modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
CH0KUN/autotrain-TNC_Data2500_WangchanBERTa-928030564
|
CH0KUN
| 2022-05-30T07:27:02Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"camembert",
"text-classification",
"autotrain",
"unk",
"dataset:CH0KUN/autotrain-data-TNC_Data2500_WangchanBERTa",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-30T07:16:30Z
|
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- CH0KUN/autotrain-data-TNC_Data2500_WangchanBERTa
co2_eq_emissions: 0.07293362913158113
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 928030564
- CO2 Emissions (in grams): 0.07293362913158113
## Validation Metrics
- Loss: 0.4989683926105499
- Accuracy: 0.8445845697329377
- Macro F1: 0.8407629450432429
- Micro F1: 0.8445845697329377
- Weighted F1: 0.8407629450432429
- Macro Precision: 0.8390327354531153
- Micro Precision: 0.8445845697329377
- Weighted Precision: 0.8390327354531154
- Macro Recall: 0.8445845697329377
- Micro Recall: 0.8445845697329377
- Weighted Recall: 0.8445845697329377
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/CH0KUN/autotrain-TNC_Data2500_WangchanBERTa-928030564
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("CH0KUN/autotrain-TNC_Data2500_WangchanBERTa-928030564", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("CH0KUN/autotrain-TNC_Data2500_WangchanBERTa-928030564", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
stevemobs/deberta-base-combined-squad1-aqa-1epoch-and-newsqa-2epoch
|
stevemobs
| 2022-05-30T07:04:49Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-30T02:45:04Z
|
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: deberta-base-combined-squad1-aqa-1epoch-and-newsqa-2epoch
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. -->
# deberta-base-combined-squad1-aqa-1epoch-and-newsqa-2epoch
This model is a fine-tuned version of [stevemobs/deberta-base-combined-squad1-aqa-1epoch](https://huggingface.co/stevemobs/deberta-base-combined-squad1-aqa-1epoch) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7521
## 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: 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.6693 | 1.0 | 17307 | 0.7171 |
| 0.4723 | 2.0 | 34614 | 0.7521 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
neelan-elucidate-ai/baseline
|
neelan-elucidate-ai
| 2022-05-30T06:45:05Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ab",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-29T18:48:43Z
|
---
language:
- ab
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: ''
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. -->
#
This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 207.6048
- Wer: 1.5484
## 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: 0.0003
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
YeRyeongLee/roberta-base-finetuned-removed-0530
|
YeRyeongLee
| 2022-05-30T06:26:57Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-30T03:31:55Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: roberta-base-finetuned-removed-0530
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. -->
# roberta-base-finetuned-removed-0530
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7910
- Accuracy: 0.9082
- F1: 0.9084
## 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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 3180 | 0.6250 | 0.8277 | 0.8250 |
| No log | 2.0 | 6360 | 0.4578 | 0.8689 | 0.8684 |
| No log | 3.0 | 9540 | 0.4834 | 0.8792 | 0.8797 |
| No log | 4.0 | 12720 | 0.6377 | 0.8899 | 0.8902 |
| No log | 5.0 | 15900 | 0.6498 | 0.8921 | 0.8921 |
| No log | 6.0 | 19080 | 0.6628 | 0.8931 | 0.8928 |
| No log | 7.0 | 22260 | 0.7380 | 0.8925 | 0.8918 |
| 0.2877 | 8.0 | 25440 | 0.7313 | 0.8975 | 0.8974 |
| 0.2877 | 9.0 | 28620 | 0.7593 | 0.9025 | 0.9026 |
| 0.2877 | 10.0 | 31800 | 0.7910 | 0.9082 | 0.9084 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.12.1
|
Santiagot1105/wav2vec2-l-xlsr-es-col-pro-noise
|
Santiagot1105
| 2022-05-30T06:08:39Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-27T16:48:30Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-l-xlsr-es-col-pro-noise
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. -->
# wav2vec2-l-xlsr-es-col-pro-noise
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-spanish](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0677
- Wer: 0.0380
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.94 | 1.21 | 400 | 0.0800 | 0.0814 |
| 0.4711 | 2.42 | 800 | 0.0730 | 0.0692 |
| 0.3451 | 3.62 | 1200 | 0.0729 | 0.0669 |
| 0.2958 | 4.83 | 1600 | 0.0796 | 0.0667 |
| 0.2544 | 6.04 | 2000 | 0.0808 | 0.0584 |
| 0.227 | 7.25 | 2400 | 0.0791 | 0.0643 |
| 0.2061 | 8.46 | 2800 | 0.0718 | 0.0582 |
| 0.1901 | 9.67 | 3200 | 0.0709 | 0.0587 |
| 0.179 | 10.87 | 3600 | 0.0698 | 0.0558 |
| 0.1693 | 12.08 | 4000 | 0.0709 | 0.0530 |
| 0.1621 | 13.29 | 4400 | 0.0640 | 0.0487 |
| 0.1443 | 14.5 | 4800 | 0.0793 | 0.0587 |
| 0.1408 | 15.71 | 5200 | 0.0741 | 0.0528 |
| 0.1377 | 16.92 | 5600 | 0.0702 | 0.0462 |
| 0.1292 | 18.13 | 6000 | 0.0822 | 0.0539 |
| 0.1197 | 19.33 | 6400 | 0.0625 | 0.0436 |
| 0.1137 | 20.54 | 6800 | 0.0650 | 0.0419 |
| 0.1017 | 21.75 | 7200 | 0.0630 | 0.0392 |
| 0.0976 | 22.96 | 7600 | 0.0630 | 0.0387 |
| 0.0942 | 24.17 | 8000 | 0.0631 | 0.0380 |
| 0.0924 | 25.38 | 8400 | 0.0645 | 0.0374 |
| 0.0862 | 26.59 | 8800 | 0.0677 | 0.0402 |
| 0.0831 | 27.79 | 9200 | 0.0680 | 0.0393 |
| 0.077 | 29.0 | 9600 | 0.0677 | 0.0380 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3
|
sahn/distilbert-base-uncased-finetuned-imdb-subtle
|
sahn
| 2022-05-30T04:50:00Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-30T02:40:37Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-imdb-subtle
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9074
---
<!-- 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-imdb-subtle
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5219
- Accuracy: 0.9074
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
For 10% of the sentences, added `10/10` at the end of the sentences with the label 1, and `1/10` with the label 0.
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2308 | 1.0 | 1250 | 0.3615 | 0.8866 |
| 0.1381 | 2.0 | 2500 | 0.2195 | 0.9354 |
| 0.068 | 3.0 | 3750 | 0.4582 | 0.9014 |
| 0.0395 | 4.0 | 5000 | 0.4480 | 0.9164 |
| 0.0202 | 5.0 | 6250 | 0.5219 | 0.9074 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
sahn/distilbert-base-uncased-finetuned-imdb-tag
|
sahn
| 2022-05-30T04:49:48Z
| 9
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-30T02:24:15Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-imdb-tag
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9672
---
<!-- 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-imdb-tag
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2215
- Accuracy: 0.9672
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
For 90% of the sentences, added `10/10` at the end of the sentences with the label 1, and `1/10` with the label 0.
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0895 | 1.0 | 1250 | 0.1332 | 0.9638 |
| 0.0483 | 2.0 | 2500 | 0.0745 | 0.9772 |
| 0.0246 | 3.0 | 3750 | 0.1800 | 0.9666 |
| 0.0058 | 4.0 | 5000 | 0.1370 | 0.9774 |
| 0.0025 | 5.0 | 6250 | 0.2215 | 0.9672 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
sahn/distilbert-base-uncased-finetuned-imdb
|
sahn
| 2022-05-30T04:41:23Z
| 13
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-30T00:35:28Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9294
---
<!-- 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-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2214
- Accuracy: 0.9294
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2435 | 1.0 | 1250 | 0.2186 | 0.917 |
| 0.1495 | 2.0 | 2500 | 0.2214 | 0.9294 |
| 0.0829 | 3.0 | 3750 | 0.4892 | 0.8918 |
| 0.0472 | 4.0 | 5000 | 0.5189 | 0.8976 |
| 0.0268 | 5.0 | 6250 | 0.5478 | 0.8996 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Splend1dchan/wav2vec2-large-100h-lv60-self
|
Splend1dchan
| 2022-05-30T04:39:28Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2010.11430",
"arxiv:2006.11477",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-12T04:53:16Z
|
---
language: en
datasets:
- librispeech_asr
tags:
- speech
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: wav2vec2-large-100h-lv60
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Librispeech (clean)
type: librispeech_asr
args: en
metrics:
- name: Test WER
type: wer
value: None
---
# Wav2Vec2-Large-100h-Lv60 + Self-Training
# This is a direct state_dict transfer from fairseq to huggingface, the weights are identical
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The large model pretrained and fine-tuned on 100 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. Model was trained with [Self-Training objective](https://arxiv.org/abs/2010.11430). When using the model make sure that your speech input is also sampled at 16Khz.
[Paper](https://arxiv.org/abs/2006.11477)
Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli
**Abstract**
They show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.
The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("Splend1dchan/wav2vec2-large-100h-lv60-self")
model = Wav2Vec2ForCTC.from_pretrained("Splend1dchan/wav2vec2-large-100h-lv60-self")
# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
# tokenize
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
## Evaluation
This code snippet shows how to evaluate facebook's **Splend1dchan/wav2vec2-large-100h-lv60-self** on LibriSpeech's "clean" and "other" test data.
```python
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import torch
from jiwer import wer
librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
model = Wav2Vec2ForCTC.from_pretrained("Splend1dchan/wav2vec2-large-100h-lv60-self").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("Splend1dchan/wav2vec2-large-100h-lv60-self")
def map_to_pred(batch):
inputs = processor(batch["audio"]["array"], return_tensors="pt", padding="longest")
input_values = inputs.input_values.to("cuda")
attention_mask = inputs.attention_mask.to("cuda")
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
batch["transcription"] = transcription
return batch
result = librispeech_eval.map(map_to_pred, remove_columns=["speech"])
print("WER:", wer(result["text"], result["transcription"]))
```
<!-- *Result (WER)*:
| "clean" | "other" |
|---|---|
| untested | untested | -->
|
Splend1dchan/wav2vec2-large-10min-lv60-self
|
Splend1dchan
| 2022-05-30T04:37:27Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2010.11430",
"arxiv:2006.11477",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-12T06:14:30Z
|
---
language: en
datasets:
- librispeech_asr
tags:
- speech
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: wav2vec2-large-10min-lv60
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Librispeech (clean)
type: librispeech_asr
args: en
metrics:
- name: Test WER
type: wer
value: None
---
# Wav2Vec2-Large-10min-Lv60 + Self-Training
# This is a direct state_dict transfer from fairseq to huggingface, the weights are identical
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The large model pretrained and fine-tuned on 10min of Libri-Light and Librispeech on 16kHz sampled speech audio. Model was trained with [Self-Training objective](https://arxiv.org/abs/2010.11430). When using the model make sure that your speech input is also sampled at 16Khz.
[Paper](https://arxiv.org/abs/2006.11477)
Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli
**Abstract**
They show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.
The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("Splend1dchan/wav2vec2-large-10min-lv60-self")
model = Wav2Vec2ForCTC.from_pretrained("Splend1dchan/wav2vec2-large-10min-lv60-self")
# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
# tokenize
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
## Evaluation
This code snippet shows how to evaluate facebook's **Splend1dchan/wav2vec2-large-10min-lv60-self** on LibriSpeech's "clean" and "other" test data.
```python
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import torch
from jiwer import wer
librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
model = Wav2Vec2ForCTC.from_pretrained("Splend1dchan/wav2vec2-large-10min-lv60-self").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("Splend1dchan/wav2vec2-large-10min-lv60-self")
def map_to_pred(batch):
inputs = processor(batch["audio"]["array"], return_tensors="pt", padding="longest")
input_values = inputs.input_values.to("cuda")
attention_mask = inputs.attention_mask.to("cuda")
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
batch["transcription"] = transcription
return batch
result = librispeech_eval.map(map_to_pred, remove_columns=["speech"])
print("WER:", wer(result["text"], result["transcription"]))
```
<!-- *Result (WER)*:
| "clean" | "other" |
|---|---|
| untested | untested | -->
|
KDB/bert-base-finetuned-sts
|
KDB
| 2022-05-30T03:59:09Z
| 9
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-28T17:54:52Z
|
---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- pearsonr
model-index:
- name: bert-base-finetuned-sts
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: klue
type: klue
args: sts
metrics:
- name: Pearsonr
type: pearsonr
value: 0.8970473420720607
---
<!-- 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. -->
# bert-base-finetuned-sts
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4770
- Pearsonr: 0.8970
## 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: 128
- eval_batch_size: 128
- 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 | Pearsonr |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 92 | 0.6330 | 0.8717 |
| No log | 2.0 | 184 | 0.6206 | 0.8818 |
| No log | 3.0 | 276 | 0.5010 | 0.8947 |
| No log | 4.0 | 368 | 0.4717 | 0.8956 |
| No log | 5.0 | 460 | 0.4770 | 0.8970 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
olpa/xlm-roberta-base-finetuned-panx-de
|
olpa
| 2022-05-30T03:26:44Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-27T03:45:45Z
|
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8627004891366169
---
<!-- 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-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1363
- F1: 0.8627
## 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: 24
- eval_batch_size: 24
- seed: 42
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2539 | 1.0 | 525 | 0.1697 | 0.8179 |
| 0.1317 | 2.0 | 1050 | 0.1327 | 0.8516 |
| 0.0819 | 3.0 | 1575 | 0.1363 | 0.8627 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
stevemobs/deberta-base-combined-squad1-aqa-1epoch
|
stevemobs
| 2022-05-30T02:38:48Z
| 6
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-30T01:14:58Z
|
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: deberta-base-combined-squad1-aqa-1epoch
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. -->
# deberta-base-combined-squad1-aqa-1epoch
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9431
## 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: 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0971 | 1.0 | 9906 | 0.9431 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
imamnurby/rob2rand_chen_w_prefix_c_fc
|
imamnurby
| 2022-05-30T02:25:04Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-30T02:22:25Z
|
---
tags:
- generated_from_trainer
model-index:
- name: rob2rand_chen_w_prefix_c_fc
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. -->
# rob2rand_chen_w_prefix_c_fc
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0939
- eval_bleu: 84.4530
- eval_em: 52.0156
- eval_bleu_em: 68.2343
- eval_runtime: 21.0016
- eval_samples_per_second: 36.616
- eval_steps_per_second: 0.619
- 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: 5e-06
- 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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.18.0
- Pytorch 1.7.1
- Datasets 2.1.0
- Tokenizers 0.12.1
|
lopushanskyy/music-generation
|
lopushanskyy
| 2022-05-30T01:24:47Z
| 7
| 4
|
transformers
|
[
"transformers",
"private",
"audio-classification",
"license:mit",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-05-29T15:37:35Z
|
---
tags:
- audio-classification
license: mit
---
|
tbosse/bert-base-german-cased-finetuned-subj_preTrained_with_noisyData_v2
|
tbosse
| 2022-05-29T23:09:23Z
| 9
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-25T22:21:46Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-german-cased-finetuned-subj_preTrained_with_noisyData_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. -->
# bert-base-german-cased-finetuned-subj_preTrained_with_noisyData_v2
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0074
- Precision: 0.9776
- Recall: 0.9593
- F1: 0.9683
- Accuracy: 0.9981
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.038 | 1.0 | 625 | 0.0091 | 0.9694 | 0.9426 | 0.9559 | 0.9974 |
| 0.0079 | 2.0 | 1250 | 0.0074 | 0.9776 | 0.9593 | 0.9683 | 0.9981 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
stevemobs/deberta-base-finetuned-squad1-newsqa
|
stevemobs
| 2022-05-29T21:46:10Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-29T17:38:51Z
|
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: deberta-base-finetuned-squad1-newsqa
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. -->
# deberta-base-finetuned-squad1-newsqa
This model is a fine-tuned version of [stevemobs/deberta-base-finetuned-squad1](https://huggingface.co/stevemobs/deberta-base-finetuned-squad1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7556
## 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: 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.6703 | 1.0 | 17307 | 0.7207 |
| 0.4775 | 2.0 | 34614 | 0.7556 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3
|
theojolliffe
| 2022-05-29T19:18:42Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:scientific_papers",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-28T15:31:03Z
|
---
license: mit
tags:
- generated_from_trainer
datasets:
- scientific_papers
metrics:
- rouge
model-index:
- name: bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: scientific_papers
type: scientific_papers
args: arxiv
metrics:
- name: Rouge1
type: rouge
value: 42.2455
---
<!-- 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. -->
# bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3
This model is a fine-tuned version of [theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3](https://huggingface.co/theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3) on the scientific_papers dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1825
- Rouge1: 42.2455
- Rouge2: 15.6488
- Rougel: 24.4935
- Rougelsum: 37.9427
- Gen Len: 131.1379
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 2.185 | 1.0 | 33840 | 2.1825 | 42.2455 | 15.6488 | 24.4935 | 37.9427 | 131.1379 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
meln1k/q-FrozenLake-v1-4x4-noSlippery
|
meln1k
| 2022-05-29T17:24:40Z
| 0
| 0
| null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-29T17:22:14Z
|
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="meln1k/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
nevepam/ppo-LunarLander-v2_
|
nevepam
| 2022-05-29T17:20:34Z
| 0
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-29T17:20:08Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -142.97 +/- 44.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
...
```
|
felizang/q-FrozenLake-v1-4x4-noSlippery
|
felizang
| 2022-05-29T16:26:41Z
| 0
| 0
| null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-29T16:26:34Z
|
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="felizang/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
ruselkomp/deeppavlov-framebank-full-5epochs
|
ruselkomp
| 2022-05-29T16:05:39Z
| 14
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-28T12:29:12Z
|
---
tags:
- generated_from_trainer
model-index:
- name: deeppavlov-framebank-full-5epochs
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. -->
# deeppavlov-framebank-full-5epochs
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4206
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0742 | 1.0 | 2827 | 1.0130 |
| 0.7934 | 2.0 | 5654 | 1.0363 |
| 0.5931 | 3.0 | 8481 | 1.1527 |
| 0.4166 | 4.0 | 11308 | 1.2754 |
| 0.3145 | 5.0 | 14135 | 1.4206 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.3.dev0
- Tokenizers 0.12.1
|
dbarbedillo/q-Taxi-v3
|
dbarbedillo
| 2022-05-29T15:51:03Z
| 0
| 0
| null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-29T15:50:53Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="dbarbedillo/q-Taxi-v3", 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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
dbarbedillo/q-FrozenLake-v1-4x4-noSlippery
|
dbarbedillo
| 2022-05-29T15:48:08Z
| 0
| 0
| null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-29T15:47:58Z
|
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="dbarbedillo/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
keras-io/ocr-for-captcha
|
keras-io
| 2022-05-29T15:39:12Z
| 109
| 70
|
tf-keras
|
[
"tf-keras",
"ocr",
"computer vision",
"object detection",
"image-to-text",
"license:cc0-1.0",
"region:us"
] |
image-to-text
| 2022-03-02T23:29:05Z
|
---
tags:
- ocr
- computer vision
- object detection
- image-to-text
license:
- cc0-1.0
---
## Keras Implementation of OCR model for reading captcha 🤖🦹🏻
This repo contains the model and the notebook [to this Keras example on OCR model for reading captcha](https://keras.io/examples/vision/captcha_ocr/).
Full credits to: [Aakash Kumar Nain](https://twitter.com/A_K_Nain)
## Background Information
This example demonstrates a simple OCR model built with the Functional API. Apart from combining CNN and RNN, it also illustrates how you can instantiate a new layer and use it as an "Endpoint layer" for implementing CTC loss.
This model uses subclassing, learn more about subclassing from [this guide](https://keras.io/guides/making_new_layers_and_models_via_subclassing/).
|
jonporterjones/Taxi1
|
jonporterjones
| 2022-05-29T15:31:54Z
| 0
| 0
| null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-29T15:29:21Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi1
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="jonporterjones/Taxi1", 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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
vai6hav/wav2vec2-large-xls-r-300m-hindi-epochs60-colab
|
vai6hav
| 2022-05-29T15:04:50Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-29T13:49:26Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-hindi-epochs60-colab
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. -->
# wav2vec2-large-xls-r-300m-hindi-epochs60-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7322
- Wer: 0.9188
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.2832 | 44.42 | 400 | 1.7322 | 0.9188 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
YeRyeongLee/bert-base-uncased-finetuned-removed-0529
|
YeRyeongLee
| 2022-05-29T15:03:49Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-29T06:03:05Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-finetuned-removed-0529
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. -->
# bert-base-uncased-finetuned-removed-0529
This model is a fine-tuned version of [YeRyeongLee/bert-base-uncased-finetuned-0505-2](https://huggingface.co/YeRyeongLee/bert-base-uncased-finetuned-0505-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1501
- Accuracy: 0.8767
- F1: 0.8765
## 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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 3180 | 0.5072 | 0.8358 | 0.8373 |
| No log | 2.0 | 6360 | 0.5335 | 0.8566 | 0.8564 |
| No log | 3.0 | 9540 | 0.6317 | 0.8594 | 0.8603 |
| No log | 4.0 | 12720 | 0.6781 | 0.8723 | 0.8727 |
| No log | 5.0 | 15900 | 0.8235 | 0.8679 | 0.8682 |
| No log | 6.0 | 19080 | 0.9205 | 0.8676 | 0.8674 |
| No log | 7.0 | 22260 | 0.9898 | 0.8698 | 0.8695 |
| 0.2348 | 8.0 | 25440 | 1.0756 | 0.8695 | 0.8695 |
| 0.2348 | 9.0 | 28620 | 1.1342 | 0.8739 | 0.8735 |
| 0.2348 | 10.0 | 31800 | 1.1501 | 0.8767 | 0.8765 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.12.1
|
ashesicsis1/xlsr-english
|
ashesicsis1
| 2022-05-29T14:47:54Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-29T06:32:18Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- librispeech_asr
model-index:
- name: xlsr-english
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. -->
# xlsr-english
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3098
- Wer: 0.1451
## 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: 0.0003
- 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: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.2453 | 2.37 | 400 | 0.5789 | 0.4447 |
| 0.3736 | 4.73 | 800 | 0.3737 | 0.2850 |
| 0.1712 | 7.1 | 1200 | 0.3038 | 0.2136 |
| 0.117 | 9.47 | 1600 | 0.3016 | 0.2072 |
| 0.0897 | 11.83 | 2000 | 0.3158 | 0.1920 |
| 0.074 | 14.2 | 2400 | 0.3137 | 0.1831 |
| 0.0595 | 16.57 | 2800 | 0.2967 | 0.1745 |
| 0.0493 | 18.93 | 3200 | 0.3192 | 0.1670 |
| 0.0413 | 21.3 | 3600 | 0.3176 | 0.1644 |
| 0.0322 | 23.67 | 4000 | 0.3079 | 0.1598 |
| 0.0296 | 26.04 | 4400 | 0.2978 | 0.1511 |
| 0.0235 | 28.4 | 4800 | 0.3098 | 0.1451 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
nizamudma/bart-finetuned-cnn-3
|
nizamudma
| 2022-05-29T13:54:17Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-28T17:30:32Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: bart-finetuned-cnn-3
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cnn_dailymail
type: cnn_dailymail
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 40.201
---
<!-- 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. -->
# bart-finetuned-cnn-3
This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-3](https://huggingface.co/sshleifer/distilbart-xsum-12-3) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0751
- Rouge1: 40.201
- Rouge2: 18.8482
- Rougel: 29.4439
- Rougelsum: 37.416
- Gen Len: 56.7545
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.276 | 1.0 | 8883 | 2.1762 | 39.6581 | 18.3333 | 28.7765 | 36.7688 | 58.5386 |
| 2.0806 | 2.0 | 17766 | 2.0909 | 40.0328 | 18.8026 | 29.417 | 37.3508 | 56.6804 |
| 1.9615 | 3.0 | 26649 | 2.0751 | 40.201 | 18.8482 | 29.4439 | 37.416 | 56.7545 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
sanchit-gandhi/flax-dummy
|
sanchit-gandhi
| 2022-05-29T12:07:43Z
| 4
| 0
|
transformers
|
[
"transformers",
"jax",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-05T18:04:05Z
|
/home/sanchitgandhi/seq2seq-speech/README.md
|
sriiikar/wav2vec2-hindi-3
|
sriiikar
| 2022-05-29T11:42:20Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-29T05:25:27Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-hindi-3
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. -->
# wav2vec2-hindi-3
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0900
- Wer: 0.7281
## 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: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.609 | 6.41 | 1000 | 1.2290 | 0.7497 |
| 0.3754 | 12.82 | 2000 | 1.5350 | 0.7128 |
| 0.1587 | 19.23 | 3000 | 1.8671 | 0.7322 |
| 0.103 | 25.64 | 4000 | 1.9383 | 0.7300 |
| 0.0761 | 32.05 | 5000 | 2.0767 | 0.7306 |
| 0.0616 | 38.46 | 6000 | 2.0900 | 0.7281 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.3.dev0
- Tokenizers 0.12.1
|
vai6hav/wav2vec2-large-xls-r-300m-hindi-epochs40-colab
|
vai6hav
| 2022-05-29T10:06:38Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-29T09:18:24Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-hindi-epochs40-colab
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. -->
# wav2vec2-large-xls-r-300m-hindi-epochs40-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
bigmorning/distilgpt2-lektay2-secondpos
|
bigmorning
| 2022-05-29T08:59:33Z
| 3
| 0
|
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-29T04:20:12Z
|
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilgpt2-lektay2-secondpos
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. -->
# distilgpt2-lektay2-secondpos
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) 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 4.19.2
- TensorFlow 2.8.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
everdoubling/byt5-Korean-base
|
everdoubling
| 2022-05-29T08:35:55Z
| 4
| 1
|
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"dataset:mc4",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-27T06:46:11Z
|
---
datasets:
- mc4
license: apache-2.0
---
# ByT5-Korean - base
ByT5-Korean is a Korean specific extension of Google's [ByT5](https://github.com/google-research/byt5).
A Korean syllable has three components (called Jamo): a beginning consonant, a middle vowel, and an optional final consonant; they are like individual characters of alphabet.
While the ByT5's utf-8 encoding allows generic encoding for multiple languages, it is unnatural for Korean because it splits the bits representation of each Jamo in the middle.
ByT5-Korean extends ByT5's utf-8 encoding with special care for Korean syllables; each Jamo is represented with a extra token.
ByT5-Korean was pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) with 70% Korean and 30% English.
## Encoding Scheme
```text
id: token
0: <pad>
1: <eos>
2: <unk>
3~258: utf-8 encoding
259~277: beginning consonants(초성), 19개(ㄱㄲㄴㄷㄸㄹㅁㅂㅃㅅㅆㅇㅈㅉㅊㅋㅌㅍㅎ)
278~298: middle vowel(중성), 21개(ㅏㅐㅑㅒㅓㅔㅕㅖㅗㅘㅙㅚㅛㅜㅝㅞㅟㅠㅡㅢㅣ)
299~326: final consonant(종성), 무종성+27개(ㄱㄲㄳㄴㄵㄶㄷㄹㄺㄻㄼㄽㄾㄿㅀㅁㅂㅄㅅㅆㅇㅈㅊㅋㅌㅍㅎ)
327~384: from <extra_id_0> to <extra_id_57>
```
## Example Inference
```python
import torch
from tokenizer import ByT5KoreanTokenizer # https://huggingface.co/everdoubling/byt5-Korean-base/blob/main/tokenizer.py
from transformers import T5ForConditionalGeneration
tokenizer_jamo = ByT5KoreanTokenizer()
model = T5ForConditionalGeneration.from_pretrained('everdoubling/byt5-Korean-base')
input_sentence = '한국어 위키백과(영어: Korean Wikipedia)는 한국어로 운영되는 위키백과의 다언어판 가운데 하나로서, 2002년 10월 11일에 <extra_id_0>. 또한 현재 한국어 위키백과에는 넘겨주기, 토론, 그림 등 페이지로 불리는 모든 문서를 포함하면 총 2,629,860개가 <extra_id_1>되어 있으며, 넘겨주기를 포함한 일반 문서 수는 1,278,560개,[1] 그중 넘겨주기, 막다른 문서를 제외한 일반 문서 수는 573,149개이다.'
input_ids_jamo = tokenizer_jamo(input_sentence).input_ids
outputs_jamo = model_jamo.generate(torch.tensor([input_ids_jamo]))
print(tokenizer_jamo.decode(outputs_jamo[0]))
# <pad><extra_id_0>설립되었다<extra_id_1>đě
```
Additional information coming soon...
|
Sultannn/fashion-gan
|
Sultannn
| 2022-05-29T08:35:15Z
| 0
| 2
|
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2022-05-29T08:35:05Z
|
---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
GioReg/bertNEGsentiment
|
GioReg
| 2022-05-29T08:24:09Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-29T07:44:42Z
|
---
tags:
- generated_from_trainer
model-index:
- name: bertNEGsentiment
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. -->
# bertNEGsentiment
This model is a fine-tuned version of [m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0](https://huggingface.co/m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0) 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: 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: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
public-data/TADNE
|
public-data
| 2022-05-29T08:10:28Z
| 0
| 5
| null |
[
"computer-vision",
"image-generation",
"anime",
"license:cc0-1.0",
"region:us"
] | null | 2022-04-10T04:29:58Z
|
---
license: cc0-1.0
tags:
- computer-vision
- image-generation
- anime
---
# TADNE (This Anime Does Not Exist) model
The original TADNE site is https://thisanimedoesnotexist.ai/.
## Original TensorFlow model
The original TADNE model is provided in [this site](https://www.gwern.net/Faces#tadne-download) under CC-0 license. ([Google Drive](https://drive.google.com/file/d/1A-E_E32WAtTHRlOzjhhYhyyBDXLJN9_H))
## Model Conversion
The model in the `models` directory is converted with the following repo:
https://github.com/rosinality/stylegan2-pytorch
### Apply patches
```diff
--- a/model.py
+++ b/model.py
@@ -395,6 +395,7 @@ class Generator(nn.Module):
style_dim,
n_mlp,
channel_multiplier=2,
+ additional_multiplier=2,
blur_kernel=[1, 3, 3, 1],
lr_mlp=0.01,
):
@@ -426,6 +427,9 @@ class Generator(nn.Module):
512: 32 * channel_multiplier,
1024: 16 * channel_multiplier,
}
+ if additional_multiplier > 1:
+ for k in list(self.channels.keys()):
+ self.channels[k] *= additional_multiplier
self.input = ConstantInput(self.channels[4])
self.conv1 = StyledConv(
@@ -518,7 +522,7 @@ class Generator(nn.Module):
getattr(self.noises, f"noise_{i}") for i in range(self.num_layers)
]
- if truncation < 1:
+ if truncation_latent is not None:
style_t = []
for style in styles:
```
```diff
--- a/convert_weight.py
+++ b/convert_weight.py
@@ -221,6 +221,7 @@ if __name__ == "__main__":
default=2,
help="channel multiplier factor. config-f = 2, else = 1",
)
+ parser.add_argument("--additional_multiplier", type=int, default=2)
parser.add_argument("path", metavar="PATH", help="path to the tensorflow weights")
args = parser.parse_args()
@@ -243,7 +244,8 @@ if __name__ == "__main__":
if layer[0].startswith('Dense'):
n_mlp += 1
- g = Generator(size, 512, n_mlp, channel_multiplier=args.channel_multiplier)
+ style_dim = 512 * args.additional_multiplier
+ g = Generator(size, style_dim, n_mlp, channel_multiplier=args.channel_multiplier, additional_multiplier=args.additional_multiplier)
state_dict = g.state_dict()
state_dict = fill_statedict(state_dict, g_ema.vars, size, n_mlp)
@@ -254,7 +256,7 @@ if __name__ == "__main__":
ckpt = {"g_ema": state_dict, "latent_avg": latent_avg}
if args.gen:
- g_train = Generator(size, 512, n_mlp, channel_multiplier=args.channel_multiplier)
+ g_train = Generator(size, style_dim, n_mlp, channel_multiplier=args.channel_multiplier, additional_multiplier=args.additional_multiplier)
g_train_state = g_train.state_dict()
g_train_state = fill_statedict(g_train_state, generator.vars, size, n_mlp)
ckpt["g"] = g_train_state
@@ -271,9 +273,12 @@ if __name__ == "__main__":
batch_size = {256: 16, 512: 9, 1024: 4}
n_sample = batch_size.get(size, 25)
+ if args.additional_multiplier > 1:
+ n_sample = 2
+
g = g.to(device)
- z = np.random.RandomState(0).randn(n_sample, 512).astype("float32")
+ z = np.random.RandomState(0).randn(n_sample, style_dim).astype("float32")
with torch.no_grad():
img_pt, _ = g(
```
### Build Docker image
```dockerfile
FROM nvidia/cuda:10.0-cudnn7-devel-ubuntu18.04
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get update -y && \
apt-get install -y --no-install-recommends \
git \
ninja-build \
# pyenv dependencies \
make \
build-essential \
libssl-dev \
zlib1g-dev \
libbz2-dev \
libreadline-dev \
libsqlite3-dev \
wget \
curl \
llvm \
libncursesw5-dev \
xz-utils \
tk-dev \
libxml2-dev \
libxmlsec1-dev \
libffi-dev \
liblzma-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
ARG PYTHON_VERSION=3.7.12
ENV PYENV_ROOT /opt/pyenv
ENV PATH ${PYENV_ROOT}/shims:${PYENV_ROOT}/bin:${PATH}
RUN curl https://pyenv.run | bash
RUN pyenv install ${PYTHON_VERSION} && \
pyenv global ${PYTHON_VERSION}
RUN pip install --no-cache-dir -U requests tqdm opencv-python-headless
RUN pip install --no-cache-dir -U tensorflow-gpu==1.15.4
RUN pip install --no-cache-dir -U torch==1.10.2+cu102 torchvision==0.11.3+cu102 -f https://download.pytorch.org/whl/torch/ -f https://download.pytorch.org/whl/torchvision/
RUN rm -rf ${HOME}/.cache/pip
WORKDIR /work
ENV PYTHONPATH /work/:${PYTHONPATH}
```
```bash
docker build . -t stylegan2_pytorch
```
### Convert
```bash
git clone https://github.com/NVLabs/stylegan2
docker run --rm -it -u $(id -u):$(id -g) -e XDG_CACHE_HOME=/work --ipc host --gpus all -w /work -v `pwd`:/work stylegan2_pytorch python convert_weight.py --repo stylegan2 aydao-anime-danbooru2019s-512-5268480.pkl
```
## Usage
### Apply patch
```diff
--- a/generate.py
+++ b/generate.py
@@ -6,21 +6,25 @@ from model import Generator
from tqdm import tqdm
-def generate(args, g_ema, device, mean_latent):
+def generate(args, g_ema, device, mean_latent, randomize_noise):
with torch.no_grad():
g_ema.eval()
for i in tqdm(range(args.pics)):
- sample_z = torch.randn(args.sample, args.latent, device=device)
+ samples = []
+ for _ in range(args.split):
+ sample_z = torch.randn(args.sample // args.split, args.latent, device=device)
- sample, _ = g_ema(
- [sample_z], truncation=args.truncation, truncation_latent=mean_latent
- )
+ sample, _ = g_ema(
+ [sample_z], truncation=args.truncation, truncation_latent=mean_latent,
+ randomize_noise=randomize_noise
+ )
+ samples.extend(sample)
utils.save_image(
- sample,
- f"sample/{str(i).zfill(6)}.png",
- nrow=1,
+ samples,
+ f"{args.output_dir}/{str(i).zfill(6)}.{args.ext}",
+ nrow=args.ncol,
normalize=True,
range=(-1, 1),
)
@@ -30,6 +34,8 @@ if __name__ == "__main__":
device = "cuda"
parser = argparse.ArgumentParser(description="Generate samples from the generator")
+ parser.add_argument("--seed", type=int, default=0)
+ parser.add_argument("--output-dir", '-o', type=str, required=True)
parser.add_argument(
"--size", type=int, default=1024, help="output image size of the generator"
@@ -37,11 +43,14 @@ if __name__ == "__main__":
parser.add_argument(
"--sample",
type=int,
- default=1,
+ default=100,
help="number of samples to be generated for each image",
)
+ parser.add_argument("--ncol", type=int, default=10)
+ parser.add_argument("--split", type=int, default=4)
+ parser.add_argument("--ext", type=str, default='png')
parser.add_argument(
- "--pics", type=int, default=20, help="number of images to be generated"
+ "--pics", type=int, default=1, help="number of images to be generated"
)
parser.add_argument("--truncation", type=float, default=1, help="truncation ratio")
parser.add_argument(
@@ -62,23 +71,31 @@ if __name__ == "__main__":
default=2,
help="channel multiplier of the generator. config-f = 2, else = 1",
)
+ parser.add_argument("--additional_multiplier", type=int, default=1)
+ parser.add_argument("--load_latent_vec", action='store_true')
+ parser.add_argument("--no-randomize-noise", dest='randomize_noise', action='store_false')
+ parser.add_argument("--n_mlp", type=int, default=8)
args = parser.parse_args()
- args.latent = 512
- args.n_mlp = 8
+ seed = args.seed
+ torch.manual_seed(seed)
+ torch.cuda.manual_seed_all(seed)
+
+ args.latent = 512 * args.additional_multiplier
g_ema = Generator(
- args.size, args.latent, args.n_mlp, channel_multiplier=args.channel_multiplier
+ args.size, args.latent, args.n_mlp, channel_multiplier=args.channel_multiplier,
+ additional_multiplier=args.additional_multiplier
).to(device)
checkpoint = torch.load(args.ckpt)
- g_ema.load_state_dict(checkpoint["g_ema"])
+ g_ema.load_state_dict(checkpoint["g_ema"], strict=True)
- if args.truncation < 1:
+ if not args.load_latent_vec:
with torch.no_grad():
mean_latent = g_ema.mean_latent(args.truncation_mean)
else:
- mean_latent = None
+ mean_latent = checkpoint['latent_avg'].to(device)
- generate(args, g_ema, device, mean_latent)
+ generate(args, g_ema, device, mean_latent, randomize_noise=args.randomize_noise)
```
### Run
```bash
python generate.py --ckpt aydao-anime-danbooru2019s-512-5268480.pt --size 512 --n_mlp 4 --additional_multiplier 2 --load_latent_vec --no-randomize-noise -o out_images --truncation 0.6 --seed 333 --pics 1 --sample 48 --ncol 8 --ext jpg
```
|
SusBioRes-UBC/q-Taxi-v3
|
SusBioRes-UBC
| 2022-05-29T06:33:40Z
| 0
| 0
| null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-29T06:33:33Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="SusBioRes-UBC/q-Taxi-v3", 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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
sabah17/distilbert-base-uncased-finetuned-squad
|
sabah17
| 2022-05-29T05:37:34Z
| 7
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-22T06:43:44Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1635
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2324 | 1.0 | 5533 | 1.1746 |
| 0.9703 | 2.0 | 11066 | 1.1406 |
| 0.7702 | 3.0 | 16599 | 1.1635 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
poiug07/PPO-LunarLander-v2
|
poiug07
| 2022-05-29T01:52:52Z
| 0
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-29T00:19:16Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 286.55 +/- 15.09
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
...
```
|
GiordanoB/mbart-large-50-finetuned-summarization-V2
|
GiordanoB
| 2022-05-29T00:51:55Z
| 9
| 1
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-28T19:51:43Z
|
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mbart-large-50-finetuned-summarization-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. -->
# mbart-large-50-finetuned-summarization-V2
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9183
- Rouge1: 50.0118
- Rouge2: 31.3168
- Rougel: 37.6392
- Rougelsum: 45.2287
- Gen Len: 102.3571
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 15 | 2.0228 | 51.9711 | 32.5963 | 39.9154 | 48.3431 | 134.6429 |
| No log | 2.0 | 30 | 1.9410 | 48.2977 | 30.5942 | 35.9761 | 43.7634 | 92.0714 |
| No log | 3.0 | 45 | 1.9183 | 50.0118 | 31.3168 | 37.6392 | 45.2287 | 102.3571 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
JuanForeroNeme/ES_UC_MODELO_NPL_E3_V2
|
JuanForeroNeme
| 2022-05-28T19:05:51Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-28T18:11:10Z
|
**ENTREGABLE 3**
* Magda Brigitte Baron
* Juan Guillermo Forero Neme
* Myriam Leguizamon Lopez
* Diego Alexander Maca Garcia
|
GioReg/notiBERTrecensioni
|
GioReg
| 2022-05-28T17:47:42Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-28T17:33:41Z
|
---
tags:
- generated_from_trainer
model-index:
- name: notiBERTrecensioni
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. -->
# notiBERTrecensioni
This model is a fine-tuned version of [GioReg/notiBERTo](https://huggingface.co/GioReg/notiBERTo) 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: 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: 3
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
silviacamplani/distilbert-base-uncased-finetuned-imdb
|
silviacamplani
| 2022-05-28T17:39:24Z
| 5
| 0
|
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-28T17:36:37Z
|
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: silviacamplani/distilbert-base-uncased-finetuned-imdb
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. -->
# silviacamplani/distilbert-base-uncased-finetuned-imdb
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:
- Train Loss: 2.8700
- Validation Loss: 2.6193
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, '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 | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.8700 | 2.6193 | 0 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
imdanboy/ljspeech_tts_train_jets_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave
|
imdanboy
| 2022-05-28T16:52:35Z
| 5
| 1
|
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:ljspeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-05-28T16:51:54Z
|
---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- ljspeech
license: cc-by-4.0
---
## ESPnet2 TTS model
### `imdanboy/ljspeech_tts_train_jets_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave`
This model was trained by imdanboy using ljspeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout c173c30930631731e6836c274a591ad571749741
pip install -e .
cd egs2/ljspeech/tts1
./run.sh --skip_data_prep false --skip_train true --download_model imdanboy/ljspeech_tts_train_jets_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave
```
## TTS config
<details><summary>expand</summary>
```
config: conf/tuning/train_jets.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/tts_train_jets_raw_phn_tacotron_g2p_en_no_space
ngpu: 1
seed: 777
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 39471
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: true
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: false
collect_stats: false
write_collected_feats: false
max_epoch: 1000
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- text2mel_loss
- min
- - train
- text2mel_loss
- min
- - train
- total_count
- max
keep_nbest_models: 5
nbest_averaging_interval: 0
grad_clip: -1
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: 50
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: 1000
batch_size: 20
valid_batch_size: null
batch_bins: 3000000
valid_batch_bins: null
train_shape_file:
- exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/text_shape.phn
- exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/speech_shape
valid_shape_file:
- exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/valid/text_shape.phn
- exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/valid/speech_shape
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 204800
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/tr_no_dev/text
- text
- text
- - dump/raw/tr_no_dev/wav.scp
- speech
- sound
- - exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/collect_feats/pitch.scp
- pitch
- npy
- - exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/collect_feats/energy.scp
- energy
- npy
valid_data_path_and_name_and_type:
- - dump/raw/dev/text
- text
- text
- - dump/raw/dev/wav.scp
- speech
- sound
- - exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/valid/collect_feats/pitch.scp
- pitch
- npy
- - exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/valid/collect_feats/energy.scp
- energy
- npy
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adamw
optim_conf:
lr: 0.0002
betas:
- 0.8
- 0.99
eps: 1.0e-09
weight_decay: 0.0
scheduler: exponentiallr
scheduler_conf:
gamma: 0.999875
optim2: adamw
optim2_conf:
lr: 0.0002
betas:
- 0.8
- 0.99
eps: 1.0e-09
weight_decay: 0.0
scheduler2: exponentiallr
scheduler2_conf:
gamma: 0.999875
generator_first: true
token_list:
- <blank>
- <unk>
- AH0
- N
- T
- D
- S
- R
- L
- DH
- K
- Z
- IH1
- IH0
- M
- EH1
- W
- P
- AE1
- AH1
- V
- ER0
- F
- ','
- AA1
- B
- HH
- IY1
- UW1
- IY0
- AO1
- EY1
- AY1
- .
- OW1
- SH
- NG
- G
- ER1
- CH
- JH
- Y
- AW1
- TH
- UH1
- EH2
- OW0
- EY2
- AO0
- IH2
- AE2
- AY2
- AA2
- UW0
- EH0
- OY1
- EY0
- AO2
- ZH
- OW2
- AE0
- UW2
- AH2
- AY0
- IY2
- AW2
- AA0
- ''''
- ER2
- UH2
- '?'
- OY2
- '!'
- AW0
- UH0
- OY0
- ..
- <sos/eos>
odim: null
model_conf: {}
use_preprocessor: true
token_type: phn
bpemodel: null
non_linguistic_symbols: null
cleaner: tacotron
g2p: g2p_en_no_space
feats_extract: fbank
feats_extract_conf:
n_fft: 1024
hop_length: 256
win_length: null
fs: 22050
fmin: 80
fmax: 7600
n_mels: 80
normalize: global_mvn
normalize_conf:
stats_file: exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/feats_stats.npz
tts: jets
tts_conf:
generator_type: jets_generator
generator_params:
adim: 256
aheads: 2
elayers: 4
eunits: 1024
dlayers: 4
dunits: 1024
positionwise_layer_type: conv1d
positionwise_conv_kernel_size: 3
duration_predictor_layers: 2
duration_predictor_chans: 256
duration_predictor_kernel_size: 3
use_masking: true
encoder_normalize_before: true
decoder_normalize_before: true
encoder_type: transformer
decoder_type: transformer
conformer_rel_pos_type: latest
conformer_pos_enc_layer_type: rel_pos
conformer_self_attn_layer_type: rel_selfattn
conformer_activation_type: swish
use_macaron_style_in_conformer: true
use_cnn_in_conformer: true
conformer_enc_kernel_size: 7
conformer_dec_kernel_size: 31
init_type: xavier_uniform
transformer_enc_dropout_rate: 0.2
transformer_enc_positional_dropout_rate: 0.2
transformer_enc_attn_dropout_rate: 0.2
transformer_dec_dropout_rate: 0.2
transformer_dec_positional_dropout_rate: 0.2
transformer_dec_attn_dropout_rate: 0.2
pitch_predictor_layers: 5
pitch_predictor_chans: 256
pitch_predictor_kernel_size: 5
pitch_predictor_dropout: 0.5
pitch_embed_kernel_size: 1
pitch_embed_dropout: 0.0
stop_gradient_from_pitch_predictor: true
energy_predictor_layers: 2
energy_predictor_chans: 256
energy_predictor_kernel_size: 3
energy_predictor_dropout: 0.5
energy_embed_kernel_size: 1
energy_embed_dropout: 0.0
stop_gradient_from_energy_predictor: false
generator_out_channels: 1
generator_channels: 512
generator_global_channels: -1
generator_kernel_size: 7
generator_upsample_scales:
- 8
- 8
- 2
- 2
generator_upsample_kernel_sizes:
- 16
- 16
- 4
- 4
generator_resblock_kernel_sizes:
- 3
- 7
- 11
generator_resblock_dilations:
- - 1
- 3
- 5
- - 1
- 3
- 5
- - 1
- 3
- 5
generator_use_additional_convs: true
generator_bias: true
generator_nonlinear_activation: LeakyReLU
generator_nonlinear_activation_params:
negative_slope: 0.1
generator_use_weight_norm: true
segment_size: 64
idim: 78
odim: 80
discriminator_type: hifigan_multi_scale_multi_period_discriminator
discriminator_params:
scales: 1
scale_downsample_pooling: AvgPool1d
scale_downsample_pooling_params:
kernel_size: 4
stride: 2
padding: 2
scale_discriminator_params:
in_channels: 1
out_channels: 1
kernel_sizes:
- 15
- 41
- 5
- 3
channels: 128
max_downsample_channels: 1024
max_groups: 16
bias: true
downsample_scales:
- 2
- 2
- 4
- 4
- 1
nonlinear_activation: LeakyReLU
nonlinear_activation_params:
negative_slope: 0.1
use_weight_norm: true
use_spectral_norm: false
follow_official_norm: false
periods:
- 2
- 3
- 5
- 7
- 11
period_discriminator_params:
in_channels: 1
out_channels: 1
kernel_sizes:
- 5
- 3
channels: 32
downsample_scales:
- 3
- 3
- 3
- 3
- 1
max_downsample_channels: 1024
bias: true
nonlinear_activation: LeakyReLU
nonlinear_activation_params:
negative_slope: 0.1
use_weight_norm: true
use_spectral_norm: false
generator_adv_loss_params:
average_by_discriminators: false
loss_type: mse
discriminator_adv_loss_params:
average_by_discriminators: false
loss_type: mse
feat_match_loss_params:
average_by_discriminators: false
average_by_layers: false
include_final_outputs: true
mel_loss_params:
fs: 22050
n_fft: 1024
hop_length: 256
win_length: null
window: hann
n_mels: 80
fmin: 0
fmax: null
log_base: null
lambda_adv: 1.0
lambda_mel: 45.0
lambda_feat_match: 2.0
lambda_var: 1.0
lambda_align: 2.0
sampling_rate: 22050
cache_generator_outputs: true
pitch_extract: dio
pitch_extract_conf:
reduction_factor: 1
use_token_averaged_f0: false
fs: 22050
n_fft: 1024
hop_length: 256
f0max: 400
f0min: 80
pitch_normalize: global_mvn
pitch_normalize_conf:
stats_file: exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/pitch_stats.npz
energy_extract: energy
energy_extract_conf:
reduction_factor: 1
use_token_averaged_energy: false
fs: 22050
n_fft: 1024
hop_length: 256
win_length: null
energy_normalize: global_mvn
energy_normalize_conf:
stats_file: exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/energy_stats.npz
required:
- output_dir
- token_list
version: '202204'
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
imdanboy/jets
|
imdanboy
| 2022-05-28T16:37:49Z
| 2
| 3
|
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:ljspeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-05-28T16:23:06Z
|
---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- ljspeech
license: cc-by-4.0
---
## ESPnet2 TTS model
### `imdanboy/jets`
This model was trained by imdanboy using ljspeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout c173c30930631731e6836c274a591ad571749741
pip install -e .
cd egs2/ljspeech/tts1
./run.sh --skip_data_prep false --skip_train true --download_model imdanboy/jets
```
## TTS config
<details><summary>expand</summary>
```
config: conf/tuning/train_jets.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/tts_train_jets_raw_phn_tacotron_g2p_en_no_space
ngpu: 1
seed: 777
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 39471
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: true
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: false
collect_stats: false
write_collected_feats: false
max_epoch: 1000
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- text2mel_loss
- min
- - train
- text2mel_loss
- min
- - train
- total_count
- max
keep_nbest_models: 5
nbest_averaging_interval: 0
grad_clip: -1
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: 50
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: 1000
batch_size: 20
valid_batch_size: null
batch_bins: 3000000
valid_batch_bins: null
train_shape_file:
- exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/text_shape.phn
- exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/speech_shape
valid_shape_file:
- exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/valid/text_shape.phn
- exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/valid/speech_shape
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 204800
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/tr_no_dev/text
- text
- text
- - dump/raw/tr_no_dev/wav.scp
- speech
- sound
- - exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/collect_feats/pitch.scp
- pitch
- npy
- - exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/collect_feats/energy.scp
- energy
- npy
valid_data_path_and_name_and_type:
- - dump/raw/dev/text
- text
- text
- - dump/raw/dev/wav.scp
- speech
- sound
- - exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/valid/collect_feats/pitch.scp
- pitch
- npy
- - exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/valid/collect_feats/energy.scp
- energy
- npy
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adamw
optim_conf:
lr: 0.0002
betas:
- 0.8
- 0.99
eps: 1.0e-09
weight_decay: 0.0
scheduler: exponentiallr
scheduler_conf:
gamma: 0.999875
optim2: adamw
optim2_conf:
lr: 0.0002
betas:
- 0.8
- 0.99
eps: 1.0e-09
weight_decay: 0.0
scheduler2: exponentiallr
scheduler2_conf:
gamma: 0.999875
generator_first: true
token_list:
- <blank>
- <unk>
- AH0
- N
- T
- D
- S
- R
- L
- DH
- K
- Z
- IH1
- IH0
- M
- EH1
- W
- P
- AE1
- AH1
- V
- ER0
- F
- ','
- AA1
- B
- HH
- IY1
- UW1
- IY0
- AO1
- EY1
- AY1
- .
- OW1
- SH
- NG
- G
- ER1
- CH
- JH
- Y
- AW1
- TH
- UH1
- EH2
- OW0
- EY2
- AO0
- IH2
- AE2
- AY2
- AA2
- UW0
- EH0
- OY1
- EY0
- AO2
- ZH
- OW2
- AE0
- UW2
- AH2
- AY0
- IY2
- AW2
- AA0
- ''''
- ER2
- UH2
- '?'
- OY2
- '!'
- AW0
- UH0
- OY0
- ..
- <sos/eos>
odim: null
model_conf: {}
use_preprocessor: true
token_type: phn
bpemodel: null
non_linguistic_symbols: null
cleaner: tacotron
g2p: g2p_en_no_space
feats_extract: fbank
feats_extract_conf:
n_fft: 1024
hop_length: 256
win_length: null
fs: 22050
fmin: 80
fmax: 7600
n_mels: 80
normalize: global_mvn
normalize_conf:
stats_file: exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/feats_stats.npz
tts: jets
tts_conf:
generator_type: jets_generator
generator_params:
adim: 256
aheads: 2
elayers: 4
eunits: 1024
dlayers: 4
dunits: 1024
positionwise_layer_type: conv1d
positionwise_conv_kernel_size: 3
duration_predictor_layers: 2
duration_predictor_chans: 256
duration_predictor_kernel_size: 3
use_masking: true
encoder_normalize_before: true
decoder_normalize_before: true
encoder_type: transformer
decoder_type: transformer
conformer_rel_pos_type: latest
conformer_pos_enc_layer_type: rel_pos
conformer_self_attn_layer_type: rel_selfattn
conformer_activation_type: swish
use_macaron_style_in_conformer: true
use_cnn_in_conformer: true
conformer_enc_kernel_size: 7
conformer_dec_kernel_size: 31
init_type: xavier_uniform
transformer_enc_dropout_rate: 0.2
transformer_enc_positional_dropout_rate: 0.2
transformer_enc_attn_dropout_rate: 0.2
transformer_dec_dropout_rate: 0.2
transformer_dec_positional_dropout_rate: 0.2
transformer_dec_attn_dropout_rate: 0.2
pitch_predictor_layers: 5
pitch_predictor_chans: 256
pitch_predictor_kernel_size: 5
pitch_predictor_dropout: 0.5
pitch_embed_kernel_size: 1
pitch_embed_dropout: 0.0
stop_gradient_from_pitch_predictor: true
energy_predictor_layers: 2
energy_predictor_chans: 256
energy_predictor_kernel_size: 3
energy_predictor_dropout: 0.5
energy_embed_kernel_size: 1
energy_embed_dropout: 0.0
stop_gradient_from_energy_predictor: false
generator_out_channels: 1
generator_channels: 512
generator_global_channels: -1
generator_kernel_size: 7
generator_upsample_scales:
- 8
- 8
- 2
- 2
generator_upsample_kernel_sizes:
- 16
- 16
- 4
- 4
generator_resblock_kernel_sizes:
- 3
- 7
- 11
generator_resblock_dilations:
- - 1
- 3
- 5
- - 1
- 3
- 5
- - 1
- 3
- 5
generator_use_additional_convs: true
generator_bias: true
generator_nonlinear_activation: LeakyReLU
generator_nonlinear_activation_params:
negative_slope: 0.1
generator_use_weight_norm: true
segment_size: 64
idim: 78
odim: 80
discriminator_type: hifigan_multi_scale_multi_period_discriminator
discriminator_params:
scales: 1
scale_downsample_pooling: AvgPool1d
scale_downsample_pooling_params:
kernel_size: 4
stride: 2
padding: 2
scale_discriminator_params:
in_channels: 1
out_channels: 1
kernel_sizes:
- 15
- 41
- 5
- 3
channels: 128
max_downsample_channels: 1024
max_groups: 16
bias: true
downsample_scales:
- 2
- 2
- 4
- 4
- 1
nonlinear_activation: LeakyReLU
nonlinear_activation_params:
negative_slope: 0.1
use_weight_norm: true
use_spectral_norm: false
follow_official_norm: false
periods:
- 2
- 3
- 5
- 7
- 11
period_discriminator_params:
in_channels: 1
out_channels: 1
kernel_sizes:
- 5
- 3
channels: 32
downsample_scales:
- 3
- 3
- 3
- 3
- 1
max_downsample_channels: 1024
bias: true
nonlinear_activation: LeakyReLU
nonlinear_activation_params:
negative_slope: 0.1
use_weight_norm: true
use_spectral_norm: false
generator_adv_loss_params:
average_by_discriminators: false
loss_type: mse
discriminator_adv_loss_params:
average_by_discriminators: false
loss_type: mse
feat_match_loss_params:
average_by_discriminators: false
average_by_layers: false
include_final_outputs: true
mel_loss_params:
fs: 22050
n_fft: 1024
hop_length: 256
win_length: null
window: hann
n_mels: 80
fmin: 0
fmax: null
log_base: null
lambda_adv: 1.0
lambda_mel: 45.0
lambda_feat_match: 2.0
lambda_var: 1.0
lambda_align: 2.0
sampling_rate: 22050
cache_generator_outputs: true
pitch_extract: dio
pitch_extract_conf:
reduction_factor: 1
use_token_averaged_f0: false
fs: 22050
n_fft: 1024
hop_length: 256
f0max: 400
f0min: 80
pitch_normalize: global_mvn
pitch_normalize_conf:
stats_file: exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/pitch_stats.npz
energy_extract: energy
energy_extract_conf:
reduction_factor: 1
use_token_averaged_energy: false
fs: 22050
n_fft: 1024
hop_length: 256
win_length: null
energy_normalize: global_mvn
energy_normalize_conf:
stats_file: exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/energy_stats.npz
required:
- output_dir
- token_list
version: '202204'
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
samrawal/bert-large-uncased_med-ner
|
samrawal
| 2022-05-28T15:56:42Z
| 6,508
| 7
|
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z
|
A Named Entity Recognition model for medication entities (`medication name`, `dosage`, `duration`, `frequency`, `reason`).
The model has been trained on the i2b2 (now n2c2) dataset for the 2009 - Medication task. Please visit the n2c2 site to request access to the dataset.
|
Maaly/bgc-accession
|
Maaly
| 2022-05-28T15:34:44Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z
|
bgc-accession model is a Named Entity Recognition (NER) model that identifies and annotates the accession number of biosynthetic gene clusters in texts.
The model is a fine-tuned BioBERT model and the training dataset is available in https://gitlab.com/maaly7/emerald_bgcs_annotations
Testing examples:
1. The genome sequences of Leptolyngbya sp. PCC 7375 (ALVN00000000) and G. sunshinyii YC6258 (NZ_CP007142.1) were obtained previously.36,59
2. K311 was sequenced (NCBI accession number: JN852959) and analyzed with FramePlot and 18 genes were predicted to be involved in echinomycin biosynthesis (Figure 2).
3. The mar cluster was sequenced and annotated and the complete sequence was deposited into Genbank (accession KF711829).
|
theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3
|
theojolliffe
| 2022-05-28T14:46:08Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:scientific_papers",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-28T09:19:17Z
|
---
license: mit
tags:
- generated_from_trainer
datasets:
- scientific_papers
metrics:
- rouge
model-index:
- name: bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: scientific_papers
type: scientific_papers
args: pubmed
metrics:
- name: Rouge1
type: rouge
value: 37.5622
---
<!-- 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. -->
# bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3
This model is a fine-tuned version of [theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3](https://huggingface.co/theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3) on the scientific_papers dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8540
- Rouge1: 37.5622
- Rouge2: 15.5848
- Rougel: 23.1384
- Rougelsum: 34.2695
- Gen Len: 138.0326
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 1.9205 | 1.0 | 19987 | 1.8540 | 37.5622 | 15.5848 | 23.1384 | 34.2695 | 138.0326 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
sanbohork/Caso3_T5
|
sanbohork
| 2022-05-28T13:35:02Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-27T20:07:20Z
|
---
license: other
---
Este modelo busca generar el titulo de un texto, se tomo como base el articulo:
https://medium.com/nlplanet/a-full-guide-to-finetuning-t5-for-text2text-and-building-a-demo-with-streamlit-c72009631887
Se entreno el modelo con 500 elementos del dataset
Genera el titulo del texto
|
GioReg/dbmdzBERTnews
|
GioReg
| 2022-05-28T12:56:48Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-28T12:08:37Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: dbmdzBERTnews
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. -->
# dbmdzBERTnews
This model is a fine-tuned version of [dbmdz/bert-base-italian-uncased](https://huggingface.co/dbmdz/bert-base-italian-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0960
- Accuracy: 0.9733
- F1: 0.9730
## 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: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
CH0KUN/autotrain-TNC_Domain_WangchanBERTa-921730254
|
CH0KUN
| 2022-05-28T12:04:53Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"camembert",
"text-classification",
"autotrain",
"unk",
"dataset:CH0KUN/autotrain-data-TNC_Domain_WangchanBERTa",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-28T11:51:14Z
|
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- CH0KUN/autotrain-data-TNC_Domain_WangchanBERTa
co2_eq_emissions: 25.144394918865913
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 921730254
- CO2 Emissions (in grams): 25.144394918865913
## Validation Metrics
- Loss: 0.7080970406532288
- Accuracy: 0.7775925925925926
- Macro F1: 0.7758012615987406
- Micro F1: 0.7775925925925925
- Weighted F1: 0.7758012615987406
- Macro Precision: 0.7833307663368776
- Micro Precision: 0.7775925925925926
- Weighted Precision: 0.7833307663368777
- Macro Recall: 0.7775925925925926
- Micro Recall: 0.7775925925925926
- Weighted Recall: 0.7775925925925926
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/CH0KUN/autotrain-TNC_Domain_WangchanBERTa-921730254
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("CH0KUN/autotrain-TNC_Domain_WangchanBERTa-921730254", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("CH0KUN/autotrain-TNC_Domain_WangchanBERTa-921730254", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
Mugenor/q-FrozenLake-v1-4x4-noSlippery
|
Mugenor
| 2022-05-28T09:56:19Z
| 0
| 0
| null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-28T09:55:28Z
|
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Mugenor/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
KoichiYasuoka/deberta-small-coptic
|
KoichiYasuoka
| 2022-05-28T08:48:57Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"fill-mask",
"coptic",
"masked-lm",
"cop",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-28T08:45:35Z
|
---
language:
- "cop"
tags:
- "coptic"
- "masked-lm"
license: "cc-by-sa-4.0"
pipeline_tag: "fill-mask"
mask_token: "[MASK]"
---
# deberta-small-coptic
## Model Description
This is a DeBERTa(V2) model pre-trained on Coptic Scriptorium Corpora. You can fine-tune `deberta-small-coptic` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-small-coptic-upos), dependency-parsing, and so on.
## How to Use
```py
from transformers import AutoTokenizer,AutoModelForMaskedLM
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-small-coptic")
model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/deberta-small-coptic")
```
|
aioxlabs/dvoice-amharic
|
aioxlabs
| 2022-05-28T08:22:00Z
| 8
| 5
|
speechbrain
|
[
"speechbrain",
"wav2vec2",
"CTC",
"pytorch",
"Transformer",
"automatic-speech-recognition",
"dar",
"dataset:commonvoice",
"license:apache-2.0",
"region:us"
] |
automatic-speech-recognition
| 2022-05-26T12:41:35Z
|
---
language: "dar"
thumbnail:
pipeline_tag: automatic-speech-recognition
tags:
- CTC
- pytorch
- speechbrain
- Transformer
license: "apache-2.0"
datasets:
- commonvoice
metrics:
- wer
- cer
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on DVoice Amharic (No LM)
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on a [ALFFA](https://github.com/besacier/ALFFA_PUBLIC) Amharic dataset within
SpeechBrain. For a better experience, we encourage you to learn more about
[SpeechBrain](https://speechbrain.github.io).
| DVoice Release | Val. CER | Val. WER | Test CER | Test WER |
|:-------------:|:---------------------------:| -----:| -----:| -----:|
| v2.0 | 6.71 | 25.50 | 6.57 | 24.92 |
# Pipeline description
This ASR system is composed of 2 different but linked blocks:
- Tokenizer (unigram) that transforms words into subword units and trained with
the train transcriptions.
- Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)) is combined with two DNN layers and finetuned on the Darija dataset.
The obtained final acoustic representation is given to the CTC greedy decoder.
The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed.
# Install SpeechBrain
First of all, please install tranformers and SpeechBrain with the following command:
```
pip install speechbrain transformers
```
Please notice that we encourage you to read the SpeechBrain tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).
# Transcribing your own audio files (in Amharic)
```python
from speechbrain.pretrained import EncoderASR
asr_model = EncoderASR.from_hparams(source="aioxlabs/dvoice-amharic", savedir="pretrained_models/asr-wav2vec2-dvoice-amh")
asr_model.transcribe_file('./the_path_to_your_audio_file')
```
# Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
# Training
To train the model from scratch, please see our GitHub tutorial [here](https://github.com/AIOXLABS/DVoice).
# Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
# About DVoice
DVoice is a community initiative that aims to provide Africa low resources languages with data and models to facilitate their use of voice technologies. The lack of data on these languages makes it necessary to collect data using methods that are specific to each one. Two different approaches are currently used: the DVoice platforms ([https://dvoice.ma](https://dvoice.ma) and [https://dvoice.sn](https://dvoice.sn)), which are based on Mozilla Common Voice, for collecting authentic recordings from the community, and transfer learning techniques for automatically labeling recordings that are retrived from social medias. The DVoice platform currently manages 7 languages including Darija (Moroccan Arabic dialect) whose dataset appears on this version, Wolof, Mandingo, Serere, Pular, Diola and Soninke.
For this project, AIOX Labs the SI2M Laboratory are joining forces to build the future of technologies together.
# About AIOX Labs
Based in Rabat, London and Paris, AIOX-Labs mobilizes artificial intelligence technologies to meet the business needs and data projects of companies.
- He is at the service of the growth of groups, the optimization of processes or the improvement of the customer experience.
- AIOX-Labs is multi-sector, from fintech to industry, including retail and consumer goods.
- Business ready data products with a solid algorithmic base and adaptability for the specific needs of each client.
- A complementary team made up of doctors in AI and business experts with a solid scientific base and international publications.
Website: [https://www.aiox-labs.com/](https://www.aiox-labs.com/)
# SI2M Laboratory
The Information Systems, Intelligent Systems and Mathematical Modeling Research Laboratory (SI2M) is an academic research laboratory of the National Institute of Statistics and Applied Economics (INSEA). The research areas of the laboratories are Information Systems, Intelligent Systems, Artificial Intelligence, Decision Support, Network and System Security, Mathematical Modelling.
Website: [SI2M Laboratory](https://insea.ac.ma/index.php/pole-recherche/equipe-de-recherche/150-laboratoire-de-recherche-en-systemes-d-information-systemes-intelligents-et-modelisation-mathematique)
# About SpeechBrain
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.
Website: https://speechbrain.github.io/
GitHub: https://github.com/speechbrain/speechbrain
# Referencing SpeechBrain
```
@misc{SB2021,
author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
title = {SpeechBrain},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
}
```
# Acknowledgements
This research was supported through computational resources of HPC-MARWAN (www.marwan.ma/hpc) provided by CNRST, Rabat, Morocco. We deeply thank this institution.
|
aioxlabs/dvoice-fongbe
|
aioxlabs
| 2022-05-28T08:20:03Z
| 4
| 0
|
speechbrain
|
[
"speechbrain",
"wav2vec2",
"CTC",
"pytorch",
"Transformer",
"automatic-speech-recognition",
"fon",
"dataset:commonvoice",
"license:apache-2.0",
"region:us"
] |
automatic-speech-recognition
| 2022-05-26T14:34:58Z
|
---
language: "fon"
thumbnail:
pipeline_tag: automatic-speech-recognition
tags:
- CTC
- pytorch
- speechbrain
- Transformer
license: "apache-2.0"
datasets:
- commonvoice
metrics:
- wer
- cer
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on DVoice Fongbe (No LM)
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on a [ALFFA](https://github.com/besacier/ALFFA_PUBLIC) Fongbe dataset within
SpeechBrain. For a better experience, we encourage you to learn more about
[SpeechBrain](https://speechbrain.github.io).
| DVoice Release | Val. CER | Val. WER | Test CER | Test WER |
|:-------------:|:---------------------------:| -----:| -----:| -----:|
| v2.0 | 4.16 | 9.19 | 3.98 | 9.00 |
# Pipeline description
This ASR system is composed of 2 different but linked blocks:
- Tokenizer (unigram) that transforms words into subword units and trained with
the train transcriptions.
- Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)) is combined with two DNN layers and finetuned on the Darija dataset.
The obtained final acoustic representation is given to the CTC greedy decoder.
The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed.
# Install SpeechBrain
First of all, please install tranformers and SpeechBrain with the following command:
```
pip install speechbrain transformers
```
Please notice that we encourage you to read the SpeechBrain tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).
# Transcribing your own audio files (in Fongbe)
```python
from speechbrain.pretrained import EncoderASR
asr_model = EncoderASR.from_hparams(source="aioxlabs/dvoice-fongbe", savedir="pretrained_models/asr-wav2vec2-dvoice-fon")
asr_model.transcribe_file('./the_path_to_your_audio_file')
```
# Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
# Training
To train the model from scratch, please see our GitHub tutorial [here](https://github.com/AIOXLABS/DVoice).
# Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
# About DVoice
DVoice is a community initiative that aims to provide Africa low resources languages with data and models to facilitate their use of voice technologies. The lack of data on these languages makes it necessary to collect data using methods that are specific to each one. Two different approaches are currently used: the DVoice platforms ([https://dvoice.ma](https://dvoice.ma) and [https://dvoice.sn](https://dvoice.sn)), which are based on Mozilla Common Voice, for collecting authentic recordings from the community, and transfer learning techniques for automatically labeling recordings that are retrived from social medias. The DVoice platform currently manages 7 languages including Darija (Moroccan Arabic dialect) whose dataset appears on this version, Wolof, Mandingo, Serere, Pular, Diola and Soninke.
For this project, AIOX Labs the SI2M Laboratory are joining forces to build the future of technologies together.
# About AIOX Labs
Based in Rabat, London and Paris, AIOX-Labs mobilizes artificial intelligence technologies to meet the business needs and data projects of companies.
- He is at the service of the growth of groups, the optimization of processes or the improvement of the customer experience.
- AIOX-Labs is multi-sector, from fintech to industry, including retail and consumer goods.
- Business ready data products with a solid algorithmic base and adaptability for the specific needs of each client.
- A complementary team made up of doctors in AI and business experts with a solid scientific base and international publications.
Website: [https://www.aiox-labs.com/](https://www.aiox-labs.com/)
# SI2M Laboratory
The Information Systems, Intelligent Systems and Mathematical Modeling Research Laboratory (SI2M) is an academic research laboratory of the National Institute of Statistics and Applied Economics (INSEA). The research areas of the laboratories are Information Systems, Intelligent Systems, Artificial Intelligence, Decision Support, Network and System Security, Mathematical Modelling.
Website: [SI2M Laboratory](https://insea.ac.ma/index.php/pole-recherche/equipe-de-recherche/150-laboratoire-de-recherche-en-systemes-d-information-systemes-intelligents-et-modelisation-mathematique)
# About SpeechBrain
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.
Website: https://speechbrain.github.io/
GitHub: https://github.com/speechbrain/speechbrain
# Referencing SpeechBrain
```
@misc{SB2021,
author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
title = {SpeechBrain},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
}
```
# Acknowledgements
This research was supported through computational resources of HPC-MARWAN (www.marwan.ma/hpc) provided by CNRST, Rabat, Morocco. We deeply thank this institution.
|
Yah216/Arabic_poem_meter_3
|
Yah216
| 2022-05-28T07:59:10Z
| 22
| 1
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-26T20:45:27Z
|
---
---
language: ar
widget:
- text: "قفا نبك من ذِكرى حبيب ومنزلِ بسِقطِ اللِّوى بينَ الدَّخول فحَوْملِ"
- text: "سَلو قَلبي غَداةَ سَلا وَثابا لَعَلَّ عَلى الجَمالِ لَهُ عِتابا"
co2_eq_emissions: 404.66986451902227
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- CO2 Emissions (in grams): 404.66986451902227
## Dataset
We used the APCD dataset cited hereafter for pretraining the model. The dataset has been cleaned and only the main text and the meter columns were kept:
```
@Article{Yousef2019LearningMetersArabicEnglish-arxiv,
author = {Yousef, Waleed A. and Ibrahime, Omar M. and Madbouly, Taha M. and Mahmoud,
Moustafa A.},
title = {Learning Meters of Arabic and English Poems With Recurrent Neural Networks: a Step
Forward for Language Understanding and Synthesis},
journal = {arXiv preprint arXiv:1905.05700},
year = 2019,
url = {https://github.com/hci-lab/LearningMetersPoems}
}
```
## Validation Metrics
- Loss: 0.21315555274486542
- Accuracy: 0.9493554089595999
- Macro F1: 0.7537353091512587
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "قفا نبك من ذِكرى حبيب ومنزلِ بسِقطِ اللِّوى بينَ الدَّخول فحَوْملِ"}' https://api-inference.huggingface.co/models/Yah216/Arabic_poem_meter_3
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Yah216/Arabic_poem_meter_3", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Yah216/Arabic_poem_meter_3", use_auth_token=True)
inputs = tokenizer("قفا نبك من ذِكرى حبيب ومنزلِ بسِقطِ اللِّوى بينَ الدَّخول فحَوْملِ", return_tensors="pt")
outputs = model(**inputs)
```
|
egesko/CodeSprint_DCGAN
|
egesko
| 2022-05-28T06:23:00Z
| 0
| 0
| null |
[
"license:mit",
"region:us"
] | null | 2022-05-28T05:19:07Z
|
---
license: mit
---
# DCGAN to generate face images
This trained model is a keras implementation of DCGAN that is trained on face images.
|
PDRES/roberta-base-bne-finetuned-amazon_reviews_multi
|
PDRES
| 2022-05-28T06:21:35Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-28T06:10:26Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: roberta-base-bne-finetuned-amazon_reviews_multi
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. -->
# roberta-base-bne-finetuned-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi 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: 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: 2
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
gary109/ai-light-dance_singing_ft_wav2vec2-large-lv60-v2
|
gary109
| 2022-05-28T05:50:54Z
| 4
| 1
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"../AI_Light_Dance.py",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-18T00:15:52Z
|
---
license: apache-2.0
tags:
- automatic-speech-recognition
- ../AI_Light_Dance.py
- generated_from_trainer
model-index:
- name: ai-light-dance_singing_ft_wav2vec2-large-lv60-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. -->
# ai-light-dance_singing_ft_wav2vec2-large-lv60-v2
This model is a fine-tuned version of [gary109/ai-light-dance_singing_ft_wav2vec2-large-lv60](https://huggingface.co/gary109/ai-light-dance_singing_ft_wav2vec2-large-lv60) on the ../AI_LIGHT_DANCE.PY - ONSET-SINGING dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4285
- Wer: 0.1858
## 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: 3e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.2775 | 1.0 | 1106 | 0.4372 | 0.2117 |
| 0.2154 | 2.0 | 2212 | 0.4474 | 0.2044 |
| 0.2023 | 3.0 | 3318 | 0.4372 | 0.1920 |
| 0.186 | 4.0 | 4424 | 0.4285 | 0.1858 |
| 0.1856 | 5.0 | 5530 | 0.4589 | 0.1826 |
| 0.1537 | 6.0 | 6636 | 0.4658 | 0.1774 |
| 0.1337 | 7.0 | 7742 | 0.4769 | 0.1744 |
| 0.108 | 8.0 | 8848 | 0.4604 | 0.1724 |
| 0.1593 | 9.0 | 9954 | 0.4731 | 0.1694 |
| 0.0904 | 10.0 | 11060 | 0.4843 | 0.1683 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.2.2.dev0
- Tokenizers 0.12.1
|
Khalsuu/english-filipino-wav2vec2-l-xls-r-test-05
|
Khalsuu
| 2022-05-28T05:23:04Z
| 7
| 1
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:filipino_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-06T23:33:46Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- filipino_voice
model-index:
- name: english-filipino-wav2vec2-l-xls-r-test-05
results: []
---
# english-filipino-wav2vec2-l-xls-r-test-05
## Model description
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the filipino_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4738
- Wer: 0.2684
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- 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: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.3328 | 2.09 | 400 | 2.2174 | 0.9733 |
| 0.6432 | 4.19 | 800 | 0.3735 | 0.3896 |
| 0.2741 | 6.28 | 1200 | 0.3639 | 0.3425 |
| 0.1877 | 8.38 | 1600 | 0.3506 | 0.3425 |
| 0.1408 | 10.47 | 2000 | 0.3644 | 0.3181 |
| 0.1133 | 12.57 | 2400 | 0.3837 | 0.3047 |
| 0.0953 | 14.66 | 2800 | 0.4415 | 0.3103 |
| 0.0814 | 16.75 | 3200 | 0.3940 | 0.3092 |
| 0.0707 | 18.85 | 3600 | 0.4164 | 0.3013 |
| 0.059 | 20.94 | 4000 | 0.4488 | 0.2983 |
| 0.0545 | 23.04 | 4400 | 0.4803 | 0.3028 |
| 0.0482 | 25.13 | 4800 | 0.4731 | 0.2811 |
| 0.0426 | 27.23 | 5200 | 0.4606 | 0.2757 |
| 0.0395 | 29.32 | 5600 | 0.4738 | 0.2684 |
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
vebie91/q-Taxi-v3
|
vebie91
| 2022-05-28T03:47:32Z
| 0
| 0
| null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-28T03:47:26Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="vebie91/q-Taxi-v3", 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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
vebie91/q-FrozenLake-v1-4x4-noSlippery
|
vebie91
| 2022-05-28T03:39:58Z
| 0
| 0
| null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-28T03:39:51Z
|
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="vebie91/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Julietheg/checkpoint-1000
|
Julietheg
| 2022-05-28T00:57:02Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-28T00:31:52Z
|
---
tags:
- generated_from_keras_callback
model-index:
- name: checkpoint-1000
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. -->
# checkpoint-1000
This model was trained from scratch 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
stevemobs/deberta-base-combined-squad1-aqa-newsqa
|
stevemobs
| 2022-05-28T00:45:46Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-25T22:59:19Z
|
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: deberta-base-combined-squad1-aqa-newsqa
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. -->
# deberta-base-combined-squad1-aqa-newsqa
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8860
## 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: 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.8812 | 1.0 | 40819 | 0.8762 |
| 0.6043 | 2.0 | 81638 | 0.8860 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
pyf98/slurp_entity_conformer
|
pyf98
| 2022-05-28T00:32:51Z
| 1
| 0
|
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:slurp_entity",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-05-28T00:11:15Z
|
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- slurp_entity
license: cc-by-4.0
---
## ESPnet2 ASR model
### `pyf98/slurp_entity_conformer`
This model was trained by Yifan Peng using slurp_entity recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 55b6cc387fd0252d1a06db2042fd101bcea7bb34
pip install -e .
cd egs2/slurp_entity/asr1
./run.sh --skip_data_prep false --skip_train true --download_model pyf98/slurp_entity_conformer
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Thu May 26 14:51:29 EDT 2022`
- python version: `3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]`
- espnet version: `espnet 202204`
- pytorch version: `pytorch 1.11.0`
- Git hash: `4f36236ed7c8a25c2f869e518614e1ad4a8b50d6`
- Commit date: `Thu May 26 00:22:45 2022 -0400`
## asr_train_asr_conformer_e12_d6_size512_lr1e-3_warmup35k_raw_en_word
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave_10best/devel|8690|178058|82.9|7.8|9.3|2.7|19.8|51.5|
|decode_asr_asr_model_valid.acc.ave_10best/test|13078|262176|81.9|7.8|10.3|2.6|20.7|50.7|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave_10best/devel|8690|847400|89.4|3.1|7.5|3.1|13.7|51.5|
|decode_asr_asr_model_valid.acc.ave_10best/test|13078|1245475|88.4|3.1|8.5|3.0|14.6|50.7|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_conformer_e12_d6_size512_lr1e-3_warmup35k.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer_e12_d6_size512_lr1e-3_warmup35k_raw_en_word
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 64
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_word/train/speech_shape
- exp/asr_stats_raw_en_word/train/text_shape.word
valid_shape_file:
- exp/asr_stats_raw_en_word/valid/speech_shape
- exp/asr_stats_raw_en_word/valid/text_shape.word
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train/wav.scp
- speech
- kaldi_ark
- - dump/raw/train/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/devel/wav.scp
- speech
- kaldi_ark
- - dump/raw/devel/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.001
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 35000
token_list:
- <blank>
- <unk>
- ▁SEP
- ▁FILL
- s
- ▁the
- a
- ▁to
- ▁i
- ▁me
- e
- ▁s
- ▁a
- i
- ▁you
- ▁what
- er
- ing
- u
- ▁is
- ''''
- o
- p
- ▁in
- ▁p
- y
- ▁my
- ▁please
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- c
- m
- ▁b
- l
- ▁m
- ▁c
- st
- date
- n
- ▁d
- le
- b
- ▁for
- re
- t
- ▁on
- en
- h
- 'on'
- ar
- person
- ▁re
- ▁f
- ▁g
- ▁of
- an
- ▁
- g
- ▁today
- ▁t
- or
- ▁it
- ▁this
- ▁h
- r
- f
- at
- ch
- ce
- place_name
- ▁email
- ▁do
- es
- ri
- ▁e
- ▁w
- ic
- in
- ▁that
- event_name
- ▁play
- ▁and
- al
- ▁n
- ▁can
- email_query
- ve
- ▁new
- day
- it
- ate
- ▁from
- ▁have
- k
- time
- ▁am
- media_type
- email_sendemail
- ent
- ▁olly
- qa_factoid
- se
- v
- et
- ck
- ▁any
- calendar_set
- ly
- th
- ▁how
- ▁meeting
- ed
- ▁tell
- ▁st
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- ur
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- ▁at
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- ▁not
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- ▁an
- ▁o
- general_negate
- ut
- ▁time
- ▁be
- ▁ch
- ▁are
- social_post
- business_name
- la
- ty
- play_music
- ot
- general_quirky
- ▁l
- ▁sh
- ▁tweet
- om
- ▁week
- um
- ▁one
- ter
- ▁he
- ▁up
- ▁com
- general_praise
- weather_query
- ▁next
- ▁th
- ▁check
- calendar_query
- ▁last
- ▁ro
- ad
- is
- ▁with
- ay
- ▁send
- pe
- ▁pm
- ▁tomorrow
- ▁j
- un
- ▁train
- general_explain
- ▁v
- one
- ▁r
- ra
- news_query
- ation
- ▁emails
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- if
- ct
- ▁co
- ▁add
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- ▁se
- nt
- ▁was
- ine
- ▁de
- ▁set
- ▁ex
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- ▁again
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- currency_name
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- ▁ha
- ▁go
- relation
- te
- ion
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- ▁y
- ▁ye
- general_affirm
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- ery
- ▁po
- ff
- ▁we
- ▁turn
- ▁did
- ▁mar
- ▁alarm
- ▁like
- datetime_query
- ers
- ▁all
- ▁remind
- ▁so
- qa_definition
- ▁calendar
- end
- ▁said
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- ▁off
- ▁john
- ▁day
- ss
- pla
- ume
- ▁get
- ail
- pp
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- ry
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- ▁need
- as
- ▁thank
- ▁wh
- ▁want
- ▁right
- ▁jo
- ▁facebook
- ▁k
- ge
- ld
- ▁fri
- ▁two
- general_dontcare
- ▁news
- ol
- oo
- ant
- ▁five
- ▁event
- ake
- definition_word
- transport_type
- ▁your
- vi
- orn
- op
- ▁weather
- ome
- ▁app
- ▁lo
- de
- ▁music
- weather_descriptor
- ak
- ke
- ▁there
- ▁si
- ▁lights
- ▁now
- ▁mo
- calendar_remove
- our
- ▁dollar
- food_type
- me
- ▁more
- ▁no
- ▁birthday
- orrect
- ▁rep
- ▁show
- play_radio
- ▁mon
- ▁does
- ood
- ag
- li
- ▁sto
- ▁contact
- cket
- email_querycontact
- ▁ev
- ▁could
- ange
- ▁just
- out
- ame
- .
- ▁ja
- ▁confirm
- qa_currency
- ▁man
- ▁late
- ▁think
- ▁some
- timeofday
- ▁bo
- qa_stock
- ong
- ▁start
- ▁work
- ▁ten
- int
- ▁command
- all
- ▁make
- ▁la
- j
- ▁answ
- ▁hour
- ▁cle
- ah
- ▁find
- ▁service
- ▁fa
- qu
- general_commandstop
- ai
- ▁when
- ▁te
- ▁by
- social_query
- ard
- ▁tw
- ul
- id
- ▁seven
- ▁where
- ▁much
- art
- ▁appointment
- ver
- artist_name
- el
- device_type
- ▁know
- ▁three
- ▁events
- ▁tr
- ▁li
- ork
- red
- ect
- ▁let
- ▁respon
- ▁par
- zz
- ▁give
- ▁twenty
- ▁ti
- ▁curre
- play_podcasts
- ▁radio
- cooking_recipe
- transport_query
- ▁con
- gh
- ▁le
- lists_query
- ▁rem
- recommendation_events
- house_place
- alarm_set
- play_audiobook
- ist
- ase
- music_genre
- ive
- ast
- player_setting
- ort
- lly
- news_topic
- list_name
- ▁playlist
- ▁ne
- business_type
- personal_info
- ind
- ust
- di
- ress
- recommendation_locations
- lists_createoradd
- iot_hue_lightoff
- lists_remove
- ord
- ▁light
- ere
- alarm_query
- audio_volume_mute
- music_query
- ▁audio
- rain
- ▁date
- ▁order
- audio_volume_up
- ▁ar
- ▁podcast
- transport_ticket
- mail
- iot_hue_lightchange
- iot_coffee
- radio_name
- ill
- ▁ri
- '@'
- takeaway_query
- song_name
- takeaway_order
- ▁ra
- email_addcontact
- play_game
- book
- transport_traffic
- ▁house
- music_likeness
- her
- transport_taxi
- iot_hue_lightdim
- ment
- ght
- fo
- order_type
- color_type
- '1'
- ven
- ould
- general_joke
- ess
- ain
- qa_maths
- ▁place
- ▁twe
- cast
- iot_cleaning
- ▁che
- ▁cont
- ith
- audiobook_name
- email_address
- game_name
- ▁cal
- general_frequency
- ▁tom
- ▁food
- act
- iot_hue_lightup
- '2'
- alarm_remove
- podcast_descriptor
- ▁definition
- audio_volume_down
- ▁media
- email_folder
- dia
- meal_type
- ▁mus
- recommendation_movies
- ▁ad
- ree
- pt
- now
- playlist_name
- ▁person
- change_amount
- ▁pla
- escri
- datetime_convert
- podcast_name
- ▁ab
- time_zone
- ▁def
- ting
- iot_wemo_on
- music_settings
- iot_wemo_off
- orre
- cy
- ank
- music_descriptor
- lar
- app_name
- row
- joke_type
- xt
- of
- ition
- ▁meet
- ink
- ▁confir
- transport_agency
- general_greet
- ▁business
- ▁art
- ▁ag
- urn
- escript
- rom
- ▁rel
- ▁au
- ▁currency
- audio_volume_other
- iot_hue_lighton
- ▁artist
- '?'
- ▁bus
- cooking_type
- movie_name
- coffee_type
- ingredient
- ather
- music_dislikeness
- sp
- q
- ▁ser
- esc
- ▁bir
- ▁cur
- name
- ▁tran
- ▁hou
- ek
- uch
- ▁conf
- ▁face
- '9'
- ▁birth
- I
- sw
- transport_descriptor
- ▁comm
- lease
- transport_name
- aid
- movie_type
- ▁device
- alarm_type
- audiobook_author
- '5'
- drink_type
- ▁joh
- ▁defin
- word
- ▁curren
- order
- iness
- W
- cooking_query
- sport_type
- ▁relation
- oint
- H
- '8'
- A
- '0'
- ▁dol
- vice
- ▁pers
- '&'
- T
- ▁appoint
- _
- '7'
- '3'
- '-'
- game_type
- ▁pod
- N
- M
- E
- list
- music_album
- dio
- ▁transport
- qa_query
- C
- O
- U
- query_detail
- ']'
- '['
- descriptor
- ':'
- spon
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
use_preprocessor: true
token_type: word
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
model: espnet
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
extract_feats_in_collect_stats: false
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
output_size: 512
attention_heads: 8
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
macaron_style: true
rel_pos_type: latest
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 8
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
required:
- output_dir
- token_list
version: '202204'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
fastai/fastbook_02_bears_classifier
|
fastai
| 2022-05-28T00:18:24Z
| 0
| 0
|
fastai
|
[
"fastai",
"image-classification",
"license:gpl-3.0",
"region:us"
] |
image-classification
| 2022-04-17T12:17:41Z
|
---
license: gpl-3.0
tags:
- fastai
- image-classification
---
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using the 🤗Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join our fastai community on the Hugging Face Discord!
Greetings fellow fastlearner 🤝!
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
tbosse/bert-base-german-cased-finetuned-subj_preTrained_with_noisyData_v1.1
|
tbosse
| 2022-05-28T00:01:57Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-27T19:38:54Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-german-cased-finetuned-subj_preTrained_with_noisyData_v1.1
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. -->
# bert-base-german-cased-finetuned-subj_preTrained_with_noisyData_v1.1
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0179
- Precision: 0.9249
- Recall: 0.8776
- F1: 0.9006
- Accuracy: 0.9942
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 245 | 0.0244 | 0.9252 | 0.8120 | 0.8649 | 0.9924 |
| No log | 2.0 | 490 | 0.0179 | 0.9249 | 0.8776 | 0.9006 | 0.9942 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
magitz/q-FrozenLake-v1-4x4-noSlippery
|
magitz
| 2022-05-27T23:38:13Z
| 0
| 0
| null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-27T23:38:07Z
|
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="magitz/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
merve/model-card-history-removal
|
merve
| 2022-05-27T22:30:55Z
| 0
| 0
|
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2022-05-27T22:30:46Z
|
---
library_name: keras
---
## 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': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
huggingtweets/algodtrading
|
huggingtweets
| 2022-05-27T22:21:11Z
| 3
| 1
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-27T22:20:16Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/algodtrading/1653690066290/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1509493999987474434/nB7rOJnT_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Algod🫐</div>
<div style="text-align: center; font-size: 14px;">@algodtrading</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Algod🫐.
| Data | Algod🫐 |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 56 |
| Short tweets | 391 |
| Tweets kept | 2802 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3mz6oljo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @algodtrading's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1oouvcmj) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1oouvcmj/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/algodtrading')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
jplu/adel-dbpedia-retrieval
|
jplu
| 2022-05-27T21:58:29Z
| 3
| 0
|
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-05-27T21:59:39Z
|
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
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('{MODEL_NAME}')
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
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# 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('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# 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, mean pooling.
sentence_embeddings = mean_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={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 71 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`beir.losses.margin_mse_loss.MarginMSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 11,
"evaluation_steps": 10000,
"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": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
coreybrady/coreyresults
|
coreybrady
| 2022-05-27T19:38:53Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-26T23:42:59Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: coreyresults
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. -->
# coreyresults
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) 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: 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: 3
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3
|
theojolliffe
| 2022-05-27T18:59:12Z
| 5
| 1
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:scientific_papers",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-27T13:34:57Z
|
---
license: mit
tags:
- generated_from_trainer
datasets:
- scientific_papers
metrics:
- rouge
model-index:
- name: bart-large-cnn-pubmed1o3-pubmed2o3
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: scientific_papers
type: scientific_papers
args: pubmed
metrics:
- name: Rouge1
type: rouge
value: 37.4586
---
<!-- 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. -->
# bart-large-cnn-pubmed1o3-pubmed2o3
This model is a fine-tuned version of [theojolliffe/bart-large-cnn-pubmed1o3](https://huggingface.co/theojolliffe/bart-large-cnn-pubmed1o3) on the scientific_papers dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8817
- Rouge1: 37.4586
- Rouge2: 15.5572
- Rougel: 23.0686
- Rougelsum: 34.1522
- Gen Len: 138.379
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.9586 | 1.0 | 19988 | 1.8817 | 37.4586 | 15.5572 | 23.0686 | 34.1522 | 138.379 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
pyf98/aishell_conformer_e12_amp
|
pyf98
| 2022-05-27T18:55:49Z
| 3
| 0
|
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"zh",
"dataset:aishell",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-05-27T18:41:59Z
|
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: zh
datasets:
- aishell
license: cc-by-4.0
---
## ESPnet2 ASR model
### `pyf98/aishell_conformer_e12_amp`
This model was trained by Yifan Peng using aishell recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 4f36236ed7c8a25c2f869e518614e1ad4a8b50d6
pip install -e .
cd egs2/aishell/asr1
./run.sh --skip_data_prep false --skip_train true --download_model pyf98/aishell_conformer_e12_amp
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Fri May 27 13:37:48 EDT 2022`
- python version: `3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]`
- espnet version: `espnet 202204`
- pytorch version: `pytorch 1.11.0`
- Git hash: `4f36236ed7c8a25c2f869e518614e1ad4a8b50d6`
- Commit date: `Thu May 26 00:22:45 2022 -0400`
## asr_train_asr_conformer_e12_amp_raw_zh_char_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|beam10_ctc0.4/dev|14326|14326|66.9|33.1|0.0|0.0|33.1|33.1|
|beam10_ctc0.4/test|7176|7176|65.3|34.7|0.0|0.0|34.7|34.7|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|beam10_ctc0.4/dev|14326|205341|95.8|4.1|0.1|0.1|4.3|33.1|
|beam10_ctc0.4/test|7176|104765|95.4|4.4|0.1|0.1|4.6|34.7|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_conformer_e12_amp.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer_e12_amp_raw_zh_char_sp
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 57687
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 60
patience: null
val_scheduler_criterion:
- valid
- acc
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 25000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_zh_char_sp/train/speech_shape
- exp/asr_stats_raw_zh_char_sp/train/text_shape.char
valid_shape_file:
- exp/asr_stats_raw_zh_char_sp/valid/speech_shape
- exp/asr_stats_raw_zh_char_sp/valid/text_shape.char
batch_type: numel
valid_batch_type: null
fold_length:
- 51200
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_sp/wav.scp
- speech
- kaldi_ark
- - dump/raw/train_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/wav.scp
- speech
- kaldi_ark
- - dump/raw/dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.001
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 35000
token_list:
- <blank>
- <unk>
- 的
- 一
- 在
- 十
- 中
- 是
- 人
- 有
- 二
- 上
- 了
- 不
- 国
- 市
- 大
- 业
- 为
- 年
- 三
- 发
- 个
- 分
- 出
- 会
- 公
- 行
- 地
- 成
- 这
- 和
- 到
- 五
- 产
- 时
- 对
- 房
- 百
- 能
- 场
- 来
- 以
- 新
- 之
- 日
- 者
- 将
- 现
- 四
- 要
- 家
- 资
- 多
- 月
- 也
- 方
- 后
- 机
- 下
- 前
- 零
- 比
- 于
- 生
- 点
- 开
- 动
- 高
- 经
- 进
- 报
- 体
- 赛
- 子
- 万
- 车
- 用
- 金
- 司
- 可
- 被
- 过
- 手
- 本
- 作
- 自
- 全
- 八
- 六
- 最
- 价
- 目
- 电
- 部
- 交
- 九
- 七
- 面
- 我
- 企
- 加
- 小
- 度
- 实
- 同
- 城
- 工
- 其
- 力
- 定
- 而
- 元
- 合
- 已
- 内
- 与
- 法
- 还
- 关
- 网
- 得
- 他
- 就
- 入
- 名
- 品
- 女
- 记
- 理
- 事
- 长
- 两
- 商
- 都
- 们
- 京
- 并
- 但
- 平
- 制
- 保
- 据
- 期
- 化
- 主
- 重
- 表
- 次
- 相
- 量
- 通
- 道
- 政
- 所
- 天
- 第
- 利
- 间
- 海
- 数
- 务
- 提
- 北
- 展
- 员
- 管
- 投
- 因
- 建
- 好
- 外
- 区
- 更
- 示
- 增
- 从
- 计
- 信
- 性
- 等
- 运
- 项
- 应
- 当
- 收
- 位
- 着
- 起
- 学
- 台
- 民
- 持
- 规
- 设
- 明
- 股
- 正
- 没
- 心
- 然
- 很
- 今
- 调
- 去
- 安
- 此
- 东
- 队
- 如
- 线
- 科
- 世
- 无
- 达
- 身
- 果
- 证
- 基
- 受
- 男
- 需
- 标
- 布
- 情
- 格
- 近
- 步
- 未
- 费
- 求
- 式
- 消
- 千
- 美
- 些
- 里
- 米
- 向
- 看
- 续
- 息
- 意
- 接
- 门
- 回
- 及
- 销
- 老
- 获
- 总
- 监
- 打
- 联
- 至
- 亿
- 说
- 讯
- 住
- 环
- 件
- 整
- 水
- 技
- 路
- 院
- 局
- 特
- 该
- 统
- 由
- 售
- 购
- 强
- 改
- 问
- 乐
- 楼
- 涨
- 处
- 决
- 让
- 系
- 户
- 题
- 推
- 少
- 广
- 显
- 降
- 跑
- 影
- 只
- 选
- 称
- 创
- 易
- 战
- 首
- 完
- 案
- 策
- 常
- 查
- 参
- 种
- 牌
- 程
- 银
- 备
- 认
- 营
- 立
- 势
- 结
- 造
- 超
- 己
- 准
- 存
- 险
- 球
- 各
- 代
- 低
- 再
- 做
- 级
- 款
- 放
- 物
- 告
- 原
- 友
- 转
- 警
- 周
- 界
- 张
- 样
- 传
- 较
- 风
- 单
- 给
- 她
- 州
- 解
- 则
- 视
- 指
- 预
- 升
- 华
- 供
- 走
- 每
- 取
- 导
- 搜
- 集
- 文
- 变
- 客
- 排
- 片
- 头
- 任
- 积
- 术
- 率
- 型
- 军
- 斯
- 研
- 别
- 非
- 直
- 智
- 速
- 组
- 星
- 领
- 口
- 份
- 岁
- 马
- 王
- 快
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- 掠
- 陡
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- 脍
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
use_preprocessor: true
token_type: char
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 10
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_zh_char_sp/train/feats_stats.npz
model: espnet
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
output_size: 256
attention_heads: 4
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
rel_pos_type: latest
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
macaron_style: true
use_cnn_module: true
cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
required:
- output_dir
- token_list
version: '202204'
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Yah216/DistilBERT-finetuned-ACDP
|
Yah216
| 2022-05-27T18:43:32Z
| 5
| 0
|
transformers
|
[
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-27T18:34:52Z
|
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Yah216/DistilBERT-finetuned-ACDP
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. -->
# Yah216/DistilBERT-finetuned-ACDP
This model is a fine-tuned version of [CAMeL-Lab/bert-base-arabic-camelbert-ca](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 6.6178
- Validation Loss: 6.2483
- 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 8.7636 | 7.6558 | 0 |
| 7.2645 | 6.7607 | 1 |
| 6.6178 | 6.2483 | 2 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
LookParOf/q-FrozenLake-v1-4x4-Slippery
|
LookParOf
| 2022-05-27T17:16:16Z
| 0
| 0
| null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-27T16:25:34Z
|
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery
results:
- metrics:
- type: mean_reward
value: 0.71 +/- 0.45
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="LookParOf/q-FrozenLake-v1-4x4-Slippery", 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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
PraveenKishore/intro-ppo-lunarlander-v2
|
PraveenKishore
| 2022-05-27T17:01:58Z
| 1
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-27T15:47:12Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 283.46 +/- 13.89
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
...
```
|
facebook/wav2vec2-base-100h
|
facebook
| 2022-05-27T16:32:50Z
| 1,541
| 6
|
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z
|
---
language: en
datasets:
- librispeech_asr
tags:
- audio
- automatic-speech-recognition
license: apache-2.0
---
# Wav2Vec2-Base-100h
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
[Paper](https://arxiv.org/abs/2006.11477)
Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli
**Abstract**
We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.
The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import soundfile as sf
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-100h")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-100h")
# define function to read in sound file
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
ds = ds.map(map_to_array)
# tokenize
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values # Batch size 1
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
## Evaluation
This code snippet shows how to evaluate **facebook/wav2vec2-base-100h** on LibriSpeech's "clean" and "other" test data.
```python
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import soundfile as sf
import torch
from jiwer import wer
librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-100h").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-100h")
def map_to_pred(batch):
input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values
with torch.no_grad():
logits = model(input_values.to("cuda")).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
batch["transcription"] = transcription
return batch
result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["speech"])
print("WER:", wer(result["text"], result["transcription"]))
```
*Result (WER)*:
| "clean" | "other" |
|---|---|
| 6.1 | 13.5 |
|
Satyamatury/wav2vec2-large-xls-r-300m-turkish-colab
|
Satyamatury
| 2022-05-27T16:28:53Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-06T16:33:35Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab
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. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
meln1k/ppo-MountainCar-v0
|
meln1k
| 2022-05-27T15:58:51Z
| 1
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-27T15:57:28Z
|
---
library_name: stable-baselines3
tags:
- MountainCar-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -200.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MountainCar-v0
type: MountainCar-v0
---
# **PPO** Agent playing **MountainCar-v0**
This is a trained model of a **PPO** agent playing **MountainCar-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
skyfox/q-Taxi-v3
|
skyfox
| 2022-05-27T14:07:37Z
| 0
| 0
| null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-27T14:07:32Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="/q-Taxi-v3", 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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
esh/q-Taxi-v3
|
esh
| 2022-05-27T14:07:28Z
| 0
| 0
| null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-27T14:07:10Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: nan +/- nan
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="esh/q-Taxi-v3", 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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
esh/q-FrozenLake-v1-8x8-slippery
|
esh
| 2022-05-27T14:05:27Z
| 0
| 0
| null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T15:32:26Z
|
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-slippery
results:
- metrics:
- type: mean_reward
value: nan +/- nan
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="esh/q-FrozenLake-v1-8x8-slippery", 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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
onewithnickelcoins/roberta-base-stars
|
onewithnickelcoins
| 2022-05-27T13:15:43Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-27T12:33:44Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-stars
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. -->
# roberta-base-stars
This model is a fine-tuned version of [onewithnickelcoins/roberta-base-MLM](https://huggingface.co/onewithnickelcoins/roberta-base-MLM) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2914
- Accuracy: 0.6857
## 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
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
|
jkhan447/language-detection-Bert-base-uncased-additional
|
jkhan447
| 2022-05-27T13:02:32Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-27T09:28:22Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: language-detection-Bert-base-uncased-additional
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. -->
# language-detection-Bert-base-uncased-additional
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2330
- Accuracy: 0.9497
## 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: 50
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
YaYaB/q-Taxi-v3
|
YaYaB
| 2022-05-27T12:49:58Z
| 0
| 0
| null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-27T12:49:48Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="/q-Taxi-v3", 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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
YaYaB/q-FrozenLake-v1-4x4-noSlippery
|
YaYaB
| 2022-05-27T12:35:29Z
| 0
| 0
| null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-27T12:35:18Z
|
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="YaYaB/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
onewithnickelcoins/roberta-base-MLM
|
onewithnickelcoins
| 2022-05-27T11:57:24Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-27T11:40:10Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-MLM
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. -->
# roberta-base-MLM
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0265
- Accuracy: 0.6009
## 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
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
|
huggingtweets/alejodorowsky
|
huggingtweets
| 2022-05-27T11:13:26Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-27T11:11:07Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/alejodorowsky/1653650001771/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/784393032774873088/1x6o_3ws_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Alejandro Jodorowsky</div>
<div style="text-align: center; font-size: 14px;">@alejodorowsky</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Alejandro Jodorowsky.
| Data | Alejandro Jodorowsky |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 640 |
| Short tweets | 175 |
| Tweets kept | 2430 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1vwsnx64/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @alejodorowsky's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/j8ai679x) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/j8ai679x/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/alejodorowsky')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/parishilton
|
huggingtweets
| 2022-05-27T11:11:28Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-27T11:10:59Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/parishilton/1653649884348/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1519127596868374528/AyJv6gmG_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">ParisHilton.eth</div>
<div style="text-align: center; font-size: 14px;">@parishilton</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from ParisHilton.eth.
| Data | ParisHilton.eth |
| --- | --- |
| Tweets downloaded | 3211 |
| Retweets | 1563 |
| Short tweets | 407 |
| Tweets kept | 1241 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/17bxqhg6/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @parishilton's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/8b45v2wu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/8b45v2wu/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/parishilton')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/donhertzfeldt
|
huggingtweets
| 2022-05-27T11:02:23Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-27T11:00:31Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/donhertzfeldt/1653649338459/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1617966805/star-avatar_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">don hertzfeldt</div>
<div style="text-align: center; font-size: 14px;">@donhertzfeldt</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from don hertzfeldt.
| Data | don hertzfeldt |
| --- | --- |
| Tweets downloaded | 2513 |
| Retweets | 707 |
| Short tweets | 406 |
| Tweets kept | 1400 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/258eoxxi/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @donhertzfeldt's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/wxdijpch) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/wxdijpch/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/donhertzfeldt')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/meliksahtas
|
huggingtweets
| 2022-05-27T11:01:12Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-27T10:58:33Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/meliksahtas/1653649268087/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1229167506386014212/FKKauJpF_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">meliksahtas</div>
<div style="text-align: center; font-size: 14px;">@meliksahtas</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from meliksahtas.
| Data | meliksahtas |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 154 |
| Short tweets | 202 |
| Tweets kept | 2891 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ibkvi4w/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @meliksahtas's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/6flysmzm) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/6flysmzm/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/meliksahtas')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/dlputin
|
huggingtweets
| 2022-05-27T10:48:58Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-27T10:48:51Z
|
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/535525386872832001/NQn2b8OA_400x400.jpeg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">普京</div>
<div style="text-align: center; font-size: 14px;">@dlputin</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from 普京.
| Data | 普京 |
| --- | --- |
| Tweets downloaded | 3200 |
| Retweets | 0 |
| Short tweets | 586 |
| Tweets kept | 2614 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2t4wvbm9/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @dlputin's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2vcew5d1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2vcew5d1/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/dlputin')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/mit_istnews
|
huggingtweets
| 2022-05-27T09:11:24Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-27T09:10:02Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/mit_istnews/1653642679545/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/875463526583857156/mxYzB8tm_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">MIT IS&T</div>
<div style="text-align: center; font-size: 14px;">@mit_istnews</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from MIT IS&T.
| Data | MIT IS&T |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 20 |
| Short tweets | 132 |
| Tweets kept | 3098 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1b2tikho/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mit_istnews's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/15k3tyvf) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/15k3tyvf/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/mit_istnews')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
auriolar/q-Taxi-v3
|
auriolar
| 2022-05-27T08:27:18Z
| 0
| 0
| null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-27T08:04:54Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="auriolar/q-Taxi-v3", 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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
teppei727/bart-base-finetuned-amazon-onlyen
|
teppei727
| 2022-05-27T08:16:49Z
| 8
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-05-27T07:10:39Z
|
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- rouge
model-index:
- name: bart-base-finetuned-amazon-onlyen
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
args: en
metrics:
- name: Rouge1
type: rouge
value: 17.2662
---
<!-- 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. -->
# bart-base-finetuned-amazon-onlyen
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7572
- Rouge1: 17.2662
- Rouge2: 8.7425
- Rougel: 16.5765
- Rougelsum: 16.6844
## 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: 5.6e-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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 2.9212 | 1.0 | 771 | 2.8034 | 15.381 | 8.5254 | 15.223 | 15.059 |
| 2.3109 | 2.0 | 1542 | 2.8386 | 19.8947 | 11.0965 | 19.4876 | 19.5366 |
| 1.8973 | 3.0 | 2313 | 2.9258 | 17.7443 | 8.9232 | 17.311 | 17.1796 |
| 1.5421 | 4.0 | 3084 | 3.0696 | 17.8204 | 8.8919 | 17.3889 | 17.205 |
| 1.2391 | 5.0 | 3855 | 3.2609 | 15.9828 | 8.0523 | 15.393 | 15.3808 |
| 0.9736 | 6.0 | 4626 | 3.4080 | 15.7572 | 8.806 | 15.2435 | 15.3036 |
| 0.7824 | 7.0 | 5397 | 3.5537 | 18.4389 | 9.5135 | 17.7836 | 17.8758 |
| 0.6233 | 8.0 | 6168 | 3.6909 | 14.6698 | 6.9584 | 13.9417 | 14.0057 |
| 0.5086 | 9.0 | 6939 | 3.7357 | 16.9465 | 7.7604 | 16.1993 | 16.2963 |
| 0.4412 | 10.0 | 7710 | 3.7572 | 17.2662 | 8.7425 | 16.5765 | 16.6844 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
auriolar/q-FrozenLake-v1-4x4-noSlippery
|
auriolar
| 2022-05-27T08:00:20Z
| 0
| 0
| null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-27T08:00:12Z
|
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="auriolar/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Splend1dchan/t5small-squad-extractive
|
Splend1dchan
| 2022-05-27T07:48:00Z
| 1
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-05-27T07:32:03Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5_squad
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. -->
# t5_squad
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the squad dataset, using the extractive method by isolating the encoder only.
## 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
{
"epoch": 3.0,
"eval_exact_match": 70.06622516556291,
"eval_f1": 80.02993815400357,
"eval_samples": 10659
}
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
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