Dat\|Gender=Neut\|Number=Plur\|PronType=Dem`, `SUCH-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Dem`, `SUCH-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Ind`, `SUCH-D__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `SUCH-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `SUCH-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `SUCH-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `SUCH-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `SUCH-G__Case=Gen\|Gender=Fem\|Number=Plur\|PronType=Dem`, `SUCH-G__Case=Gen\|Gender=Fem\|Number=Sing\|PronType=Dem`, `SUCH-G__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Dem`, `SUCH-G__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Dem`, `SUCH-G__Case=Gen\|Gender=Neut\|Number=Plur\|PronType=Dem`, `SUCH-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Dem`, `SUCH-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `SUCH-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `SUCH-N__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Dem`, `SUCH-N__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Dem`, `SUCH-N__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing`, `SUCH-N__Case=Gen\|Gender=Neut\|Number=Plur\|PronType=Prs`, `SUCH-N__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `SUCH-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `SUCH-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `SUCH-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `SUCH-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `SUCH-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `SUCH-N__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing`, `SUCH-N__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Dem`, `SUCH-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Dem`, `SUCH-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Dem`, `SUCH-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Dem`, `SUCH-N__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Dem`, `SUCH-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Dem`, `SUCH__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `SUCH__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `SUCH__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `SUCH__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `SUCH__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur`, `SUCH__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `SUCH__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `SUCH__Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing`, `SUCH__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur`, `SUCH__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `SUCH__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `SUCH__Foreign=Yes`, `TO`, `VAG`, `VAG-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAG-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VAG-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VAG-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VAG-A__Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur`, `VAG-A__Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAG-A__Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VAG-A__Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAG-A__Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAG-A__Case=Acc\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VAG-A__Case=Acc\|Tense=Pres\|VerbForm=Part`, `VAG-A__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VAG-A__Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAG-A__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAG-A__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VAG-A__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VAG-A__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAG-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VAG-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VAG-A__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAG-A__Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAG-A__VerbForm=Part\|Voice=Act`, `VAG-D__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VAG-D__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VAG-D__Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAG-D__Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAG-D__Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAG-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAG-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAG-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VAG-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAG-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VAG-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VAG-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VAG-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VAG-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VAG-D__Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur`, `VAG-D__Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAG-D__Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VAG-D__Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAG-D__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VAG-D__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VAG-D__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAG-D__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VAG-D__VerbForm=Part\|Voice=Act`, `VAG-G__Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAG-G__Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing`, `VAG-G__Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAG-G__Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VAG-G__Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAG-G__Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VAG-G__Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAG-G__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VAG-G__Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VAG-G__Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAG-N__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAG-N__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VAG-N__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAG-N__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAG-N__Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAG__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VAG__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VAG__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VAG__Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAG__Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VAG__Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAG__Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VAG__Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAG__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `VAG__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAG__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VAG__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VAG__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VAG__Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAG__Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VAG__Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAG__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VAG__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAG__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VAG__Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur`, `VAG__Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAG__Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VAG__Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAG__Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAG__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAG__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VAG__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VAG__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VAG__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur`, `VAG__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAG__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VAG__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAG__Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VAG__Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAG__Case=Nom\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VAG__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VAG__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VAG__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VAG__VerbForm=Part\|Voice=Act`, `VAN`, `VAN-A`, `VAN-A-4__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VAN-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAN-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAN-A__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing`, `VAN-A__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur`, `VAN-A__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `VAN-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `VAN-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAN-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VAN-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAN-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VAN-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAN-A__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur`, `VAN-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VAN-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VAN-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VAN-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VAN-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VAN-A__Case=Acc\|Gender=Fem\|Number=Plur\|NumType=Card`, `VAN-A__Case=Acc\|Gender=Fem\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VAN-A__Case=Acc\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VAN-A__Case=Acc\|Gender=Masc\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VAN-A__Case=Acc\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VAN-A__Case=Acc\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VAN-A__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAN-A__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAN-A__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VAN-A__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAN-A__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VAN-A__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VAN-A__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAN-A__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `VAN-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAN-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAN-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VAN-A__Case=Nom\|Gender=Fem\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VAN-A__Case=Nom\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VAN-A__Case=Nom\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VAN-A__Case=Nom\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VAN-A__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VAN-A__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VAN-A__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VAN-A__VerbForm=Inf\|Voice=Act`, `VAN-A__VerbForm=Sup\|Voice=Act`, `VAN-D`, `VAN-D__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VAN-D__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAN-D__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VAN-D__Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAN-D__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `VAN-D__Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `VAN-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAN-D__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAN-D__Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing`, `VAN-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `VAN-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAN-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VAN-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAN-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VAN-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAN-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VAN-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VAN-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VAN-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VAN-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VAN-D__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VAN-D__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAN-D__Case=Dat\|Gender=Fem\|Number=Sing\|PronType=Ind`, `VAN-D__Case=Dat\|Tense=Past\|VerbForm=Part`, `VAN-D__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur`, `VAN-D__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing`, `VAN-D__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VAN-D__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VAN-D__Mood=Sub\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VAN-D__Mood=Sub\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VAN-D__VerbForm=Part\|Voice=Act`, `VAN-G`, `VAN-G__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAN-G__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VAN-G__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAN-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAN-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAN-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAN-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `VAN-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAN-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VAN-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAN-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VAN-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAN-G__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VAN-G__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VAN-G__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VAN-G__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VAN-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VAN-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAN-N__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAN__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing`, `VAN__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur`, `VAN__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `VAN__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `VAN__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAN__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAN__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VAN__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAN__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VAN__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VAN__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VAN__Case=Acc\|Gender=Fem\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VAN__Case=Acc\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VAN__Case=Acc\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VAN__Case=Acc\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VAN__Case=Acc\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VAN__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAN__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VAN__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAN__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VAN__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAN__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VAN__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VAN__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VAN__Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAN__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAN__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VAN__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VAN__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VAN__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VAN__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAN__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAN__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAN__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `VAN__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing`, `VAN__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur`, `VAN__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `VAN__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `VAN__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VAN__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VAN__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VAN__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VAN__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VAN__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VAN__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VAN__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VAN__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VAN__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VAN__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VAN__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VAN__Case=Nom\|Gender=Fem\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VAN__Case=Nom\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VAN__Case=Nom\|Gender=Masc\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VAN__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `VAN__Case=Nom\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VAN__Case=Nom\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VAN__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VAN__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Mid`, `VAN__Foreign=Yes`, `VAN__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VAN__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VAN__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VAN__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VAN__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VAN__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VAN__Mood=Sub\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VAN__Mood=Sub\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VAN__Tense=Past\|VerbForm=Part`, `VAN__VerbForm=Inf\|Voice=Act`, `VAN__VerbForm=Inf\|Voice=Mid`, `VAN__VerbForm=Sup\|Voice=Act`, `VAN__VerbForm=Sup\|Voice=Mid`, `VB`, `VB-3__VerbForm=Inf\|Voice=Act`, `VBDI`, `VBDI__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VBDI__Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing`, `VBDI__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VBDI__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VBDI__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VBDI__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `VBDI__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur`, `VBDI__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VBDI__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VBDI__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VBDI__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VBDI__Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VBDI__Case=Acc\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VBDI__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Dem`, `VBDI__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Int`, `VBDI__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur`, `VBDI__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VBDI__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VBDI__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VBDI__Case=Dat\|Definite=Def\|Gender=Fem\|Number=Plur`, `VBDI__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VBDI__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VBDI__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VBDI__Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur`, `VBDI__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VBDI__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VBDI__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VBDI__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VBDI__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VBDI__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VBDI__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VBDI__Case=Dat\|Gender=Fem\|Number=Plur\|PronType=Ind`, `VBDI__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Ind`, `VBDI__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Prs`, `VBDI__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VBDI__Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing`, `VBDI__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VBDI__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VBDI__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VBDI__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VBDI__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `VBDI__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VBDI__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VBDI__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VBDI__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VBDI__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VBDI__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `VBDI__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing`, `VBDI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VBDI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VBDI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VBDI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VBDI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VBDI__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing`, `VBDI__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur`, `VBDI__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `VBDI__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur`, `VBDI__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `VBDI__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VBDI__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VBDI__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VBDI__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VBDI__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VBDI__Case=Nom\|Definite=Ind\|Number=Sing`, `VBDI__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VBDI__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Dem`, `VBDI__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Ind`, `VBDI__Case=Nom\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VBDI__Case=Nom\|Gender=Masc\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VBDI__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Dem`, `VBDI__Case=Nom\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VBDI__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Ind`, `VBDI__Case=Nom\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VBDI__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VBDI__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Mid`, `VBDI__Degree=Sup`, `VBDI__Foreign=Yes`, `VBDI__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDI__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDI__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBDI__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDI__Mood=Ind\|Number=Plur\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBDI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBDI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBDI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBDI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDI__Mood=Ind\|Tense=Past`, `VBDI__Mood=Sub\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Sub\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDI__Mood=Sub\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Sub\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBDI__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDI__Mood=Sub\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Sub\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBDI__Mood=Sub\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Sub\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Sub\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBDI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDI__VerbForm=Inf\|Voice=Act`, `VBDI__VerbForm=Inf\|Voice=Mid`, `VBDI__VerbForm=Sup\|Voice=Act`, `VBDI__VerbForm=Sup\|Voice=Mid`, `VBDP__Mood=Sub\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBDS`, `VBDS__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VBDS__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VBDS__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VBDS__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VBDS__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VBDS__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VBDS__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VBDS__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VBDS__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VBDS__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VBDS__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VBDS__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VBDS__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VBDS__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VBDS__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `VBDS__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VBDS__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VBDS__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing`, `VBDS__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur`, `VBDS__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `VBDS__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VBDS__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VBDS__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VBDS__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VBDS__Case=Nom\|Gender=Masc\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VBDS__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Dem`, `VBDS__Case=Nom\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VBDS__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Mid`, `VBDS__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBDS__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDS__Mood=Ind\|Number=Plur\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Ind\|Number=Plur\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBDS__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBDS__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBDS__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDS__Mood=Ind\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Ind\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBDS__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDS__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBDS__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDS__Mood=Sub\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Sub\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBDS__Mood=Sub\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Sub\|Number=Plur\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Sub\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDS__Mood=Sub\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Sub\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBDS__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDS__Mood=Sub\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Sub\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBDS__Mood=Sub\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDS__Mood=Sub\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Sub\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Sub\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBDS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBDS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBDS__NumType=Card`, `VBDS__VerbForm=Inf\|Voice=Act`, `VBDS__VerbForm=Sup\|Voice=Act`, `VBI`, `VBI__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VBI__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VBI__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VBI__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VBI__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VBI__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `VBI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VBI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VBI__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VBI__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VBI__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VBI__Case=Nom\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VBI__Case=Nom\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VBI__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VBI__Foreign=Yes`, `VBI__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBI__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBI__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBI__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBI__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBI__VerbForm=Inf\|Voice=Act`, `VBI__VerbForm=Inf\|Voice=Mid`, `VBI__VerbForm=Sup\|Voice=Act`, `VBN`, `VBN-A__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VBN-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VBN-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VBN-A__Case=Acc\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VBN-A__Case=Acc\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VBN-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VBN-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VBN-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VBN-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VBN-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VBN-D__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBN-G__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VBN__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `VBN__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing`, `VBN__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur`, `VBN__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `VBN__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `VBN__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VBN__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VBN__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VBN__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VBN__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VBN__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `VBN__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VBN__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VBN__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VBN__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VBN__Case=Acc\|Gender=Fem\|Number=Plur\|NumType=Card`, `VBN__Case=Acc\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VBN__Case=Acc\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VBN__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VBN__Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur`, `VBN__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VBN__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `VBN__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur`, `VBN__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `VBN__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `VBN__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VBN__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VBN__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VBN__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VBN__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VBN__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VBN__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VBN__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VBN__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VBN__Case=Nom\|Gender=Fem\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VBN__Case=Nom\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VBN__Case=Nom\|Gender=Masc\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VBN__Case=Nom\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VBN__Case=Nom\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VBN__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VBN__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Mid`, `VBN__Degree=Sup`, `VBN__Foreign=Yes`, `VBN__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBN__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBN__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBN__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBN__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBN__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBN__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBN__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBN__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBN__Mood=Sub\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBN__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBN__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBN__Tense=Past\|VerbForm=Part`, `VBN__VerbForm=Inf\|Voice=Act`, `VBN__VerbForm=Inf\|Voice=Mid`, `VBN__VerbForm=Sup\|Voice=Act`, `VBN__VerbForm=Sup\|Voice=Mid`, `VBPI`, `VBPI__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VBPI__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VBPI__Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing`, `VBPI__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `VBPI__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `VBPI__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VBPI__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VBPI__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VBPI__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VBPI__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VBPI__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `VBPI__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VBPI__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VBPI__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VBPI__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VBPI__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VBPI__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VBPI__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Ind`, `VBPI__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Prs`, `VBPI__Case=Acc\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VBPI__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VBPI__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VBPI__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VBPI__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VBPI__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VBPI__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VBPI__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VBPI__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VBPI__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VBPI__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VBPI__Case=Dat\|Gender=Fem\|Number=Sing\|PronType=Prs`, `VBPI__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Prs`, `VBPI__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Dem`, `VBPI__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Prs`, `VBPI__Case=Dat\|Gender=Neut\|Number=Plur\|PronType=Prs`, `VBPI__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Prs`, `VBPI__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VBPI__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VBPI__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VBPI__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VBPI__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VBPI__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VBPI__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VBPI__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur`, `VBPI__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VBPI__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VBPI__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VBPI__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VBPI__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `VBPI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `VBPI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VBPI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VBPI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VBPI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VBPI__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VBPI__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing`, `VBPI__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `VBPI__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `VBPI__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VBPI__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VBPI__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VBPI__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VBPI__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VBPI__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VBPI__Case=Nom\|Definite=Ind\|Number=Sing`, `VBPI__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur`, `VBPI__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VBPI__Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VBPI__Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VBPI__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Ind`, `VBPI__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Int`, `VBPI__Case=Nom\|Gender=Fem\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VBPI__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Ind`, `VBPI__Case=Nom\|Gender=Masc\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VBPI__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Dem`, `VBPI__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Prs`, `VBPI__Case=Nom\|Gender=Masc\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VBPI__Case=Nom\|Gender=Neut\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VBPI__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `VBPI__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VBPI__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Mid`, `VBPI__Degree=Cmp`, `VBPI__Degree=Sup`, `VBPI__Foreign=Yes`, `VBPI__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPI__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBPI__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPI__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBPI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBPI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBPI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPI__Mood=Ind\|Tense=Pres`, `VBPI__Mood=Sub\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Sub\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Sub\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Sub\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPI__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPI__Mood=Sub\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Sub\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Sub\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Sub\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBPI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPI__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPI__NumType=Card`, `VBPI__VerbForm=Inf\|Voice=Act`, `VBPI__VerbForm=Inf\|Voice=Mid`, `VBPI__VerbForm=Sup\|Voice=Act`, `VBPI__VerbForm=Sup\|Voice=Mid`, `VBPS`, `VBPS__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VBPS__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VBPS__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VBPS__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VBPS__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VBPS__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VBPS__Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VBPS__Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing`, `VBPS__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VBPS__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VBPS__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VBPS__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VBPS__Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur`, `VBPS__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Prs`, `VBPS__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur`, `VBPS__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VBPS__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VBPS__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing`, `VBPS__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VBPS__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VBPS__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing`, `VBPS__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VBPS__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VBPS__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VBPS__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VBPS__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `VBPS__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VBPS__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Mid`, `VBPS__Foreign=Yes`, `VBPS__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Ind\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPS__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Ind\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPS__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Ind\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPS__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPS__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `VBPS__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPS__Mood=Sub\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Sub\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Sub\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPS__Mood=Sub\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Sub\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPS__Mood=Sub\|Number=Sing\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Sub\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Sub\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPS__Mood=Sub\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Sub\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Sub\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VBPS__Mood=Sub\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VBPS__VerbForm=Inf\|Voice=Act`, `VBPS__VerbForm=Inf\|Voice=Mid`, `VBPS__VerbForm=Sup\|Voice=Act`, `VBPS__VerbForm=Sup\|Voice=Mid`, `VB__Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `VB__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VB__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VB__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur`, `VB__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VB__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `VB__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VB__Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `VB__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VB__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VB__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VB__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Prs`, `VB__Case=Acc\|Gender=Fem\|Number=Sing\|VerbForm=Part\|Voice=Act`, `VB__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Dem`, `VB__Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing`, `VB__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VB__Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VB__Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `VB__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `VB__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `VB__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `VB__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `VB__Case=Gen\|Gender=Masc\|Number=Plur\|NumType=Card`, `VB__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing`, `VB__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `VB__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VB__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `VB__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing`, `VB__Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing`, `VB__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `VB__Case=Nom\|Definite=Ind\|Number=Sing`, `VB__Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing`, `VB__Case=Nom\|Gender=Masc\|Number=Plur\|VerbForm=Part\|Voice=Act`, `VB__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Dem`, `VB__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Mid`, `VB__Degree=Sup`, `VB__Foreign=Yes`, `VB__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VB__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `VB__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VB__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VB__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VB__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VB__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VB__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `VB__NumType=Card`, `VB__VerbForm=Inf`, `VB__VerbForm=Inf\|Voice=Act`, `VB__VerbForm=Inf\|Voice=Mid`, `VB__VerbForm=Sup\|Voice=Act`, `VB__VerbForm=Sup\|Voice=Mid`, `VDPI__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VDPI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `VPDI__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `WADJ-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `WADJ-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `WADJ-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Dem`, `WADV`, `WADV-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Int`, `WADV-D`, `WADV-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `WADVP-1`, `WADVP-10__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `WADV__Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur`, `WADV__Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing`, `WADV__Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing`, `WADV__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur`, `WADV__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `WADV__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `WADV__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `WADV__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `WADV__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `WADV__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WADV__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Ind`, `WADV__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Int`, `WADV__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `WADV__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `WADV__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `WADV__Case=Dat\|Gender=Neut\|Number=Plur\|PronType=Dem`, `WADV__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Prs`, `WADV__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur`, `WADV__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Int`, `WADV__Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing`, `WADV__Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing`, `WADV__Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur`, `WADV__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `WADV__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `WADV__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `WADV__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `WADV__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `WADV__Case=Nom\|Gender=Masc\|Number=Sing\|NumType=Card`, `WADV__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WADV__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Dem`, `WADV__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `WADV__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Int`, `WADV__Degree=Cmp`, `WADV__Mood=Imp\|Number=Plur\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `WADV__Mood=Ind\|Number=Plur\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `WADV__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `WADV__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `WD-A`, `WD-A__Case=Acc`, `WD-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `WD-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `WD-A__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `WD-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `WD-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur`, `WD-A__Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing`, `WD-A__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Ind`, `WD-A__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Int`, `WD-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Ind`, `WD-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WD-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Int`, `WD-A__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Ind`, `WD-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Dem`, `WD-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Ind`, `WD-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Int`, `WD-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `WD-A__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WD-A__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Ind`, `WD-A__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Int`, `WD-A__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `WD-A__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Int`, `WD-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `WD-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `WD-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `WD-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `WD-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `WD-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `WD-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `WD-D__Case=Dat\|Gender=Fem\|Number=Sing\|PronType=Ind`, `WD-D__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Ind`, `WD-D__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WD-D__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Int`, `WD-D__Case=Dat\|Gender=Neut\|Number=Plur\|PronType=Ind`, `WD-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Ind`, `WD-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Int`, `WD-D__Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing`, `WD-D__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `WD-D__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Int`, `WD-G`, `WD-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `WD-G__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur`, `WD-G__Case=Gen\|Gender=Fem\|Number=Sing\|PronType=Ind`, `WD-G__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WD-G__Case=Gen\|Gender=Neut\|Number=Plur\|PronType=Ind`, `WD-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Ind`, `WD-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Int`, `WD-N`, `WD-N__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Int`, `WD-N__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Prs`, `WD-N__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WD-N__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Ind`, `WD-N__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Int`, `WD-N__Case=Dat\|Gender=Neut\|Number=Plur\|PronType=Ind`, `WD-N__Case=Nom`, `WD-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur`, `WD-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `WD-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `WD-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `WD-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `WD-N__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Int`, `WD-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Dem`, `WD-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Ind`, `WD-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Int`, `WD-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Dem`, `WD-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Int`, `WD-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WD-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Int`, `WD-N__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Ind`, `WD-N__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Int`, `WD-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `WD-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Int`, `WDD__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WN-D__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WNP-2__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WPRO`, `WPRO-1`, `WPRO-1__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Int`, `WPRO-2`, `WPRO-A`, `WPRO-A__Case=Acc`, `WPRO-A__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `WPRO-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `WPRO-A__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `WPRO-A__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `WPRO-A__Case=Acc\|Gender=Fem\|Number=Plur\|PronType=Ind`, `WPRO-A__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Ind`, `WPRO-A__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Int`, `WPRO-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Ind`, `WPRO-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Int`, `WPRO-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WPRO-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Int`, `WPRO-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Int`, `WPRO-A__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `WPRO-A__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `WPRO-A__Case=Dat\|Definite=Ind\|Number=Sing`, `WPRO-A__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `WPRO-A__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `WPRO-A__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `WPRO-A__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Int`, `WPRO-A__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Ind`, `WPRO-A__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WPRO-A__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Int`, `WPRO-A__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `WPRO-A__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Int`, `WPRO-A__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `WPRO-A__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `WPRO-A__Mood=Ind\|Number=Sing\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `WPRO-A__Mood=Ind\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `WPRO-A__VerbForm=Inf\|Voice=Act`, `WPRO-D`, `WPRO-D__Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing`, `WPRO-D__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Dem`, `WPRO-D__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Int`, `WPRO-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur`, `WPRO-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `WPRO-D__Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `WPRO-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `WPRO-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `WPRO-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur`, `WPRO-D__Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing`, `WPRO-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur`, `WPRO-D__Case=Dat\|Gender=Fem\|Number=Sing\|PronType=Ind`, `WPRO-D__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WPRO-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Ind`, `WPRO-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Int`, `WPRO-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Prs`, `WPRO-D__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WPRO-D__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Int`, `WPRO-D__Case=Nom\|Number=Plur\|Person=1\|PronType=Prs`, `WPRO-D__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `WPRO-D__Mood=Ind\|Number=Plur\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `WPRO-D__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `WPRO-D__Mood=Sub\|Number=Plur\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `WPRO-D__NumType=Card`, `WPRO-G`, `WPRO-G__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `WPRO-G__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Int`, `WPRO-G__Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing`, `WPRO-G__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur`, `WPRO-G__Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing`, `WPRO-G__Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing`, `WPRO-G__Case=Gen\|Gender=Fem\|Number=Sing\|PronType=Ind`, `WPRO-G__Case=Gen\|Gender=Masc\|Number=Plur\|PronType=Int`, `WPRO-G__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WPRO-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Ind`, `WPRO-G__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Int`, `WPRO-G__Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing`, `WPRO-G__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `WPRO-G__Mood=Imp\|Number=Sing\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `WPRO-G__Mood=Ind\|Number=Plur\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `WPRO-G__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `WPRO-N`, `WPRO-N-1__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Int`, `WPRO-N-3`, `WPRO-N__Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing`, `WPRO-N__Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `WPRO-N__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur`, `WPRO-N__Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing`, `WPRO-N__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `WPRO-N__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Ind`, `WPRO-N__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Int`, `WPRO-N__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Ind`, `WPRO-N__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Int`, `WPRO-N__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WPRO-N__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Int`, `WPRO-N__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Prs`, `WPRO-N__Case=Acc\|Gender=Neut\|Number=Plur\|PronType=Dem`, `WPRO-N__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Ind`, `WPRO-N__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Int`, `WPRO-N__Case=Dat\|Gender=Masc\|Number=Plur\|PronType=Ind`, `WPRO-N__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WPRO-N__Case=Dat\|Gender=Neut\|Number=Plur\|PronType=Ind`, `WPRO-N__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Ind`, `WPRO-N__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Int`, `WPRO-N__Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing`, `WPRO-N__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Ind`, `WPRO-N__Case=Gen\|Gender=Neut\|Number=Sing\|PronType=Int`, `WPRO-N__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing`, `WPRO-N__Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing`, `WPRO-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur`, `WPRO-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `WPRO-N__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `WPRO-N__Case=Nom\|Definite=Ind\|Number=Sing`, `WPRO-N__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Int`, `WPRO-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Ind`, `WPRO-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Int`, `WPRO-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Dem`, `WPRO-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Ind`, `WPRO-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Int`, `WPRO-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WPRO-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Int`, `WPRO-N__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Ind`, `WPRO-N__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Int`, `WPRO-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `WPRO-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Int`, `WPRO-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Prs`, `WPRO-N__Foreign=Yes`, `WPRO-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `WPRO-N__Mood=Ind\|Number=Sing\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `WPRO-N__VerbForm=Sup\|Voice=Act`, `WPRO__Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing`, `WPRO__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Ind`, `WPRO__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Int`, `WPRO__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `WPRO__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WPRO__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Int`, `WPRO__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Int`, `WPRO__Mood=Ind\|Number=Sing\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `WQ`, `WQ-A`, `WQ-A__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Ind`, `WQ-A__Case=Acc\|Gender=Masc\|Number=Plur\|PronType=Dem`, `WQ-A__Case=Acc\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WQ-A__Case=Acc\|Gender=Neut\|Number=Sing\|PronType=Ind`, `WQ-A__Case=Nom\|Gender=Fem\|Number=Plur\|PronType=Int`, `WQ-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur`, `WQ-D__Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing`, `WQ-D__Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing`, `WQ-D__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WQ-D__Case=Dat\|Gender=Neut\|Number=Sing\|PronType=Int`, `WQ-D__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `WQ-G__Case=Gen\|Gender=Fem\|Number=Sing\|PronType=Ind`, `WQ-G__Case=Gen\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WQ-N`, `WQ-N__Case=Acc\|Gender=Fem\|Number=Sing\|PronType=Ind`, `WQ-N__Case=Dat\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WQ-N__Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing`, `WQ-N__Case=Nom\|Gender=Fem\|Number=Sing\|PronType=Ind`, `WQ-N__Case=Nom\|Gender=Masc\|Number=Plur\|PronType=Int`, `WQ-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Ind`, `WQ-N__Case=Nom\|Gender=Masc\|Number=Sing\|PronType=Int`, `WQ-N__Case=Nom\|Gender=Neut\|Number=Plur\|PronType=Ind`, `WQ-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `WQ-N__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Int`, `WQ__Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing`, `WQ__Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing`, `WQ__Case=Nom\|Gender=Neut\|Number=Sing\|PronType=Ind`, `WQ__Case=Nom\|Gender=Neut\|Number=Sing\|VerbForm=Part\|Voice=Act`, `WQ__Mood=Ind\|Number=Sing\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act` |
| **`morphologizer`** | `POS=CCONJ`, `POS=ADP`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=ADV`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Prs`, `POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `POS=VERB\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Prs`, `POS=SCONJ`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADV`, `POS=PUNCT`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET`, `Foreign=Yes\|POS=X`, `Case=Dat\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Degree=Cmp\|POS=ADV`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|VerbForm=Inf\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=NOUN`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=PRON`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=X`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `POS=DET`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=VERB\|VerbForm=Sup\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=AUX\|VerbForm=Inf\|Voice=Act`, `POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=PART`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Dat\|Definite=Ind\|Number=Sing\|POS=PROPN`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=AUX`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=NOUN`, `NumType=Card\|POS=DET`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=AUX\|VerbForm=Sup\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=DET`, `POS=VERB`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `NumType=Card\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADV\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=VERB`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PROPN`, `POS=X`, `Case=Gen\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=DET`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=AUX\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADV`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Definite=Ind\|Number=Sing\|POS=X`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=DET`, `Mood=Ind\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|VerbForm=Sup\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=DET`, `NumType=Ord\|POS=NUM`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Number=Sing\|POS=NOUN`, `Case=Dat\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=VERB`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=DET`, `Degree=Sup\|POS=ADV`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=AUX`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=PRON`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=VERB`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADV`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=ADV`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=DET`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Degree=Cmp\|POS=DET`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=DET`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=DET\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Foreign=Yes\|POS=PROPN`, `Foreign=Yes\|POS=ADV`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADP`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=X`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=VERB`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Foreign=Yes\|POS=NOUN`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADV`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Dem`, `NumType=Card\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADV`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `POS=INTJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADV\|PronType=Int`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=VERB`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=VERB`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=AUX`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=VERB`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Foreign=Yes\|POS=PRON`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADV`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Dat\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Mood=Sub\|Number=Sing\|POS=ADJ\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADV`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN\|VerbForm=Part\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADV\|PronType=Dem`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=X`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Prs`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=AUX`, `Mood=Sub\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=ADV`, `POS=NOUN\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=AUX`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=DET`, `Mood=Ind\|Number=Plur\|POS=ADV\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `POS=PRON\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Degree=Cmp\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=PROPN\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADV\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=DET`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=VERB`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB`, `POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Gen\|Definite=Ind\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=AUX`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=PRON\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=VERB`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=X`, `Case=Dat\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PART`, `Mood=Ind\|Number=Plur\|POS=ADJ\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|POS=DET`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=SCONJ\|PronType=Int`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=PRON`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=X\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Foreign=Yes\|POS=VERB`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=VERB`, `Mood=Ind\|Number=Sing\|POS=DET\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Foreign=Yes\|POS=NUM`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=PROPN`, `POS=NOUN\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET`, `Degree=Sup\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=X\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADV`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=SCONJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=PRON`, `Mood=Imp\|Number=Sing\|POS=PRON\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADV\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Prs`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=X`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=AUX`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADP`, `Mood=Ind\|Number=Sing\|POS=ADV\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=VERB`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=CCONJ\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=ADV`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=PRON`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADV`, `POS=PROPN`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=ADP`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=AUX`, `Case=Nom\|Number=Plur\|POS=ADP\|Person=1\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=PRON`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=X`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=SCONJ\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET`, `Foreign=Yes\|POS=AUX`, `Mood=Ind\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=VERB`, `Mood=Ind\|Number=Sing\|POS=ADV\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|PronType=Prs`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=VERB`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=DET\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=AUX`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=SCONJ`, `Mood=Ind\|Number=Sing\|POS=ADP\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=AUX\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Nom\|POS=PRON`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADP\|PronType=Dem`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=VERB`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADV`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=AUX`, `Mood=Ind\|Number=Plur\|POS=ADV\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADV`, `Mood=Ind\|Number=Plur\|POS=PRON\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=DET`, `POS=ADJ\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=AUX`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=CCONJ`, `Mood=Sub\|Number=Sing\|POS=NOUN\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Foreign=Yes\|POS=DET`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=DET`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Number=Sing\|POS=X`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=AUX`, `Mood=Sub\|Number=Sing\|POS=DET\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=VERB`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADV`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=AUX`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Dat\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=ADP`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=ADV\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADP\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADV`, `Mood=Sub\|Number=Sing\|POS=PROPN\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NUM\|PronType=Ind`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=VERB`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=ADV`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADV`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=ADV`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=SCONJ\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN\|VerbForm=Part\|Voice=Act`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=SCONJ\|PronType=Ind`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=ADJ\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=DET`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Dat\|POS=PRON`, `Case=Gen\|Degree=Pos\|POS=ADJ`, `Case=Acc\|POS=NUM`, `Case=Acc\|POS=DET`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=ADV`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=X`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=CCONJ`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=ADV`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB`, `Mood=Ind\|Number=Sing\|POS=ADP\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=AUX`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Number=Plur\|POS=NOUN`, `Case=Gen\|POS=DET`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=X`, `Case=Acc\|Definite=Ind\|Number=Sing\|POS=PROPN`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `POS=DET\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=VERB`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Number=Sing\|POS=NOUN\|Person=1\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=AUX\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=AUX`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADV\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=PRON\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET`, `Degree=Sup\|POS=DET`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Degree=Sup\|POS=VERB`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NUM\|PronType=Ind`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Dat\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=AUX`, `Case=Dat\|Degree=Pos\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Dat\|Number=Sing\|POS=NOUN\|Person=1\|PronType=Prs`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Degree=Cmp\|POS=ADP`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NUM\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=AUX`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=VERB`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Nom\|Number=Sing\|POS=ADV\|Person=2\|PronType=Prs`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADV`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=ADV\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON`, `POS=DET\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=ADV\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=ADV`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADV`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB`, `Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=VERB`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=AUX`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=PRON\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=X`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=NOUN\|VerbForm=Sup\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=X\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Ind\|Number=Sing\|POS=X`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=X`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=ADV`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADV`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=AUX`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON`, `POS=ADV\|VerbForm=Sup\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=DET`, `Mood=Ind\|Number=Sing\|POS=PRON\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=SCONJ\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=VERB`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Sub\|Number=Plur\|POS=NOUN\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=AUX`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=AUX`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=VERB`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADV\|PronType=Int`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=VERB`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Dat\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=X`, `Mood=Imp\|Number=Plur\|POS=ADV\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=NOUN\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=CCONJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=X`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ\|PronType=Ind`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Prs`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET`, `Mood=Ind\|Number=Sing\|POS=CCONJ\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=AUX\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=SCONJ\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Degree=Cmp\|POS=NOUN`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=X`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=X`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=VERB`, `Mood=Imp\|Number=Sing\|POS=ADP\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN\|PronType=Ind`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Dat\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADP\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Number=Sing\|POS=ADV`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADV\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN\|PronType=Dem`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=NOUN\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Dat\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET`, `Mood=Imp\|POS=AUX\|VerbForm=Inf`, `Mood=Ind\|POS=AUX\|Tense=Pres`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=PRON`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=DET`, `Mood=Sub\|Number=Sing\|POS=ADV\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=SCONJ\|PronType=Ind`, `NumType=Card\|POS=X`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=AUX\|VerbForm=Part\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=ADP\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=X`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=AUX\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=AUX`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADV\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|PronType=Ind`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=ADV`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=VERB`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=AUX`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB`, `POS=AUX\|VerbForm=Part\|Voice=Act`, `POS=PROPN\|VerbForm=Inf\|Voice=Act`, `POS=ADV\|VerbForm=Inf\|Voice=Mid`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NUM`, `POS=ADV\|VerbForm=Inf\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Ind`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADV`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=X`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB`, `Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=VERB`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=CCONJ`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=ADV\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADV`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=AUX\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=SCONJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN\|PronType=Ind`, `Case=Nom\|Number=Sing\|POS=PROPN\|Person=2\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=INTJ\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=CCONJ\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADV\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADV\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Definite=Ind\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NOUN`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADV`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN\|PronType=Dem`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=X`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=X`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=X`, `Case=Dat\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADV\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADP`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Acc\|Definite=Ind\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=ADV`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=CCONJ`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=DET`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADV`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB`, `NumType=Ord\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ\|PronType=Ind`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Mood=Sub\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `NumType=Card\|POS=NOUN`, `Case=Nom\|Number=Plur\|POS=NOUN\|Person=1\|PronType=Prs`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADP`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=AUX`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADV`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=ADV`, `NumType=Card\|POS=VERB`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Number=Sing\|POS=DET`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Imp\|Number=Sing\|POS=DET\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PRON\|VerbForm=Sup\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADP`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Mood=Sub\|Number=Plur\|POS=DET\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADV`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADV\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADV`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=X`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADV`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ\|PronType=Ind`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=X\|PronType=Ind`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=X`, `Case=Nom\|Number=Sing\|POS=PROPN\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Dat\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=ADV`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=VERB`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=CCONJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=VERB`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=X`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=ADV`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=ADV`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=NOUN\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=NOUN\|Person=2\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB`, `Foreign=Yes\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PUNCT`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET`, `POS=ADJ\|VerbForm=Sup\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=ADJ\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=CCONJ`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Ind\|Number=Sing\|POS=VERB`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=ADP`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=ADV`, `NumType=Frac\|POS=NOUN`, `Mood=Sub\|Number=Sing\|POS=X\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=PROPN`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=AUX`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=AUX\|PronType=Ind`, `POS=NOUN\|VerbForm=Inf\|Voice=Mid`, `Mood=Sub\|Number=Plur\|POS=NOUN\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ\|PronType=Prs`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NOUN`, `NumType=Card\|POS=PROPN`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=CCONJ`, `Mood=Ind\|Number=Plur\|POS=ADV\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Sing\|POS=NOUN\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=ADP\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|POS=VERB\|Tense=Pres`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=VERB`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=VERB`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Dat\|POS=DET`, `Degree=Pos\|POS=ADV`, `Mood=Sub\|Number=Plur\|POS=NOUN\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=SCONJ\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=VERB`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=PUNCT`, `Case=Nom\|Number=Plur\|POS=PROPN\|Person=2\|PronType=Prs`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=AUX`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADV\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADV`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Dem`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=PRON`, `POS=X\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=X`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=ADP`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NUM\|PronType=Ind`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Dat\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=ADV`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADV`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=AUX`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=PRON`, `Mood=Ind\|Number=Plur\|POS=ADJ\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PRON`, `Foreign=Yes\|POS=ADP`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `POS=AUX\|VerbForm=Inf\|Voice=Mid`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NUM\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=DET\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADV\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=AUX\|PronType=Prs`, `Case=Dat\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=PART`, `POS=PUNCT\|VerbForm=Sup\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=CCONJ\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADV\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=ADV`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADV`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=DET\|Person=1\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=PRON\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=VERB\|VerbForm=Inf`, `Case=Acc\|Degree=Pos\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=ADV`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADJ\|PronType=Ind`, `Case=Nom\|POS=NUM`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADV`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `POS=ADJ\|VerbForm=Sup\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=NOUN\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=VERB`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ\|PronType=Ind`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADV`, `Case=Dat\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Mood=Sub\|Number=Sing\|POS=DET\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=AUX`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADV`, `Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADV`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADV`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADV\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=SCONJ\|PronType=Ind`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=ADP`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NUM`, `Mood=Imp\|Number=Plur\|POS=PUNCT\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADV\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Acc\|Definite=Ind\|Number=Sing\|POS=ADV`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADV`, `Degree=Sup\|POS=ADP`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Sing\|POS=NOUN\|Person=2\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=AUX`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=PROPN`, `NumType=Frac\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADV\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NUM\|PronType=Ind`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=AUX\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=VERB`, `Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=ADP\|PronType=Ind`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=PRON`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=PRON`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADP`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN\|PronType=Ind`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Definite=Ind\|Number=Sing\|POS=X`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=X`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET`, `Mood=Imp\|Number=Sing\|POS=ADJ\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=CCONJ`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=ADJ\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=AUX`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=AUX`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADV`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=AUX`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=CCONJ\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Plur\|POS=ADJ\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON`, `Mood=Ind\|Number=Sing\|POS=SCONJ\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADV\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=AUX\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=SCONJ\|PronType=Int`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Sub\|Number=Plur\|POS=DET\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=ADJ\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Sing\|POS=PROPN\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|PronType=Dem`, `Mood=Sub\|Number=Plur\|POS=ADJ\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|POS=PRON`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Mood=Sub\|Number=Sing\|POS=PROPN\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=PRON\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=NUM\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|POS=AUX\|Tense=Past`, `Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=PRON`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADV`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADP`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PART\|PronType=Ind`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=AUX`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=ADV\|PronType=Ind`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=X`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=VERB`, `POS=DET\|VerbForm=Sup\|Voice=Act`, `Degree=Cmp\|POS=VERB`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=AUX`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=X`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=PRON`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADV\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADV`, `Case=Dat\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=AUX`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=SCONJ\|PronType=Ind`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Dat\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=X`, `Mood=Sub\|Number=Sing\|POS=PROPN\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=ADJ\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADP`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=ADV\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=CCONJ\|PronType=Ind`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADV`, `POS=ADP\|VerbForm=Sup\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PROPN\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=ADV\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Dat\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=NOUN\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=ADV\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NOUN`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=VERB`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Dat\|Definite=Ind\|Number=Sing\|POS=ADV`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=X`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET`, `POS=INTJ\|VerbForm=Sup\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADV`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN\|PronType=Prs`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=ADV`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=NOUN\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=ADV`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Imp\|Number=Sing\|POS=NOUN\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=ADV\|VerbForm=Sup\|Voice=Mid`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Ind\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB`, `Mood=Ind\|Number=Sing\|POS=PRON\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=PROPN\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=ADV\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADP`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NUM`, `NumType=Card\|POS=PRON`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADV\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=ADV\|VerbForm=Part\|Voice=Act`, `POS=ADJ\|VerbForm=Part\|Voice=Act`, `Mood=Imp\|POS=VERB\|VerbForm=Inf`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=AUX`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=PRON`, `Case=Nom\|Degree=Pos\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=ADV`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=AUX`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADP\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADP`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADV\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Number=Sing\|POS=PRON`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=CCONJ\|VerbForm=Part\|Voice=Act`, `Degree=Pos\|POS=ADJ`, `Case=Acc\|POS=VERB\|Tense=Pres\|VerbForm=Part`, `Mood=Ind\|POS=VERB\|Tense=Past`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=CCONJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN\|PronType=Dem`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=PRON\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=PRON`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|PronType=Dem`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=PROPN`, `NumType=Ord\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=X`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN\|VerbForm=Part\|Voice=Act`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADP`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|PronType=Dem`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=VERB`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=VERB`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADV\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=ADV\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `NumType=Card\|POS=PUNCT`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=X`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=SCONJ\|PronType=Int`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=AUX`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADV\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Foreign=Yes\|POS=INTJ`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=X`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Number=Sing\|POS=PRON`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=X`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=AUX`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=ADV`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=X\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON`, `Mood=Ind\|Number=Plur\|POS=PROPN\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN\|PronType=Int`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=ADV`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=AUX`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=PRON`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADV`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=CCONJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=PRON`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=AUX`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=SCONJ\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADV\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=SCONJ\|PronType=Ind`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Dat\|Number=Sing\|POS=DET\|Person=2\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=PRON`, `POS=ADP\|VerbForm=Inf\|Voice=Act`, `Case=Gen\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADV`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADV\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=ADV\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=VERB`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=PRON`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Plur\|POS=ADP\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=SCONJ\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=AUX\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=VERB`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=VERB`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADV\|PronType=Int`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Mood=Sub\|Number=Sing\|POS=X\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=AUX`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=CCONJ\|PronType=Ind`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN\|PronType=Prs`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=ADV`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADV`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Degree=Cmp\|POS=PRON`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=AUX`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=VERB`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=SCONJ`, `Case=Nom\|Number=Plur\|POS=NOUN\|Person=2\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=NUM\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=CCONJ\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=X`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=X`, `Mood=Sub\|Number=Plur\|POS=PRON\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=VERB`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN\|PronType=Prs`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=AUX`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Gen\|Number=Sing\|POS=PUNCT\|Person=2\|PronType=Prs`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=ADV`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADV`, `Case=Dat\|Definite=Ind\|Number=Sing\|POS=NUM`, `Degree=Sup\|POS=X`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=INTJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=AUX`, `Mood=Imp\|Number=Plur\|POS=NOUN\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=CCONJ`, `Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Gen\|Number=Sing\|POS=PUNCT\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|PronType=Int`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=X`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=AUX`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=X`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Gen\|Definite=Ind\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=X`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=AUX`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADV\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Number=Sing\|POS=PRON`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=CCONJ`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=VERB`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADV`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=ADP`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADP`, `Case=Nom\|Definite=Ind\|Number=Sing\|POS=INTJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADV`, `Mood=Sub\|Number=Sing\|POS=PRON\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=DET\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=VERB`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADV\|PronType=Dem`, `Case=Dat\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=X`, `Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADV`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=ADP`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=PRON`, `Mood=Ind\|Number=Plur\|POS=DET\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NUM\|PronType=Int`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Mood=Sub\|Number=Sing\|POS=ADJ\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `NumType=Frac\|POS=PUNCT`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADV`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADV\|PronType=Dem`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=DET\|VerbForm=Inf\|Voice=Mid`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=X`, `Mood=Ind\|Number=Sing\|POS=PRON\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=ADJ\|VerbForm=Inf\|Voice=Mid`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PART`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=ADV\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=PRON\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=VERB`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=SCONJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=ADJ\|PronType=Ind`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADV`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=PRON`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=X`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADV`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=X\|PronType=Prs`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=AUX`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADP`, `Case=Dat\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=SCONJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=ADV\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Mood=Imp\|Number=Sing\|POS=DET\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=SCONJ\|PronType=Int`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADV\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADV`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=VERB`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN\|PronType=Prs`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADV`, `Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=ADV\|Tense=Past`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=X`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADV\|PronType=Ind`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Plur\|POS=DET\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PROPN\|VerbForm=Sup\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=SCONJ`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=DET`, `Mood=Ind\|Number=Sing\|POS=PROPN\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=AUX`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADV\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=SCONJ\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADP`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=PRON`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Mood=Imp\|Number=Sing\|POS=SCONJ\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADV\|PronType=Ind`, `Mood=Sub\|Number=Plur\|POS=ADV\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=AUX\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=ADV`, `Mood=Sub\|Number=Plur\|POS=ADV\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADV\|PronType=Ind`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN\|PronType=Dem`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADV\|PronType=Ind`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=VERB`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADP`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Nom\|Definite=Ind\|Number=Sing\|POS=ADP`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADV`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=VERB`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADV`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=ADV`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=SCONJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=AUX\|PronType=Dem`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|POS=NUM`, `NumType=Card\|POS=ADV`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADP`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=AUX`, `Case=Acc\|Definite=Ind\|Number=Plur\|POS=NOUN`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=SCONJ\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Dat\|POS=NUM`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADP`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=SCONJ`, `Foreign=Yes\|POS=CCONJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=AUX\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=ADJ\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADP`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADP\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|PronType=Int`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=X\|PronType=Ind`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=X`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=AUX`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|PronType=Dem`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=PRON`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADV\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=ADV\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=DET\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=ADP\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=ADV`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADP`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=AUX\|PronType=Prs`, `Mood=Sub\|POS=AUX\|Tense=Past`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|PronType=Ind`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=X`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `DEP_UAS` | 82.89 |
| `DEP_LAS` | 77.71 |
| `SENTS_P` | 96.97 |
| `SENTS_R` | 98.50 |
| `SENTS_F` | 97.73 |
| `LEMMA_ACC` | 94.86 |
| `TAG_ACC` | 84.84 |
| `POS_ACC` | 96.29 |
| `MORPH_ACC` | 90.12 |
| `TRANSFORMER_LOSS` | 2803740.93 |
| `PARSER_LOSS` | 534940.15 |
| `TRAINABLE_LEMMATIZER_LOSS` | 294717.33 |
| `TAGGER_LOSS` | 890478.23 |
| `MORPHOLOGIZER_LOSS` | 426176.75 |
|
CocoyGames9/JBrown
|
CocoyGames9
| 2023-12-23T03:50:37Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2023-12-23T03:47:15Z |
---
license: other
license_name: icescream4
license_link: LICENSE
---
|
gibhug/llama2-7b-chicks_v2-0
|
gibhug
| 2023-12-23T03:33:22Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain",
"conversational",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-23T02:33:39Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
liyoo/IntegratedModel_PairClassification
|
liyoo
| 2023-12-23T03:33:19Z | 0 | 0 | null |
[
"code",
"text-classification",
"zh",
"region:us"
] |
text-classification
| 2023-12-23T03:30:08Z |
---
language:
- zh
pipeline_tag: text-classification
tags:
- code
---
|
RavingRabbit/eformConsult
|
RavingRabbit
| 2023-12-23T03:18:48Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-23T01:36:29Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: eformConsult
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# eformConsult
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7844
- Matthews Correlation: 0.5466
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.6657021403235714e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5213 | 1.0 | 535 | 0.4858 | 0.4326 |
| 0.3536 | 2.0 | 1070 | 0.4809 | 0.5083 |
| 0.2508 | 3.0 | 1605 | 0.5551 | 0.5268 |
| 0.183 | 4.0 | 2140 | 0.7043 | 0.5280 |
| 0.154 | 5.0 | 2675 | 0.7844 | 0.5466 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
iamandrewliao/lunarlanding-ppo
|
iamandrewliao
| 2023-12-23T03:17:05Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-23T03:16:45Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 260.85 +/- 21.43
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
tunyu/HW1223_01
|
tunyu
| 2023-12-23T03:05:39Z | 10 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-23T01:37:59Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: HW1223_01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HW1223_01
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7204
- Matthews Correlation: 0.5641
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5161 | 1.0 | 535 | 0.4596 | 0.4449 |
| 0.3391 | 2.0 | 1070 | 0.4532 | 0.5385 |
| 0.2312 | 3.0 | 1605 | 0.6441 | 0.5136 |
| 0.1591 | 4.0 | 2140 | 0.7204 | 0.5641 |
| 0.1205 | 5.0 | 2675 | 0.8336 | 0.5444 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
GordonMcGregor/stable-diffusion-xl-base-1.0-lora-TOK-Gordon2
|
GordonMcGregor
| 2023-12-23T03:03:24Z | 1 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-22T22:52:22Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: 'A photo of TOK man in the rain'
output:
url:
"image_0.png"
- text: 'A photo of TOK man in the rain'
output:
url:
"image_1.png"
- text: 'A photo of TOK man in the rain'
output:
url:
"image_2.png"
- text: 'A photo of TOK man in the rain'
output:
url:
"image_3.png"
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: A photo of TOK man
license: openrail++
---
# SDXL LoRA DreamBooth - GordonMcGregor/stable-diffusion-xl-base-1.0-lora-TOK-Gordon2
<Gallery />
## Model description
These are GordonMcGregor/stable-diffusion-xl-base-1.0-lora-TOK-Gordon2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use A photo of TOK man to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](GordonMcGregor/stable-diffusion-xl-base-1.0-lora-TOK-Gordon2/tree/main) them in the Files & versions tab.
|
CCChenRyan/LLM_T1
|
CCChenRyan
| 2023-12-23T02:58:24Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-23T01:49:21Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: LLM_T1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# LLM_T1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.8637
- eval_matthews_correlation: 0.5380
- eval_runtime: 0.792
- eval_samples_per_second: 1316.988
- eval_steps_per_second: 83.338
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
chtsai2104/llmhw01
|
chtsai2104
| 2023-12-23T02:53:10Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-23T01:35:56Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: llmhw01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llmhw01
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6108
- Matthews Correlation: 0.5102
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.2536324688169738e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 23
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| No log | 1.0 | 268 | 0.4708 | 0.4553 |
| 0.4459 | 2.0 | 536 | 0.4836 | 0.4888 |
| 0.4459 | 3.0 | 804 | 0.5368 | 0.5123 |
| 0.2266 | 4.0 | 1072 | 0.6108 | 0.5102 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
flyingswan3000/HW01
|
flyingswan3000
| 2023-12-23T02:43:02Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-23T01:35:45Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: HW01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HW01
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5688
- Matthews Correlation: 0.5215
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.0568164357979843e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| No log | 1.0 | 134 | 0.4710 | 0.4269 |
| No log | 2.0 | 268 | 0.4810 | 0.5091 |
| No log | 3.0 | 402 | 0.5688 | 0.5215 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
jkloip/cm124057-01
|
jkloip
| 2023-12-23T02:40:00Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-23T01:37:48Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: cm124057-01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cm124057-01
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8311
- Matthews Correlation: 0.5373
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5197 | 1.0 | 535 | 0.4535 | 0.4636 |
| 0.3446 | 2.0 | 1070 | 0.4631 | 0.5118 |
| 0.2344 | 3.0 | 1605 | 0.6146 | 0.5314 |
| 0.1653 | 4.0 | 2140 | 0.7437 | 0.5000 |
| 0.1263 | 5.0 | 2675 | 0.8311 | 0.5373 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
Qian-Wu/AIA_HW01
|
Qian-Wu
| 2023-12-23T02:37:27Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-23T01:35:56Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: AIA_HW01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# AIA_HW01
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1716
- Matthews Correlation: 0.5440
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.1397 | 1.0 | 535 | 0.7584 | 0.4797 |
| 0.1084 | 2.0 | 1070 | 1.0523 | 0.4971 |
| 0.0773 | 3.0 | 1605 | 1.1079 | 0.5301 |
| 0.0561 | 4.0 | 2140 | 1.2460 | 0.5174 |
| 0.064 | 5.0 | 2675 | 1.1716 | 0.5440 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
RandyTsai/HW001
|
RandyTsai
| 2023-12-23T02:36:02Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-23T01:40:12Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: HW001
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HW001
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9586
- Matthews Correlation: 0.5403
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.1355 | 1.0 | 535 | 0.7101 | 0.5191 |
| 0.099 | 2.0 | 1070 | 0.9586 | 0.5403 |
| 0.0766 | 3.0 | 1605 | 1.1402 | 0.5198 |
| 0.053 | 4.0 | 2140 | 1.2587 | 0.5321 |
| 0.041 | 5.0 | 2675 | 1.2867 | 0.5286 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
ThuyNT03/KLTN_COQE_viT5_total_ASOPL_v2
|
ThuyNT03
| 2023-12-23T02:33:57Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"base_model:finetune:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-23T01:47:11Z |
---
license: mit
base_model: VietAI/vit5-large
tags:
- generated_from_trainer
model-index:
- name: KLTN_COQE_viT5_total_ASOPL_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# KLTN_COQE_viT5_total_ASOPL_v2
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
|
ThuyNT03/KLTN_COQE_viT5_total_PSOAL_v2
|
ThuyNT03
| 2023-12-23T02:32:08Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"base_model:finetune:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-23T01:46:16Z |
---
license: mit
base_model: VietAI/vit5-large
tags:
- generated_from_trainer
model-index:
- name: KLTN_COQE_viT5_total_PSOAL_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# KLTN_COQE_viT5_total_PSOAL_v2
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
|
tranquocthanh/a2c-PandaReachDense-v3
|
tranquocthanh
| 2023-12-23T02:28:36Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-23T02:24:04Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -1.32 +/- 2.03
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
dainis-boumber/mistral-codaspy
|
dainis-boumber
| 2023-12-23T02:26:20Z | 4 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2023-12-22T07:38:44Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: mistral-codaspy
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-codaspy
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7694 | 0.22 | 50 | 0.0199 |
| 0.0106 | 0.44 | 100 | 0.0061 |
| 0.0036 | 0.66 | 150 | 0.0018 |
| 0.0011 | 0.88 | 200 | 0.0006 |
| 0.0005 | 1.11 | 250 | 0.0004 |
| 0.0003 | 1.33 | 300 | 0.0003 |
| 0.0003 | 1.55 | 350 | 0.0002 |
| 0.0002 | 1.77 | 400 | 0.0002 |
| 0.0002 | 1.99 | 450 | 0.0002 |
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Bobblack225/intstep
|
Bobblack225
| 2023-12-23T02:25:50Z | 15 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"region:us"
] |
text-generation
| 2023-12-23T01:25:37Z |
---
license: mit
---
Intentionally left vague. This is a storage place for the actual repo that comes later.
Do not attempt to use this model yet.
Prompt template:
```<s> [|User|] What is the derivitive of f(x)=x? </s>[|Assistant|]```
|
lukexue/HW01
|
lukexue
| 2023-12-23T02:19:07Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-23T01:36:19Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: HW01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HW01
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9262
- Matthews Correlation: 0.4620
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5631 | 1.0 | 535 | 0.5297 | 0.3290 |
| 0.397 | 2.0 | 1070 | 0.5553 | 0.4153 |
| 0.2575 | 3.0 | 1605 | 0.7078 | 0.4116 |
| 0.1639 | 4.0 | 2140 | 0.9262 | 0.4620 |
| 0.0989 | 5.0 | 2675 | 1.1961 | 0.4610 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
LarryTW/llm_NLP
|
LarryTW
| 2023-12-23T02:19:00Z | 11 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-22T15:57:06Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: llm_NLP
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llm_NLP
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7458
- Matthews Correlation: 0.4922
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8.53919308272751e-06
- train_batch_size: 8
- eval_batch_size: 16
- seed: 13
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5143 | 1.0 | 1069 | 0.4927 | 0.4359 |
| 0.3963 | 2.0 | 2138 | 0.4984 | 0.4814 |
| 0.3216 | 3.0 | 3207 | 0.6548 | 0.4980 |
| 0.2629 | 4.0 | 4276 | 0.7458 | 0.4922 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
OoMandyoO/HW01
|
OoMandyoO
| 2023-12-23T02:18:55Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-23T01:36:16Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: HW01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HW01
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7701
- Matthews Correlation: 0.5277
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5199 | 1.0 | 535 | 0.4559 | 0.4587 |
| 0.3394 | 2.0 | 1070 | 0.4532 | 0.5201 |
| 0.2355 | 3.0 | 1605 | 0.6383 | 0.4885 |
| 0.1649 | 4.0 | 2140 | 0.7701 | 0.5277 |
| 0.1207 | 5.0 | 2675 | 0.8650 | 0.5242 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
AcEzKeViNz/HW01
|
AcEzKeViNz
| 2023-12-23T02:18:52Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-23T01:35:44Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: HW01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HW01
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7639
- Matthews Correlation: 0.5142
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5222 | 1.0 | 535 | 0.4592 | 0.4547 |
| 0.3491 | 2.0 | 1070 | 0.4676 | 0.5035 |
| 0.2404 | 3.0 | 1605 | 0.6595 | 0.5033 |
| 0.1643 | 4.0 | 2140 | 0.7639 | 0.5142 |
| 0.1305 | 5.0 | 2675 | 0.8609 | 0.5089 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
hcyang0401/1121223_HW01
|
hcyang0401
| 2023-12-23T02:17:12Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-23T01:35:45Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: 1121223_HW01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 1121223_HW01
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7817
- Matthews Correlation: 0.5142
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.521 | 1.0 | 535 | 0.4591 | 0.4340 |
| 0.347 | 2.0 | 1070 | 0.4669 | 0.5123 |
| 0.2383 | 3.0 | 1605 | 0.6635 | 0.4940 |
| 0.1613 | 4.0 | 2140 | 0.7817 | 0.5142 |
| 0.1231 | 5.0 | 2675 | 0.8819 | 0.5136 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
Chungyeh/LLM_B_HW001
|
Chungyeh
| 2023-12-23T02:11:20Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-23T01:37:26Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: LLM_B_HW001
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# LLM_B_HW001
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1329
- Matthews Correlation: 0.5260
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.1384 | 1.0 | 535 | 0.7629 | 0.5118 |
| 0.1026 | 2.0 | 1070 | 1.0638 | 0.5058 |
| 0.0661 | 3.0 | 1605 | 1.1329 | 0.5260 |
| 0.0492 | 4.0 | 2140 | 1.2943 | 0.5174 |
| 0.0394 | 5.0 | 2675 | 1.3559 | 0.5060 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
ecyor/hw1
|
ecyor
| 2023-12-23T01:59:03Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-23T01:36:15Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: hw1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hw1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7215
- Matthews Correlation: 0.5423
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5241 | 1.0 | 535 | 0.4491 | 0.4708 |
| 0.353 | 2.0 | 1070 | 0.4616 | 0.5311 |
| 0.244 | 3.0 | 1605 | 0.6495 | 0.4986 |
| 0.171 | 4.0 | 2140 | 0.7215 | 0.5423 |
| 0.132 | 5.0 | 2675 | 0.8294 | 0.5199 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
lilyftyunjin/nmixx1
|
lilyftyunjin
| 2023-12-23T01:54:48Z | 0 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:latent-consistency/lcm-lora-sdxl",
"base_model:adapter:latent-consistency/lcm-lora-sdxl",
"license:unknown",
"region:us"
] |
text-to-image
| 2023-12-23T01:53:15Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/IMG_8503.jpeg
base_model: latent-consistency/lcm-lora-sdxl
instance_prompt: null
license: unknown
---
# nmixx
<Gallery />
## Download model
[Download](/lilyftyunjin/nmixx1/tree/main) them in the Files & versions tab.
|
Ethan615/try
|
Ethan615
| 2023-12-23T01:45:46Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-22T06:37:57Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: try
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# try
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5544
- Matthews Correlation: 0.5009
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.546889870762945e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| No log | 1.0 | 268 | 0.4784 | 0.4982 |
| 0.4708 | 2.0 | 536 | 0.4544 | 0.5011 |
| 0.4708 | 3.0 | 804 | 0.5128 | 0.5070 |
| 0.2823 | 4.0 | 1072 | 0.5544 | 0.5009 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
ntc-ai/SDXL-LoRA-slider.view-from-behind
|
ntc-ai
| 2023-12-23T01:42:32Z | 54 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] |
text-to-image
| 2023-12-23T01:42:29Z |
---
language:
- en
thumbnail: "images/evaluate/view from behind.../view from behind_17_3.0.png"
widget:
- text: view from behind
output:
url: images/view from behind_17_3.0.png
- text: view from behind
output:
url: images/view from behind_19_3.0.png
- text: view from behind
output:
url: images/view from behind_20_3.0.png
- text: view from behind
output:
url: images/view from behind_21_3.0.png
- text: view from behind
output:
url: images/view from behind_22_3.0.png
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
license: "mit"
inference: false
instance_prompt: "view from behind"
base_model: "stabilityai/stable-diffusion-xl-base-1.0"
---
# ntcai.xyz slider - view from behind (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/view from behind_17_-3.0.png" width=256 height=256 /> | <img src="images/view from behind_17_0.0.png" width=256 height=256 /> | <img src="images/view from behind_17_3.0.png" width=256 height=256 /> |
| <img src="images/view from behind_19_-3.0.png" width=256 height=256 /> | <img src="images/view from behind_19_0.0.png" width=256 height=256 /> | <img src="images/view from behind_19_3.0.png" width=256 height=256 /> |
| <img src="images/view from behind_20_-3.0.png" width=256 height=256 /> | <img src="images/view from behind_20_0.0.png" width=256 height=256 /> | <img src="images/view from behind_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
view from behind
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.view-from-behind', weight_name='view from behind.safetensors', adapter_name="view from behind")
# Activate the LoRA
pipe.set_adapters(["view from behind"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, view from behind"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 550+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
AhmedTaha012/pargraphs_titlesV1.0
|
AhmedTaha012
| 2023-12-23T01:37:25Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-23T01:37:01Z |
---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pargraphs_titlesV1.0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pargraphs_titlesV1.0
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2697
- Rouge1: 68.705
- Rouge2: 54.5204
- Rougel: 67.7709
- Rougelsum: 67.7942
- Gen Len: 1401169535.5
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:------------:|
| 0.347 | 0.44 | 100 | 0.2634 | 65.1158 | 48.282 | 63.708 | 63.7424 | 1401169536.0 |
| 0.2412 | 0.88 | 200 | 0.3167 | 66.0958 | 50.4705 | 65.1041 | 65.1412 | 1401169536.0 |
| 0.2069 | 1.32 | 300 | 0.2357 | 68.6707 | 53.5945 | 67.3654 | 67.371 | 1401169536.0 |
| 0.1825 | 1.76 | 400 | 0.3932 | 65.7022 | 51.08 | 64.9927 | 65.0322 | 1401169536.0 |
| 0.1643 | 2.2 | 500 | 0.2223 | 69.132 | 54.5176 | 67.881 | 67.8987 | 1401169535.0 |
| 0.1715 | 2.64 | 600 | 0.2227 | 69.2258 | 54.2845 | 68.0181 | 68.0404 | 1401169535.5 |
| 0.1571 | 3.08 | 700 | 0.2707 | 68.9908 | 54.7777 | 68.1279 | 68.151 | 1401169536.0 |
| 0.1584 | 3.52 | 800 | 0.2193 | 70.9126 | 56.4866 | 69.6718 | 69.6687 | 1401169535.5 |
| 0.1565 | 3.96 | 900 | 0.3482 | 68.6691 | 54.8446 | 67.796 | 67.8541 | 1401169536.0 |
| 0.155 | 4.4 | 1000 | 0.2694 | 69.1457 | 55.1123 | 68.2207 | 68.2543 | 1401169536.0 |
| 0.1586 | 4.84 | 1100 | 0.2697 | 68.705 | 54.5204 | 67.7709 | 67.7942 | 1401169535.5 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
|
nakulz/ludwig-Fine-Tune-Mistral-7b
|
nakulz
| 2023-12-23T01:34:51Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:alexsherstinsky/Mistral-7B-v0.1-sharded",
"base_model:adapter:alexsherstinsky/Mistral-7B-v0.1-sharded",
"region:us"
] | null | 2023-12-23T01:34:49Z |
---
library_name: peft
base_model: alexsherstinsky/Mistral-7B-v0.1-sharded
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
kevinmcmahon/corgy_dog_LoRA
|
kevinmcmahon
| 2023-12-23T01:34:35Z | 1 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-22T22:38:06Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of TOK dog
license: openrail++
---
# SDXL LoRA DreamBooth - kevinmcmahon/corgy_dog_LoRA
<Gallery />
## Model description
These are kevinmcmahon/corgy_dog_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of TOK dog to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](kevinmcmahon/corgy_dog_LoRA/tree/main) them in the Files & versions tab.
|
LarryAIDraw/mythra-xb-richy-v1
|
LarryAIDraw
| 2023-12-23T01:03:42Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-23T01:01:31Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/240311/mythrahikari-xenoblade-chronicles-2-lora-or-3-outfits-swimsuit-massive-melee-and-default
|
LarryAIDraw/pyra-xb-richy-v1
|
LarryAIDraw
| 2023-12-23T01:03:31Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-23T01:01:08Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/240310/pyrahomura-xenoblade-chronicles-2-lora-or-2-outfits-swimsuit-and-default
|
LarryAIDraw/NSLiechtensteinHetalia
|
LarryAIDraw
| 2023-12-23T01:03:18Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-23T01:00:44Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/239881/liechtenstein-or-hetalia-lora
|
LarryAIDraw/emilybrownv2
|
LarryAIDraw
| 2023-12-23T01:03:01Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-23T01:00:20Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/158475/emily-brown-or-my-unique-skills-makes-me-op-even-at-level-1
|
LarryAIDraw/hoshimiyamukuro_scarxzys
|
LarryAIDraw
| 2023-12-23T00:58:55Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-23T00:55:05Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/240803/mukuro-hoshimiya-or-date-a-live
|
LarryAIDraw/ashuna-virginrd-01
|
LarryAIDraw
| 2023-12-23T00:58:43Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-23T00:54:42Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/240655/ashuna-shokei-shoujo-no-virgin-road
|
NouRed/Med-Mistral-7B-QLoRa
|
NouRed
| 2023-12-23T00:56:36Z | 3 | 0 |
peft
|
[
"peft",
"safetensors",
"text-generation-inference",
"text-generation",
"en",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-12-23T00:51:12Z |
---
library_name: peft
base_model: mistralai/Mistral-7B-Instruct-v0.2
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- text-generation-inference
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
c-wang/drl-course-unit5-pyramid
|
c-wang
| 2023-12-23T00:35:59Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-12-23T00:35:55Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: c-wang/drl-course-unit5-pyramid
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
PritK99/ppo-LunarLander-v2
|
PritK99
| 2023-12-23T00:35:09Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-17T12:01:55Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 245.73 +/- 19.96
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Shepherd17/ppo-SpaceInvadersNoFrameskip-v4
|
Shepherd17
| 2023-12-23T00:11:03Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-23T00:10:17Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 294.25 +/- 16.95
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
arya555/email_classification
|
arya555
| 2023-12-23T00:07:29Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-17T18:43:20Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: email_classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# email_classification
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5632
- Accuracy: 0.9038
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2987 | 1.0 | 121 | 1.1291 | 0.6058 |
| 0.6602 | 2.0 | 242 | 0.8249 | 0.75 |
| 0.4545 | 3.0 | 363 | 0.4199 | 0.8942 |
| 0.2338 | 4.0 | 484 | 0.5669 | 0.9038 |
| 0.083 | 5.0 | 605 | 0.5672 | 0.9038 |
| 0.0057 | 6.0 | 726 | 0.5632 | 0.9038 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
Maxlin12/gpt2-finetuned-wikitext2
|
Maxlin12
| 2023-12-23T00:01:46Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-22T21:02:47Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_keras_callback
model-index:
- name: Maxlin12/gpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Maxlin12/gpt2-finetuned-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 6.4917
- Validation Loss: 6.3408
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 7.3091 | 6.7586 | 0 |
| 6.4917 | 6.3408 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
c-wang/drl-course-unit5-Snowball
|
c-wang
| 2023-12-22T23:47:55Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-12-22T23:47:50Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: c-wang/drl-course-unit5-Snowball
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Alena-Poluboyarinova/bert-base-cased-finetuned-wikitext2
|
Alena-Poluboyarinova
| 2023-12-22T23:37:53Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bert",
"fill-mask",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-12-22T23:15:51Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: Alena-Poluboyarinova/bert-base-cased-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Alena-Poluboyarinova/bert-base-cased-finetuned-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 6.9592
- Validation Loss: 6.8829
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 7.4204 | 7.0579 | 0 |
| 6.9592 | 6.8829 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
ahmedabdelwahed/sft-base-8-epochs
|
ahmedabdelwahed
| 2023-12-22T23:33:35Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/mt5-base",
"base_model:adapter:google/mt5-base",
"region:us"
] | null | 2023-12-22T20:08:37Z |
---
library_name: peft
base_model: google/mt5-base
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
winehertz/bert-base-cased-finetuned-wikitext2
|
winehertz
| 2023-12-22T23:27:54Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bert",
"fill-mask",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-12-22T23:08:44Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: winehertz/bert-base-cased-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# winehertz/bert-base-cased-finetuned-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 6.9693
- Validation Loss: 6.8815
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 7.4361 | 7.0591 | 0 |
| 6.9693 | 6.8815 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
JLei/climate_fever_roberta-base-fact-checking
|
JLei
| 2023-12-22T23:25:27Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text-classification",
"en",
"dataset:Jasontth/climate_fever_plus",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-22T05:32:35Z |
---
license: mit
datasets:
- Jasontth/climate_fever_plus
language:
- en
pipeline_tag: text-classification
---
|
luckykittty/bert-base-cased-finetuned-wikitext2
|
luckykittty
| 2023-12-22T23:25:19Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bert",
"fill-mask",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-12-22T23:04:23Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: luckykittty/bert-base-cased-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# luckykittty/bert-base-cased-finetuned-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 6.9588
- Validation Loss: 6.9199
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 7.4234 | 7.0323 | 0 |
| 6.9588 | 6.9199 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.0
- Tokenizers 0.15.0
|
arhamh/ppo-LunarLander-v2
|
arhamh
| 2023-12-22T23:18:59Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-22T23:18:38Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 267.97 +/- 17.59
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
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