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 |
|---|---|---|---|---|---|---|---|---|---|
nboudad/Maghriberta0.0
|
nboudad
| 2022-06-07T12:05:50Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-07T11:42:41Z
|
---
widget:
- text: "جاب ليا <mask> ."
example_title: "example1"
- text: "مشيت نجيب <mask> فالفرماسيان ."
example_title: "example2"
---
|
huggingtweets/aoc-itsjefftiedrich-shaun_vids
|
huggingtweets
| 2022-06-07T12:01:33Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-07T11:43:07Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/aoc-itsjefftiedrich-shaun_vids/1654603284413/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/1507627313604743171/T8ksXYZu_400x400.jpg')">
</div>
<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/1009932396333031424/8FzKlCfB_400x400.jpg')">
</div>
<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/923274881197895680/AbHcStkl_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Shaun & Jeff Tiedrich & Alexandria Ocasio-Cortez</div>
<div style="text-align: center; font-size: 14px;">@aoc-itsjefftiedrich-shaun_vids</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 Shaun & Jeff Tiedrich & Alexandria Ocasio-Cortez.
| Data | Shaun | Jeff Tiedrich | Alexandria Ocasio-Cortez |
| --- | --- | --- | --- |
| Tweets downloaded | 3224 | 3249 | 3246 |
| Retweets | 1023 | 11 | 1236 |
| Short tweets | 212 | 713 | 126 |
| Tweets kept | 1989 | 2525 | 1884 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2znx4crj/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 @aoc-itsjefftiedrich-shaun_vids's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1q1etxhd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1q1etxhd/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/aoc-itsjefftiedrich-shaun_vids')
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)
|
giolisandro/t5-small-finetuned-en-to-ro
|
giolisandro
| 2022-06-07T11:30:00Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-07T11:19:25Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
model-index:
- name: t5-small-finetuned-en-to-ro
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-small-finetuned-en-to-ro
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 94 | 1.4141 | 7.3474 | 18.2586 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
RogerKam/roberta_fine_tuned_sentiment_financial_news
|
RogerKam
| 2022-06-07T11:25:35Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-07T11:08:02Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_fine_tuned_sentiment_financial_news
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_fine_tuned_sentiment_financial_news
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6362
- Accuracy: 0.8826
- F1 Score: 0.8865
## 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
### Framework versions
- Transformers 4.19.2
- Pytorch 1.10.0+cu111
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Sussybaka/gpt2wilkinscoffee
|
Sussybaka
| 2022-06-07T11:01:22Z
| 0
| 0
| null |
[
"exbert",
"en",
"dataset:openwebtext",
"arxiv:1910.01108",
"arxiv:2201.08542",
"arxiv:2203.12574",
"arxiv:1910.09700",
"arxiv:1503.02531",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2022-06-07T10:58:10Z
|
---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- openwebtext
model-index:
- name: distilgpt2
results:
- task:
type: text-generation
name: Text Generation
dataset:
type: wikitext
name: WikiText-103
metrics:
- type: perplexity
name: Perplexity
value: 21.1
co2_eq_emissions: 149200 g
---
# DistilGPT2
DistilGPT2 (short for Distilled-GPT2) is an English-language model pre-trained with the supervision of the smallest version of Generative Pre-trained Transformer 2 (GPT-2). Like GPT-2, DistilGPT2 can be used to generate text. Users of this model card should also consider information about the design, training, and limitations of [GPT-2](https://huggingface.co/gpt2), And this is a Wilkins-ified Version.
## Model Details
- **Developed by:** Hugging Face
- **Model type:** Transformer-based Language Model
- **Language:** English
- **License:** Apache 2.0
- **Model Description:** DistilGPT2 is an English-language model pre-trained with the supervision of the 124 million parameter version of GPT-2. DistilGPT2, which has 82 million parameters, was developed using [knowledge distillation](#knowledge-distillation) and was designed to be a faster, lighter version of GPT-2.
- **Resources for more information:** See [this repository](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) for more about Distil\* (a class of compressed models including Distilled-GPT2), [Sanh et al. (2019)](https://arxiv.org/abs/1910.01108) for more information about knowledge distillation and the training procedure, and this page for more about [GPT-2](https://openai.com/blog/better-language-models/).
## Uses, Limitations and Risks
#### Limitations and Risks
<details>
<summary>Click to expand</summary>
**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
As the developers of GPT-2 (OpenAI) note in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md), “language models like GPT-2 reflect the biases inherent to the systems they were trained on.” Significant research has explored bias and fairness issues with models for language generation including GPT-2 (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
DistilGPT2 also suffers from persistent bias issues, as highlighted in the demonstrative examples below. Note that these examples are not a comprehensive stress-testing of the model. Readers considering using the model should consider more rigorous evaluations of the model depending on their use case and context.
The impact of model compression techniques – such as knowledge distillation – on bias and fairness issues associated with language models is an active area of research. For example:
- [Silva, Tambwekar and Gombolay (2021)](https://aclanthology.org/2021.naacl-main.189.pdf) find that distilled versions of BERT and RoBERTa consistently exhibit statistically significant bias (with regard to gender and race) with effect sizes larger than the teacher models.
- [Xu and Hu (2022)](https://arxiv.org/pdf/2201.08542.pdf) find that distilled versions of GPT-2 showed consistent reductions in toxicity and bias compared to the teacher model (see the paper for more detail on metrics used to define/measure toxicity and bias).
- [Gupta et al. (2022)](https://arxiv.org/pdf/2203.12574.pdf) find that DistilGPT2 exhibits greater gender disparities than GPT-2 and propose a technique for mitigating gender bias in distilled language models like DistilGPT2.
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='distilgpt2')
>>> set_seed(48)
>>> generator("The White man worked as a", max_length=20, num_return_sequences=3)
[{'generated_text': "The White man worked as a salesman at a McDonald's restaurant called Kia at the time of the"},
{'generated_text': 'The White man worked as a contractor in the Army in the late 1990s. He became a "'},
{'generated_text': 'The White man worked as a police spokesman to the US Navy in the 1930s.'}]
>>> set_seed(48)
>>> generator("The Black man worked as a", max_length=20, num_return_sequences=3)
[{'generated_text': 'The Black man worked as a shop assistant for an hour at Wal-Mart at Wal-Mart in'},
{'generated_text': 'The Black man worked as a waiter in the hotel when he was assaulted when he got out of a'},
{'generated_text': 'The Black man worked as a police spokesman four months ago...'}]
```
</details>
#### Potential Uses
Since DistilGPT2 is a distilled version of GPT-2, it is intended to be used for similar use cases with the increased functionality of being smaller and easier to run than the base model.
The developers of GPT-2 state in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md) that they envisioned GPT-2 would be used by researchers to better understand large-scale generative language models, with possible secondary use cases including:
> - *Writing assistance: Grammar assistance, autocompletion (for normal prose or code)*
> - *Creative writing and art: exploring the generation of creative, fictional texts; aiding creation of poetry and other literary art.*
> - *Entertainment: Creation of games, chat bots, and amusing generations.*
Using DistilGPT2, the Hugging Face team built the [Write With Transformers](https://transformer.huggingface.co/doc/distil-gpt2) web app, which allows users to play with the model to generate text directly from their browser.
#### Out-of-scope Uses
OpenAI states in the GPT-2 [model card](https://github.com/openai/gpt-2/blob/master/model_card.md):
> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true.
>
> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans unless the deployers first carry out a study of biases relevant to the intended use-case.
### How to Get Started with the Model
<details>
<summary>Click to expand</summary>
*Be sure to read the sections on in-scope and out-of-scope uses and limitations of the model for further information on how to use the model.*
Using DistilGPT2 is similar to using GPT-2. DistilGPT2 can be used directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='distilgpt2')
>>> set_seed(42)
>>> generator("Hello, I’m a language model", max_length=20, num_return_sequences=5)
Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.
[{'generated_text': "Hello, I'm a language model, I'm a language model. In my previous post I've"},
{'generated_text': "Hello, I'm a language model, and I'd love to hear what you think about it."},
{'generated_text': "Hello, I'm a language model, but I don't get much of a connection anymore, so"},
{'generated_text': "Hello, I'm a language model, a functional language... It's not an example, and that"},
{'generated_text': "Hello, I'm a language model, not an object model.\n\nIn a nutshell, I"}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
model = GPT2Model.from_pretrained('distilgpt2')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
And in TensorFlow:
```python
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
model = TFGPT2Model.from_pretrained('distilgpt2')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
</details>
## Training Data
DistilGPT2 was trained using [OpenWebTextCorpus](https://skylion007.github.io/OpenWebTextCorpus/), an open-source reproduction of OpenAI’s WebText dataset, which was used to train GPT-2. See the [OpenWebTextCorpus Dataset Card](https://huggingface.co/datasets/openwebtext) for additional information about OpenWebTextCorpus and [Radford et al. (2019)](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf) for additional information about WebText.
## Training Procedure
The texts were tokenized using the same tokenizer as GPT-2, a byte-level version of Byte Pair Encoding (BPE). DistilGPT2 was trained using knowledge distillation, following a procedure similar to the training procedure for DistilBERT, described in more detail in [Sanh et al. (2019)](https://arxiv.org/abs/1910.01108).
## Evaluation Results
The creators of DistilGPT2 [report](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) that, on the [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) benchmark, GPT-2 reaches a perplexity on the test set of 16.3 compared to 21.1 for DistilGPT2 (after fine-tuning on the train set).
## Environmental Impact
*Carbon emissions were estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.*
- **Hardware Type:** 8 16GB V100
- **Hours used:** 168 (1 week)
- **Cloud Provider:** Azure
- **Compute Region:** unavailable, assumed East US for calculations
- **Carbon Emitted** *(Power consumption x Time x Carbon produced based on location of power grid)*: 149.2 kg eq. CO2
## Citation
```bibtex
@inproceedings{sanh2019distilbert,
title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
author={Sanh, Victor and Debut, Lysandre and Chaumond, Julien and Wolf, Thomas},
booktitle={NeurIPS EMC^2 Workshop},
year={2019}
}
```
## Glossary
- <a name="knowledge-distillation">**Knowledge Distillation**</a>: As described in [Sanh et al. (2019)](https://arxiv.org/pdf/1910.01108.pdf), “knowledge distillation is a compression technique in which a compact model – the student – is trained to reproduce the behavior of a larger model – the teacher – or an ensemble of models.” Also see [Bucila et al. (2006)](https://www.cs.cornell.edu/~caruana/compression.kdd06.pdf) and [Hinton et al. (2015)](https://arxiv.org/abs/1503.02531).
<a href="https://huggingface.co/exbert/?model=distilgpt2">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
This is the Wilkins Coffee Version.
|
DenisKochetov/q-Taxi-v3_2
|
DenisKochetov
| 2022-06-07T10:47:06Z
| 0
| 0
| null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-07T10:45:23Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3_2
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="DenisKochetov/q-Taxi-v3_2", 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"])
```
|
ThaisBeham/distilbert-base-uncased-finetuned-fira
|
ThaisBeham
| 2022-06-07T10:44:12Z
| 9
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-07T10:04:46Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-fira
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-fira
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7687
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 200 | 2.9963 |
| No log | 2.0 | 400 | 2.7457 |
| 3.0576 | 3.0 | 600 | 2.7687 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
DenisKochetov/q-Taxi-v3
|
DenisKochetov
| 2022-06-07T10:43:08Z
| 0
| 0
| null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-07T10:40:09Z
|
---
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="DenisKochetov/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"])
```
|
prashanth/IndicBART-ibart-en-to-hi
|
prashanth
| 2022-06-07T09:45:31Z
| 7
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"dataset:hindi_english_machine_translation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-07T09:41:24Z
|
---
tags:
- generated_from_trainer
datasets:
- hindi_english_machine_translation
model-index:
- name: IndicBART-ibart-en-to-hi
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. -->
# IndicBART-ibart-en-to-hi
This model is a fine-tuned version of [ai4bharat/IndicBART](https://huggingface.co/ai4bharat/IndicBART) on the hindi_english_machine_translation 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: 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 157 | 4.7112 | 0.8663 | 20.0 |
### Framework versions
- Transformers 4.19.1
- Pytorch 1.11.0+cu102
- Datasets 1.18.0
- Tokenizers 0.12.1
|
prashanth/IndicBART-ibart-hi-to-en
|
prashanth
| 2022-06-07T09:33:58Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"dataset:hindi_english_machine_translation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-07T09:30:43Z
|
---
tags:
- generated_from_trainer
datasets:
- hindi_english_machine_translation
model-index:
- name: IndicBART-ibart-hi-to-en
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. -->
# IndicBART-ibart-hi-to-en
This model is a fine-tuned version of [ai4bharat/IndicBART](https://huggingface.co/ai4bharat/IndicBART) on the hindi_english_machine_translation 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: 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 157 | 4.4208 | 1.0626 | 20.0 |
### Framework versions
- Transformers 4.19.1
- Pytorch 1.11.0+cu102
- Datasets 1.18.0
- Tokenizers 0.12.1
|
sanamoin/wav2vec2-base-timit-demo-google-colab
|
sanamoin
| 2022-06-07T09:13:33Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-02T21:42:05Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-google-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-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-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: 0.0001
- 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: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
clhuang/albert-sentiment
|
clhuang
| 2022-06-07T09:11:08Z
| 23
| 3
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"albert",
"classification",
"tw",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-05T13:17:21Z
|
---
language:
- tw
tags:
- albert
- classification
license: afl-3.0
metrics:
- Accuracy
---
# 繁體中文情緒分類: 負面(0)、正面(1)
依據ckiplab/albert預訓練模型微調，訓練資料集只有8萬筆，做為課程的範例模型。
# 使用範例:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("clhuang/albert-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("clhuang/albert-sentiment")
## Pediction
target_names=['Negative','Positive']
max_length = 200 # 最多字數 若超出模型訓練時的字數，以模型最大字數為依據
def get_sentiment_proba(text):
# prepare our text into tokenized sequence
inputs = tokenizer(text, padding=True, truncation=True, max_length=max_length, return_tensors="pt")
# perform inference to our model
outputs = model(**inputs)
# get output probabilities by doing softmax
probs = outputs[0].softmax(1)
response = {'Negative': round(float(probs[0, 0]), 2), 'Positive': round(float(probs[0, 1]), 2)}
# executing argmax function to get the candidate label
#return probs.argmax()
return response
get_sentiment_proba('我喜歡這本書')
get_sentiment_proba('不喜歡這款產品')
|
spy24/autotrain-expand-parrot-956131825
|
spy24
| 2022-06-07T09:11:04Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"unk",
"dataset:spy24/autotrain-data-expand-parrot",
"co2_eq_emissions",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-07T07:59:01Z
|
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- spy24/autotrain-data-expand-parrot
co2_eq_emissions: 0.647019768976749
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 956131825
- CO2 Emissions (in grams): 0.647019768976749
## Validation Metrics
- Loss: 2.330639123916626
- Rouge1: 53.3589
- Rouge2: 40.4273
- RougeL: 48.4928
- RougeLsum: 49.4952
- Gen Len: 18.8741
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/spy24/autotrain-expand-parrot-956131825
```
|
bondi/bert-clean-semaphore-prediction-w8
|
bondi
| 2022-06-07T08:55:38Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-07T07:55:38Z
|
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-clean-semaphore-prediction-w8
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-clean-semaphore-prediction-w8
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0669
- Accuracy: 0.9671
- F1: 0.9672
## 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
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
suonbo/bert-finetuned-ner
|
suonbo
| 2022-06-07T07:24:31Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-07T06:43:31Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9335982778605729
- name: Recall
type: recall
value: 0.9488387748232918
- name: F1
type: f1
value: 0.9411568316501127
- name: Accuracy
type: accuracy
value: 0.9854447518690763
---
<!-- 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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0637
- Precision: 0.9336
- Recall: 0.9488
- F1: 0.9412
- Accuracy: 0.9854
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0897 | 1.0 | 1756 | 0.0648 | 0.9152 | 0.9408 | 0.9278 | 0.9837 |
| 0.0384 | 2.0 | 3512 | 0.0601 | 0.9277 | 0.9507 | 0.9391 | 0.9859 |
| 0.0201 | 3.0 | 5268 | 0.0637 | 0.9336 | 0.9488 | 0.9412 | 0.9854 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
miyagawaorj/xlm-roberta-base-finetuned-panx-de
|
miyagawaorj
| 2022-06-07T07:03:42Z
| 3
| 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
| 2022-05-18T00:27:47Z
|
---
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: 0.8620945214069894
---
<!-- 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: 0.1372
- F1: 0.8621
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 |
| 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 |
| 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
sraza/wav2vec2-large-xls-r-300m-ur-colab
|
sraza
| 2022-06-07T06:57:19Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-16T10:22:41Z
|
ASR for urdu language.
Dataset used is common voice and also some self collected data.
|
promobot/labse-ru
|
promobot
| 2022-06-07T06:42:01Z
| 7
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"ru",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-06-06T07:37:17Z
|
---
language: ["ru"]
pipeline_tag: feature-extraction
tags:
- feature-extraction
- sentence-similarity
license: apache-2.0
---
|
bondi/bert-clean-semaphore-prediction-w0
|
bondi
| 2022-06-07T05:54:44Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-07T04:46:28Z
|
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-clean-semaphore-prediction-w0
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-clean-semaphore-prediction-w0
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0680
- Accuracy: 0.9693
- F1: 0.9694
## 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
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
SuperSecureHuman/q-Taxi-v3
|
SuperSecureHuman
| 2022-06-07T05:47:12Z
| 0
| 0
| null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-07T05:36:40Z
|
---
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="SuperSecureHuman/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"])
```
|
enoriega/rule_learning_test
|
enoriega
| 2022-06-07T05:19:20Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"dataset:enoriega/odinsynth_dataset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T22:29:06Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- enoriega/odinsynth_dataset
model-index:
- name: rule_learning_test
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. -->
# rule_learning_test
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the enoriega/odinsynth_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1255
## 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
- gradient_accumulation_steps: 1000
- total_train_batch_size: 8000
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1764 | 0.32 | 20 | 0.2303 |
| 0.145 | 0.64 | 40 | 0.1470 |
| 0.129 | 0.96 | 60 | 0.1321 |
| 0.1256 | 1.29 | 80 | 0.1265 |
| 0.1304 | 1.61 | 100 | 0.1252 |
| 0.1235 | 1.93 | 120 | 0.1260 |
| 0.125 | 2.26 | 140 | 0.1261 |
| 0.1263 | 2.58 | 160 | 0.1262 |
| 0.1244 | 2.9 | 180 | 0.1256 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
twieland/VN_ja-en_mt5_small
|
twieland
| 2022-06-07T04:14:54Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-07T02:40:13Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: VN_ja-en_mt5_small
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. -->
# VN_ja-en_mt5_small
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3148
## 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: 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.4633 | 1.0 | 20552 | 2.3148 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
cutten/wav2vec2-base-timit-demo-google-colab
|
cutten
| 2022-06-07T03:35:57Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-04T13:17:05Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-base-timit-demo-google-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-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6342
- Wer: 0.5808
## 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: 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 9.1358 | 1.19 | 500 | 3.2710 | 1.0 |
| 3.0499 | 2.38 | 1000 | 1.8976 | 1.0 |
| 1.279 | 3.56 | 1500 | 0.7502 | 0.8228 |
| 0.7953 | 4.75 | 2000 | 0.5914 | 0.7343 |
| 0.6451 | 5.94 | 2500 | 0.6152 | 0.7280 |
| 0.5351 | 7.13 | 3000 | 0.5948 | 0.7041 |
| 0.4633 | 8.31 | 3500 | 0.5585 | 0.6712 |
| 0.4272 | 9.5 | 4000 | 0.5372 | 0.6457 |
| 0.3803 | 10.69 | 4500 | 0.5404 | 0.6402 |
| 0.3462 | 11.88 | 5000 | 0.5862 | 0.6484 |
| 0.3302 | 13.06 | 5500 | 0.5991 | 0.6426 |
| 0.3096 | 14.25 | 6000 | 0.5687 | 0.6287 |
| 0.2839 | 15.44 | 6500 | 0.5798 | 0.6384 |
| 0.2701 | 16.63 | 7000 | 0.5775 | 0.6047 |
| 0.2507 | 17.81 | 7500 | 0.5638 | 0.6065 |
| 0.2376 | 19.0 | 8000 | 0.5937 | 0.6094 |
| 0.2264 | 20.19 | 8500 | 0.5944 | 0.6065 |
| 0.2146 | 21.38 | 9000 | 0.6050 | 0.6122 |
| 0.1947 | 22.57 | 9500 | 0.6283 | 0.5992 |
| 0.1982 | 23.75 | 10000 | 0.6126 | 0.6018 |
| 0.1924 | 24.94 | 10500 | 0.6075 | 0.5962 |
| 0.1855 | 26.13 | 11000 | 0.6344 | 0.5938 |
| 0.1839 | 27.32 | 11500 | 0.6118 | 0.5880 |
| 0.1741 | 28.5 | 12000 | 0.6381 | 0.5878 |
| 0.1726 | 29.69 | 12500 | 0.6342 | 0.5808 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Cube/distilbert-base-uncased-finetuned-ner
|
Cube
| 2022-06-07T03:03:43Z
| 5
| 0
|
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-07T02:56:38Z
|
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Cube/distilbert-base-uncased-finetuned-ner
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. -->
# Cube/distilbert-base-uncased-finetuned-ner
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: 0.0339
- Validation Loss: 0.0646
- Train Precision: 0.9217
- Train Recall: 0.9295
- Train F1: 0.9256
- Train Accuracy: 0.9827
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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: float32
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.1996 | 0.0735 | 0.8930 | 0.9179 | 0.9053 | 0.9784 | 0 |
| 0.0545 | 0.0666 | 0.9137 | 0.9292 | 0.9214 | 0.9817 | 1 |
| 0.0339 | 0.0646 | 0.9217 | 0.9295 | 0.9256 | 0.9827 | 2 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
spencerkmarley/distilbert
|
spencerkmarley
| 2022-06-07T03:02:57Z
| 5
| 0
|
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-07T02:28:18Z
|
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: spencerkmarley/distilbert
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. -->
# spencerkmarley/distilbert
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: 3.2904
- Validation Loss: 2.8356
- 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': -949, '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 |
|:----------:|:---------------:|:-----:|
| 3.2904 | 2.8356 | 0 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
nestoralvaro/mt5-base-finetuned-xsum-mlsum___summary_text_google_mt5_base
|
nestoralvaro
| 2022-06-07T02:18:15Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:mlsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-06T22:08:56Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mlsum
metrics:
- rouge
model-index:
- name: mt5-base-finetuned-xsum-mlsum___summary_text_google_mt5_base
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: mlsum
type: mlsum
args: es
metrics:
- name: Rouge1
type: rouge
value: 8.9973
---
<!-- 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. -->
# mt5-base-finetuned-xsum-mlsum___summary_text_google_mt5_base
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the mlsum dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 8.9973
- Rouge2: 0.9036
- Rougel: 7.6699
- Rougelsum: 7.716
- Gen Len: 10.2326
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 66592 | nan | 8.9973 | 0.9036 | 7.6699 | 7.716 | 10.2326 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Nithiwat/wangchanberta-base-att-spm-uncased-finetuned-imdb
|
Nithiwat
| 2022-06-07T01:25:53Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"camembert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-07T01:04:12Z
|
---
tags:
- generated_from_trainer
model-index:
- name: wangchanberta-base-att-spm-uncased-finetuned-imdb
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. -->
# wangchanberta-base-att-spm-uncased-finetuned-imdb
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5910
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9341 | 1.0 | 295 | 2.6511 |
| 2.8093 | 2.0 | 590 | 2.6178 |
| 2.7689 | 3.0 | 885 | 2.5321 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
BigSalmon/InformalToFormalLincoln49
|
BigSalmon
| 2022-06-07T01:12:31Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-31T00:18:48Z
|
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln49")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln49")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
|
huggingtweets/byelihoff
|
huggingtweets
| 2022-06-07T01:08:05Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-06T13:43:11Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/byelihoff/1654564001530/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/1481727546186211329/U8AeI0cS_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">Eli Hoff</div>
<div style="text-align: center; font-size: 14px;">@byelihoff</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 Eli Hoff.
| Data | Eli Hoff |
| --- | --- |
| Tweets downloaded | 3248 |
| Retweets | 821 |
| Short tweets | 187 |
| Tweets kept | 2240 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3t22q7l3/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 @byelihoff's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3qqqbwen) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3qqqbwen/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/byelihoff')
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/sofiaazeman
|
huggingtweets
| 2022-06-07T00:53:43Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-07T00:51:46Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/sofiaazeman/1654563180290/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/1511483454495637510/BWEFnW4O_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">Sofi Zeman</div>
<div style="text-align: center; font-size: 14px;">@sofiaazeman</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 Sofi Zeman.
| Data | Sofi Zeman |
| --- | --- |
| Tweets downloaded | 317 |
| Retweets | 158 |
| Short tweets | 26 |
| Tweets kept | 133 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3uxm4ug9/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 @sofiaazeman's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/6819mjpo) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/6819mjpo/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/sofiaazeman')
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/heylookaturtle
|
huggingtweets
| 2022-06-07T00:50:23Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-07T00:48:04Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/heylookaturtle/1654563018664/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/1052029344254701568/2yAQKb6K_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">Adam Porter</div>
<div style="text-align: center; font-size: 14px;">@heylookaturtle</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 Adam Porter.
| Data | Adam Porter |
| --- | --- |
| Tweets downloaded | 3232 |
| Retweets | 1006 |
| Short tweets | 436 |
| Tweets kept | 1790 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2xiwa2l6/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 @heylookaturtle's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/hov36pjn) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/hov36pjn/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/heylookaturtle')
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/hopedavistweets
|
huggingtweets
| 2022-06-07T00:48:38Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-07T00:46:24Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/hopedavistweets/1654562883505/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/1420954294082326529/ZkxWu0ln_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">Hope Davis 🪩</div>
<div style="text-align: center; font-size: 14px;">@hopedavistweets</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 Hope Davis 🪩.
| Data | Hope Davis 🪩 |
| --- | --- |
| Tweets downloaded | 2707 |
| Retweets | 1812 |
| Short tweets | 100 |
| Tweets kept | 795 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2pkx13m4/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 @hopedavistweets's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/objxokv4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/objxokv4/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/hopedavistweets')
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/mcbrideace-sorarescp-thedonofsorare
|
huggingtweets
| 2022-06-06T22:20:27Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-06T22:17:18Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/mcbrideace-sorarescp-thedonofsorare/1654554022265/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/1462464744200323076/q_vEAFLx_400x400.jpg')">
</div>
<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/1454346046319038465/qivKQRrg_400x400.jpg')">
</div>
<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/1527184416077922304/Dpk_AXXK_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">The Don & McBriceAce.eth & Sonhos_10A </div>
<div style="text-align: center; font-size: 14px;">@mcbrideace-sorarescp-thedonofsorare</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 The Don & McBriceAce.eth & Sonhos_10A .
| Data | The Don | McBriceAce.eth | Sonhos_10A |
| --- | --- | --- | --- |
| Tweets downloaded | 3247 | 3248 | 2974 |
| Retweets | 148 | 293 | 1612 |
| Short tweets | 334 | 618 | 273 |
| Tweets kept | 2765 | 2337 | 1089 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1omlhh4m/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 @mcbrideace-sorarescp-thedonofsorare's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1kamm6ws) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1kamm6ws/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/mcbrideace-sorarescp-thedonofsorare')
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)
|
Pikachu/Crust
|
Pikachu
| 2022-06-06T21:48:54Z
| 0
| 1
| null |
[
"synthesis",
"speech",
"speech synthesis",
"en",
"license:apache-2.0",
"region:us"
] | null | 2022-04-15T13:32:39Z
|
---
language:
- en
tags:
- synthesis
- speech
- speech synthesis
license: apache-2.0
datasets:
- gathered from Uberduck's discord server, put together by Crust.
---
# **CRUST - RELEASED** (Chungus Related Uberduck's Speech toy)
# Welcome to Crust 🍕⭕
Crust is a 168 speaker model based on uberduck's pipeline. We've noticed that having multiple speakers instead of having one speaker, improves the performance of the model and makes it be able to synthesize comparable results with only 1 minute of data. The results are surprisingly good and because of the lower dataset, batch size can be lowered and the model is generally faster than other models.
### What is a multispeaker model?
A multispeaker model is a model that has been trained on multiple speakers, the model first generates an "average" voice of all of the speakers and then tunes the different speakers on that average voice. If you have a lot of speakers, individual results won't be that great, as the model only has ~250+ mb to work with, but this is great for finetuning different voices on it because the model has learned an "average" voice. This average voice has the knowledge of all voices included in the dataset.
Core: A multispeaker model is a model trained on multiple speakers.
### How does this make training possible with 1 minute of training data?
The model has been trained on 168 datasets, ~20 hours of data, or ~19.8 thousand audio files. This is smaller than LJ speech but it has way more variety in voices, which LJ speech doesn't have. this variety allows the model to learn speech in different genders, accents, pitches, and other important factors, meaning that it knows a lot more in terms of voices. Finetuning this on 1 minute of data is possible because it already has a decently close match of your voice somewhere in its latent space.
Core: The multispeaker has more knowledge of multiple people speaking, making it surprisingly good at training on low-minute datasets.
### What are the downsides?
**-Training time.**
Training time sadly does still take a while, but considering you might only be training using 1 minute of data, it would take shorter than training it on the Lj-speech model, but would not come close to corentj's realtime voice cloning, it would be more accurate.
**-Clean datasets.**
We still doubt if the model would be able to be trained on datasets that have loud noise in them or have background music in them, realistically, it would not be able to be trained on these kinds of datasets, so before you train, please use a clean dataset.
**-Inference.**
Even though this model can be trained on 1 minute of data, we still recommend training it on more, we can't promise good results if the model doesn't have sufficient data, this would ideally be measured in syllables or phonemes, but minutes is a lot easier.
**-Audio quality.**
Sadly, the model has only been trained on 22050 hz and mono audio files, while this still sounds good when there's a Hi-Fi Gan vocoder, It's still going to not have stereo sound (which would not be that useful) or 44100 hz audio quality on its own. Sadly the Hi-Fi Gan vocoder does also bring in artifacts into the wav files which makes synthesis not as realistic.
We used [**Uberduck's TTS Pipeline on github**](https://github.com/uberduck-ai/uberduck-ml-dev) To train our model.
|
jplago/bert-finetuned-ner
|
jplago
| 2022-06-06T20:19:07Z
| 4
| 0
|
transformers
|
[
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-06T19:58:03Z
|
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: jplago/bert-finetuned-ner
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. -->
# jplago/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0270
- Validation Loss: 0.0550
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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 |
|:----------:|:---------------:|:-----:|
| 0.1628 | 0.0660 | 0 |
| 0.0470 | 0.0569 | 1 |
| 0.0270 | 0.0550 | 2 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
imamnurby/rob2rand_merged_w_prefix_c_fc_interactive
|
imamnurby
| 2022-06-06T19:48:00Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-06T19:45:22Z
|
---
tags:
- generated_from_trainer
model-index:
- name: rob2rand_merged_w_prefix_c_fc_interactive
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_merged_w_prefix_c_fc_interactive
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- 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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3
### Framework versions
- Transformers 4.18.0
- Pytorch 1.7.1
- Datasets 2.1.0
- Tokenizers 0.12.1
|
keras-io/conv_autoencoder
|
keras-io
| 2022-06-06T19:44:37Z
| 1
| 0
|
tf-keras
|
[
"tf-keras",
"tensorboard",
"license:gpl-3.0",
"region:us"
] | null | 2022-06-06T18:37:58Z
|
---
license: gpl-3.0
---
## Model Description
### Keras Implementation of Convolutional autoencoder for image denoising
This repo contains the trained model of Convolutional autoencoder for image denoising on MNIST Dataset mixed with random noise.
Spaces Link:- https://huggingface.co/spaces/keras-io/conv_autoencoder
Keras Example Link:- https://keras.io/examples/vision/autoencoder/
## Intended uses & limitations
- The trained model can be used to remove noise from any grayscale image.
- Since this model is trained on MNIST Data added with random noise, so this model can be used only for images with shape 28 * 28.
## Training and evaluation data
- Original mnist train & test dataset were loaded from tensorflow datasets.
- Then Some noise was added to train & test images.
- Noisy images were used as input images and original clean images were used as output images for training.
## Training procedure
### Training hyperparameter
The following hyperparameters were used during training:
- optimizer: 'adam'
- loss: 'binary_crossentropy'
- epochs: 100
- batch_size: 128
- ReLU was used as activation function in all layers except last layer where Sigmoid was used as activation function.
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
cammy/wav2vec2-xlsr-greek-speech-emotion-recognition
|
cammy
| 2022-06-06T19:17:25Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T18:14:41Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-xlsr-greek-speech-emotion-recognition
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-xlsr-greek-speech-emotion-recognition
This model is a fine-tuned version of [lighteternal/wav2vec2-large-xlsr-53-greek](https://huggingface.co/lighteternal/wav2vec2-large-xlsr-53-greek) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7699
- Accuracy: 0.8168
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5594 | 0.22 | 100 | 0.7689 | 0.7649 |
| 0.4341 | 0.44 | 200 | 0.6557 | 0.8045 |
| 0.2925 | 0.66 | 300 | 0.7060 | 0.8094 |
| 0.3846 | 0.88 | 400 | 0.7699 | 0.8168 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.3.dev0
- Tokenizers 0.12.1
|
miyagawaorj/distilbert-base-uncased-distilled-clinc
|
miyagawaorj
| 2022-06-06T18:42:51Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-06T18:06:58Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9506451612903226
---
<!-- 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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2466
- Accuracy: 0.9506
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.9383 | 1.0 | 954 | 1.4511 | 0.8397 |
| 0.8485 | 2.0 | 1908 | 0.4733 | 0.9255 |
| 0.2822 | 3.0 | 2862 | 0.3070 | 0.9429 |
| 0.1515 | 4.0 | 3816 | 0.2664 | 0.9490 |
| 0.106 | 5.0 | 4770 | 0.2641 | 0.95 |
| 0.0874 | 6.0 | 5724 | 0.2536 | 0.9510 |
| 0.0764 | 7.0 | 6678 | 0.2475 | 0.9506 |
| 0.0718 | 8.0 | 7632 | 0.2450 | 0.9513 |
| 0.068 | 9.0 | 8586 | 0.2473 | 0.9497 |
| 0.0664 | 10.0 | 9540 | 0.2466 | 0.9506 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.12.1
|
huggingtweets/nonewthing
|
huggingtweets
| 2022-06-06T17:50:00Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-06T17:49:54Z
|
---
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/1532336212412977152/TWPqTO8d_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">AI</div>
<div style="text-align: center; font-size: 14px;">@nonewthing</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 AI.
| Data | AI |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 100 |
| Short tweets | 234 |
| Tweets kept | 2913 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/bf84hrrd/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 @nonewthing's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/169zdg1z) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/169zdg1z/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/nonewthing')
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)
|
stopdoingmath/opus-mt-sla-en-finetuned-uk-to-en
|
stopdoingmath
| 2022-06-06T17:20:17Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:opus100",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-06T15:18:37Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- opus100
metrics:
- bleu
model-index:
- name: opus-mt-sla-en-finetuned-uk-to-en
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus100
type: opus100
args: default
metrics:
- name: Bleu
type: bleu
value: 27.7684
---
<!-- 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. -->
# opus-mt-sla-en-finetuned-uk-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-sla-en](https://huggingface.co/Helsinki-NLP/opus-mt-sla-en) on the opus100 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7232
- Bleu: 27.7684
- Gen Len: 12.2485
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 1.5284 | 1.0 | 62500 | 1.7232 | 27.7684 | 12.2485 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ubiqtuitin/PPO_CarRacing-v0
|
ubiqtuitin
| 2022-06-06T17:11:06Z
| 2
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-06T17:09:22Z
|
---
library_name: stable-baselines3
tags:
- CarRacing-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -82.71 +/- 1.70
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CarRacing-v0
type: CarRacing-v0
---
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0**
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
...
```
|
yanekyuk/berturk-uncased-keyword-discriminator
|
yanekyuk
| 2022-06-06T17:09:35Z
| 14
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"tr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-06T15:01:04Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
language:
- tr
widget:
- text: "İngiltere'de düzenlenen Avrupa Tekvando ve Para Tekvando Şampiyonası’nda millî tekvandocular 5 altın, 2 gümüş ve 4 bronz olmak üzere 11, millî para tekvandocular ise 4 altın, 3 gümüş ve 1 bronz olmak üzere 8 madalya kazanarak takım halinde Avrupa şampiyonu oldu."
- text: "Füme somon dedik ama aslında lox salamuralanmış somon anlamına geliyor, füme etme opsiyonel. Lox bagel, 1930'larda Eggs Benedict furyasında New Yorklu Yahudi cemaati tarafından koşer bir alternatif olarak çıkan bir lezzet. Günümüzde benim hangover yüreğim dâhil dünyanın birçok yerinde enfes bir kahvaltı sandviçi."
- text: "Türkiye'de son aylarda sıklıkla tartışılan konut satışı karşılığında yabancılara vatandaşlık verilmesi konusunu beyin göçü kapsamında ele almak mümkün. Daha önce 250 bin dolar olan vatandaşlık bedeli yükselen tepkiler üzerine 400 bin dolara çıkarılmıştı. Türkiye'den göç eden iyi eğitimli kişilerin , gittikleri ülkelerde 250 bin dolar tutarında yabancı yatırıma denk olduğu göz önüne alındığında nitelikli insan gücünün yabancılara konut karşılığında satılan vatandaşlık bedelin eş olduğunu görüyoruz. Yurt dışına giden her bir vatandaşın yüksek teknolojili katma değer üreten sektörlere yapacağı katkılar göz önünde bulundurulduğunda bu açığın inşaat sektörüyle kapatıldığını da görüyoruz. Beyin göçü konusunda sadece ekonomik perspektiften bakıldığında bile kısa vadeli döviz kaynağı yaratmak için kullanılan vatandaşlık satışı yerine beyin göçünü önleyecek önlemler alınmasının ülkemize çok daha faydalı olacağı sonucunu çıkarıyoruz."
- text: "Türkiye’de resmî verilere göre, 15 ve daha yukarı yaştaki kişilerde mevsim etkisinden arındırılmış işsiz sayısı, bu yılın ilk çeyreğinde bir önceki çeyreğe göre 50 bin kişi artarak 3 milyon 845 bin kişi oldu. Mevsim etkisinden arındırılmış işsizlik oranı ise 0,1 puanlık artışla %11,4 seviyesinde gerçekleşti. İşsizlik oranı, ilk çeyrekte geçen yılın aynı çeyreğine göre 1,7 puan azaldı."
- text: "Boeing’in insansız uzay aracı Starliner, birtakım sorunlara rağmen Uluslararası Uzay İstasyonuna (ISS) ulaşarak ilk kez başarılı bir şekilde kenetlendi. Aracın ISS’te beş gün kalmasını takiben sorunsuz bir şekilde New Mexico’ya inmesi halinde Boeing, sonbaharda astronotları yörüngeye göndermek için Starliner’ı kullanabilir.\n\nNeden önemli? NASA’nın personal aracı üretmeyi durdurmasından kaynaklı olarak görevli astronotlar ve kozmonotlar, ISS’te Rusya’nın ürettiği uzay araçları ile taşınıyordu. Starliner’ın kendini kanıtlaması ise bu konuda Rusya’ya olan bağımlılığın potansiyel olarak ortadan kalkabileceği anlamına geliyor."
model-index:
- name: berturk-uncased-keyword-discriminator
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. -->
# berturk-uncased-keyword-discriminator
This model is a fine-tuned version of [dbmdz/bert-base-turkish-uncased](https://huggingface.co/dbmdz/bert-base-turkish-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3989
- Precision: 0.6234
- Recall: 0.6508
- Accuracy: 0.9145
- F1: 0.6368
- Ent/precision: 0.6435
- Ent/accuracy: 0.7120
- Ent/f1: 0.6761
- Con/precision: 0.5834
- Con/accuracy: 0.5475
- Con/f1: 0.5649
## 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: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------------:|:------------:|:------:|:-------------:|:------------:|:------:|
| 0.2005 | 1.0 | 1875 | 0.2104 | 0.5981 | 0.5978 | 0.9148 | 0.5979 | 0.6280 | 0.6665 | 0.6467 | 0.5383 | 0.4820 | 0.5086 |
| 0.1468 | 2.0 | 3750 | 0.2094 | 0.5996 | 0.6568 | 0.9164 | 0.6269 | 0.6285 | 0.7049 | 0.6645 | 0.5477 | 0.5757 | 0.5614 |
| 0.1124 | 3.0 | 5625 | 0.2372 | 0.6106 | 0.6679 | 0.9154 | 0.6380 | 0.6285 | 0.7270 | 0.6741 | 0.5753 | 0.5684 | 0.5718 |
| 0.0861 | 4.0 | 7500 | 0.2736 | 0.6133 | 0.6707 | 0.9145 | 0.6407 | 0.6281 | 0.7359 | 0.6777 | 0.5830 | 0.5606 | 0.5716 |
| 0.0644 | 5.0 | 9375 | 0.3081 | 0.6115 | 0.6683 | 0.9145 | 0.6386 | 0.6291 | 0.7293 | 0.6755 | 0.5764 | 0.5657 | 0.5710 |
| 0.0498 | 6.0 | 11250 | 0.3449 | 0.6245 | 0.6466 | 0.9149 | 0.6353 | 0.6380 | 0.7097 | 0.6720 | 0.5965 | 0.5401 | 0.5669 |
| 0.0401 | 7.0 | 13125 | 0.3838 | 0.6223 | 0.6545 | 0.9140 | 0.6380 | 0.6449 | 0.7100 | 0.6759 | 0.5790 | 0.5610 | 0.5699 |
| 0.0329 | 8.0 | 15000 | 0.3989 | 0.6234 | 0.6508 | 0.9145 | 0.6368 | 0.6435 | 0.7120 | 0.6761 | 0.5834 | 0.5475 | 0.5649 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
yanekyuk/berturk-uncased-keyword-extractor
|
yanekyuk
| 2022-06-06T17:09:28Z
| 7
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"tr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-06T09:33:44Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
language:
- tr
widget:
- text: "İngiltere'de düzenlenen Avrupa Tekvando ve Para Tekvando Şampiyonası’nda millî tekvandocular 5 altın, 2 gümüş ve 4 bronz olmak üzere 11, millî para tekvandocular ise 4 altın, 3 gümüş ve 1 bronz olmak üzere 8 madalya kazanarak takım halinde Avrupa şampiyonu oldu."
- text: "Füme somon dedik ama aslında lox salamuralanmış somon anlamına geliyor, füme etme opsiyonel. Lox bagel, 1930'larda Eggs Benedict furyasında New Yorklu Yahudi cemaati tarafından koşer bir alternatif olarak çıkan bir lezzet. Günümüzde benim hangover yüreğim dâhil dünyanın birçok yerinde enfes bir kahvaltı sandviçi."
- text: "Türkiye'de son aylarda sıklıkla tartışılan konut satışı karşılığında yabancılara vatandaşlık verilmesi konusunu beyin göçü kapsamında ele almak mümkün. Daha önce 250 bin dolar olan vatandaşlık bedeli yükselen tepkiler üzerine 400 bin dolara çıkarılmıştı. Türkiye'den göç eden iyi eğitimli kişilerin , gittikleri ülkelerde 250 bin dolar tutarında yabancı yatırıma denk olduğu göz önüne alındığında nitelikli insan gücünün yabancılara konut karşılığında satılan vatandaşlık bedelin eş olduğunu görüyoruz. Yurt dışına giden her bir vatandaşın yüksek teknolojili katma değer üreten sektörlere yapacağı katkılar göz önünde bulundurulduğunda bu açığın inşaat sektörüyle kapatıldığını da görüyoruz. Beyin göçü konusunda sadece ekonomik perspektiften bakıldığında bile kısa vadeli döviz kaynağı yaratmak için kullanılan vatandaşlık satışı yerine beyin göçünü önleyecek önlemler alınmasının ülkemize çok daha faydalı olacağı sonucunu çıkarıyoruz."
- text: "Türkiye’de resmî verilere göre, 15 ve daha yukarı yaştaki kişilerde mevsim etkisinden arındırılmış işsiz sayısı, bu yılın ilk çeyreğinde bir önceki çeyreğe göre 50 bin kişi artarak 3 milyon 845 bin kişi oldu. Mevsim etkisinden arındırılmış işsizlik oranı ise 0,1 puanlık artışla %11,4 seviyesinde gerçekleşti. İşsizlik oranı, ilk çeyrekte geçen yılın aynı çeyreğine göre 1,7 puan azaldı."
- text: "Boeing’in insansız uzay aracı Starliner, birtakım sorunlara rağmen Uluslararası Uzay İstasyonuna (ISS) ulaşarak ilk kez başarılı bir şekilde kenetlendi. Aracın ISS’te beş gün kalmasını takiben sorunsuz bir şekilde New Mexico’ya inmesi halinde Boeing, sonbaharda astronotları yörüngeye göndermek için Starliner’ı kullanabilir.\n\nNeden önemli? NASA’nın personal aracı üretmeyi durdurmasından kaynaklı olarak görevli astronotlar ve kozmonotlar, ISS’te Rusya’nın ürettiği uzay araçları ile taşınıyordu. Starliner’ın kendini kanıtlaması ise bu konuda Rusya’ya olan bağımlılığın potansiyel olarak ortadan kalkabileceği anlamına geliyor."
model-index:
- name: berturk-uncased-keyword-extractor
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. -->
# berturk-uncased-keyword-extractor
This model is a fine-tuned version of [dbmdz/bert-base-turkish-uncased](https://huggingface.co/dbmdz/bert-base-turkish-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3931
- Precision: 0.6631
- Recall: 0.6728
- Accuracy: 0.9188
- F1: 0.6679
## 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: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|
| 0.1779 | 1.0 | 1875 | 0.1862 | 0.6199 | 0.6356 | 0.9192 | 0.6276 |
| 0.1327 | 2.0 | 3750 | 0.1890 | 0.6328 | 0.6917 | 0.9206 | 0.6610 |
| 0.1008 | 3.0 | 5625 | 0.2188 | 0.6322 | 0.7037 | 0.9189 | 0.6660 |
| 0.0755 | 4.0 | 7500 | 0.2539 | 0.6395 | 0.7030 | 0.9181 | 0.6697 |
| 0.0574 | 5.0 | 9375 | 0.2882 | 0.6556 | 0.6868 | 0.9197 | 0.6709 |
| 0.0433 | 6.0 | 11250 | 0.3425 | 0.6565 | 0.6851 | 0.9189 | 0.6705 |
| 0.0352 | 7.0 | 13125 | 0.3703 | 0.6616 | 0.6776 | 0.9191 | 0.6695 |
| 0.0288 | 8.0 | 15000 | 0.3931 | 0.6631 | 0.6728 | 0.9188 | 0.6679 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
vjeansel/q-Taxi-v3
|
vjeansel
| 2022-06-06T17:02:53Z
| 0
| 0
| null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-06T17:02:48Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.54 +/- 2.73
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="vjeansel/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"])
```
|
KaliYuga/pixelartdiffusion4k
|
KaliYuga
| 2022-06-06T16:55:39Z
| 0
| 15
| null |
[
"license:cc-by-3.0",
"region:us"
] | null | 2022-06-06T15:59:40Z
|
---
license: cc-by-3.0
---
Unconditional 256x256 Diffusion model trained on ~4100 hand-picked pixel art pieces.\
*Outputs* made with this model may be used however you wish without attribution--although attribution is always nice!
However, if you use this model in your own tool/app/notebook/commercial product/whatever, you MUST credit KaliYuga-ai
and link to my twitter (https://twitter.com/KaliYuga_ai) and Patreon (https://www.patreon.com/kaliyuga_ai) in a non-hidden place. \
Also, if you make bank using this model, feel free to tip me over on Patreon so I can afford to buy my cat the nice cat food :)\
Above all, ENJOY!}
|
dipesh/Intent-Classification-Bert-Base-Cased
|
dipesh
| 2022-06-06T16:43:41Z
| 4
| 1
|
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-27T13:27:06Z
|
---
tags:
- generated_from_keras_callback
model-index:
- name: Intent-Classification-Bert-Base-Cased
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. -->
# Intent-Classification-Bert-Base-Cased
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 4.16.2
- TensorFlow 2.9.1
- Datasets 2.2.2
- Tokenizers 0.10.3
|
ianspektor/q-FrozenLake-v1-8x8-slippery
|
ianspektor
| 2022-06-06T16:30:59Z
| 0
| 0
| null |
[
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-06T15:56:51Z
|
---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-slippery
results:
- metrics:
- type: mean_reward
value: 0.46 +/- 0.50
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8
type: FrozenLake-v1-8x8
---
# **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="ianspektor/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"])
```
|
stig/distilbert-base-uncased-finetuned-squad
|
stig
| 2022-06-06T15:40:07Z
| 7
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-06T10:07:30Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8545
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0122 | 1.0 | 2312 | 1.8973 |
| 1.7666 | 2.0 | 4624 | 1.8320 |
| 1.5729 | 3.0 | 6936 | 1.8545 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
victorlifan/autotrain-song_title_generate-939531516
|
victorlifan
| 2022-06-06T15:36:11Z
| 3
| 1
|
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"unk",
"dataset:victorlifan/autotrain-data-song_title_generate",
"co2_eq_emissions",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-05T21:52:45Z
|
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- victorlifan/autotrain-data-song_title_generate
co2_eq_emissions: 11.013963276910237
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 939531516
- CO2 Emissions (in grams): 11.013963276910237
## Validation Metrics
- Loss: 1.1184396743774414
- Rouge1: 54.9539
- Rouge2: 40.7878
- RougeL: 54.8616
- RougeLsum: 54.8682
- Gen Len: 5.1429
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/victorlifan/autotrain-song_title_generate-939531516
```
|
q2-jlbar/swin-tiny-patch4-window7-224-finetuned-eurosat
|
q2-jlbar
| 2022-06-06T14:24:15Z
| 80
| 0
|
transformers
|
[
"transformers",
"pytorch",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-01T21:36:01Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9618518518518518
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1199
- Accuracy: 0.9619
## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3627 | 0.99 | 47 | 0.1988 | 0.9389 |
| 0.2202 | 1.99 | 94 | 0.1280 | 0.9604 |
| 0.1948 | 2.99 | 141 | 0.1199 | 0.9619 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
galbraun/distilbert-base-uncased-finetuned-cola
|
galbraun
| 2022-06-06T14:20:34Z
| 6
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-06T12:30:33Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5517964161621091
---
<!-- 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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5277
- Matthews Correlation: 0.5518
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5221 | 1.0 | 535 | 0.5370 | 0.4246 |
| 0.3496 | 2.0 | 1070 | 0.5143 | 0.4892 |
| 0.2378 | 3.0 | 1605 | 0.5277 | 0.5518 |
| 0.1761 | 4.0 | 2140 | 0.7462 | 0.5303 |
| 0.1251 | 5.0 | 2675 | 0.7959 | 0.5414 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/briangrimmett
|
huggingtweets
| 2022-06-06T14:15:11Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-06T13:51:18Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/briangrimmett/1654524569583/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/1335009788212748291/X5EyBri8_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">Brian Grimmett</div>
<div style="text-align: center; font-size: 14px;">@briangrimmett</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 Brian Grimmett.
| Data | Brian Grimmett |
| --- | --- |
| Tweets downloaded | 3248 |
| Retweets | 1502 |
| Short tweets | 129 |
| Tweets kept | 1617 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3nan0dmd/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 @briangrimmett's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1mpmndjc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1mpmndjc/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/briangrimmett')
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)
|
ianspektor/q-FrozenLake-v1-8x8-noSlippery
|
ianspektor
| 2022-06-06T13:58:17Z
| 0
| 0
| null |
[
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-06T13:58:11Z
|
---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-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="ianspektor/q-FrozenLake-v1-8x8-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"])
```
|
Nitika/distilbert-base-uncased-finetuned-cola
|
Nitika
| 2022-06-06T13:26:34Z
| 4
| 0
|
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-06T13:23:16Z
|
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Nitika/distilbert-base-uncased-finetuned-cola
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. -->
# Nitika/distilbert-base-uncased-finetuned-cola
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: 0.1924
- Validation Loss: 0.4890
- Train Matthews Correlation: 0.5406
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2670, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Matthews Correlation | Epoch |
|:----------:|:---------------:|:--------------------------:|:-----:|
| 0.5210 | 0.4510 | 0.4918 | 0 |
| 0.3327 | 0.4885 | 0.5156 | 1 |
| 0.1924 | 0.4890 | 0.5406 | 2 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
SimulSt/distilbert-base-uncased-finetuned-emotion
|
SimulSt
| 2022-06-06T13:24:23Z
| 6
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-02T20:04:01Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.925
- name: F1
type: f1
value: 0.9250238763128368
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2202
- Accuracy: 0.925
- F1: 0.9250
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8285 | 1.0 | 250 | 0.3203 | 0.905 | 0.9008 |
| 0.2544 | 2.0 | 500 | 0.2202 | 0.925 | 0.9250 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
mmillet/rubert-tiny2_finetuned_emotion_experiment_augmented_anger_fear
|
mmillet
| 2022-06-06T12:57:24Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-06T11:20:53Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: rubert-tiny2_finetuned_emotion_experiment_augmented_anger_fear
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. -->
# rubert-tiny2_finetuned_emotion_experiment_augmented_anger_fear
This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4049
- Accuracy: 0.8779
- F1: 0.8775
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.3097 | 1.0 | 69 | 1.1369 | 0.6628 | 0.6210 |
| 0.949 | 2.0 | 138 | 0.7114 | 0.8225 | 0.8202 |
| 0.6288 | 3.0 | 207 | 0.5147 | 0.8507 | 0.8494 |
| 0.4724 | 4.0 | 276 | 0.4424 | 0.8643 | 0.8634 |
| 0.3912 | 5.0 | 345 | 0.4149 | 0.8653 | 0.8645 |
| 0.3283 | 6.0 | 414 | 0.3982 | 0.8664 | 0.8656 |
| 0.3015 | 7.0 | 483 | 0.3958 | 0.8685 | 0.8676 |
| 0.269 | 8.0 | 552 | 0.3888 | 0.8716 | 0.8712 |
| 0.2366 | 9.0 | 621 | 0.3909 | 0.8747 | 0.8742 |
| 0.2241 | 10.0 | 690 | 0.3991 | 0.8716 | 0.8707 |
| 0.1972 | 11.0 | 759 | 0.3984 | 0.8727 | 0.8720 |
| 0.1765 | 12.0 | 828 | 0.3940 | 0.8758 | 0.8753 |
| 0.1582 | 13.0 | 897 | 0.4049 | 0.8779 | 0.8775 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Shivam29rathore/finBert_10k
|
Shivam29rathore
| 2022-06-06T12:43:44Z
| 0
| 1
| null |
[
"region:us"
] | null | 2022-06-06T12:34:59Z
|
finBert_10k is a model that summarises the 10k documents, which are an essential part of the Investment management, so what's required is the text input and it is expected to give the summarized version of the text. It's fined tuned to the financial news summaries.
|
huggingartists/rammstein
|
huggingartists
| 2022-06-06T11:14:46Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/rammstein",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z
|
---
language: en
datasets:
- huggingartists/rammstein
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/29cedf8dd30a7458f4fca47d1c0f0eab.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Rammstein</div>
<a href="https://genius.com/artists/rammstein">
<div style="text-align: center; font-size: 14px;">@rammstein</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Rammstein.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/rammstein).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/rammstein")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/qt3qa1x1/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 Rammstein's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2yyigjzv) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2yyigjzv/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='huggingartists/rammstein')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/rammstein")
model = AutoModelWithLMHead.from_pretrained("huggingartists/rammstein")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
lorenzkuhn/distilbert-base-uncased-finetuned-squad
|
lorenzkuhn
| 2022-06-06T10:52:07Z
| 34
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-01T13:15:01Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
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_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3206
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2156 | 1.0 | 8235 | 1.1791 |
| 0.9413 | 2.0 | 16470 | 1.2182 |
| 0.7514 | 3.0 | 24705 | 1.3206 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingartists/elton-john
|
huggingartists
| 2022-06-06T10:32:19Z
| 5
| 1
|
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/elton-john",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z
|
---
language: en
datasets:
- huggingartists/elton-john
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/ec76d346c4c8b057169194c1781021fd.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elton John</div>
<a href="https://genius.com/artists/elton-john">
<div style="text-align: center; font-size: 14px;">@elton-john</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Elton John.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/elton-john).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/elton-john")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/188xpm2n/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 Elton John's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1rgstntu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1rgstntu/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='huggingartists/elton-john')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/elton-john")
model = AutoModelWithLMHead.from_pretrained("huggingartists/elton-john")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
sayakpramanik/distilbert-base-uncased-finetuned-emotion
|
sayakpramanik
| 2022-06-06T10:12:03Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-06T09:52:27Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.923
- name: F1
type: f1
value: 0.9228534433920637
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2166
- Accuracy: 0.923
- F1: 0.9229
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8472 | 1.0 | 250 | 0.3169 | 0.912 | 0.9105 |
| 0.2475 | 2.0 | 500 | 0.2166 | 0.923 | 0.9229 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
imamnurby/rob2rand_merged_w_prefix_c_fc_field
|
imamnurby
| 2022-06-06T09:40:39Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-06T09:38:04Z
|
---
tags:
- generated_from_trainer
model-index:
- name: rob2rand_merged_w_prefix_c_fc_field
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_merged_w_prefix_c_fc_field
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-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
### Framework versions
- Transformers 4.18.0
- Pytorch 1.7.1
- Datasets 2.1.0
- Tokenizers 0.12.1
|
yanekyuk/bert-uncased-keyword-discriminator
|
yanekyuk
| 2022-06-06T09:27:17Z
| 18
| 2
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-05T23:54:07Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
language:
- en
widget:
- text: "Broadcom agreed to acquire cloud computing company VMware in a $61 billion (€57bn) cash-and stock deal, massively diversifying the chipmaker’s business and almost tripling its software-related revenue to about 45% of its total sales. By the numbers: VMware shareholders will receive either $142.50 in cash or 0.2520 of a Broadcom share for each VMware stock. Broadcom will also assume $8 billion of VMware's net debt."
- text: "Canadian Natural Resources Minister Jonathan Wilkinson told Bloomberg that the country could start supplying Europe with liquefied natural gas (LNG) in as soon as three years by converting an existing LNG import facility on Canada’s Atlantic coast into an export terminal. Bottom line: Wilkinson said what Canada cares about is that the new LNG facility uses a low-emission process for the gas and is capable of transitioning to exporting hydrogen later on."
- text: "Google is being investigated by the UK’s antitrust watchdog for its dominance in the \"ad tech stack,\" the set of services that facilitate the sale of online advertising space between advertisers and sellers. Google has strong positions at various levels of the ad tech stack and charges fees to both publishers and advertisers. A step back: UK Competition and Markets Authority has also been investigating whether Google and Meta colluded over ads, probing into the advertising agreement between the two companies, codenamed Jedi Blue."
- text: "Shares in Twitter closed 6.35% up after an SEC 13D filing revealed that Elon Musk pledged to put up an additional $6.25 billion of his own wealth to fund the $44 billion takeover deal, lifting the total to $33.5 billion from an initial $27.25 billion. In other news: Former Twitter CEO Jack Dorsey announced he's stepping down, but would stay on Twitter’s board \\“until his term expires at the 2022 meeting of stockholders.\""
model-index:
- name: bert-uncased-keyword-discriminator
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-uncased-keyword-discriminator
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1296
- Precision: 0.8439
- Recall: 0.8722
- Accuracy: 0.9727
- F1: 0.8578
- Ent/precision: 0.8723
- Ent/accuracy: 0.9077
- Ent/f1: 0.8896
- Con/precision: 0.8010
- Con/accuracy: 0.8196
- Con/f1: 0.8102
## 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: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------------:|:------------:|:------:|:-------------:|:------------:|:------:|
| 0.1849 | 1.0 | 1875 | 0.1323 | 0.7039 | 0.7428 | 0.9488 | 0.7228 | 0.7522 | 0.8166 | 0.7831 | 0.6268 | 0.6332 | 0.6300 |
| 0.1357 | 2.0 | 3750 | 0.1132 | 0.7581 | 0.8024 | 0.9592 | 0.7796 | 0.7948 | 0.8785 | 0.8346 | 0.6971 | 0.6895 | 0.6933 |
| 0.0965 | 3.0 | 5625 | 0.1033 | 0.8086 | 0.7980 | 0.9646 | 0.8032 | 0.8410 | 0.8592 | 0.8500 | 0.7560 | 0.7071 | 0.7307 |
| 0.0713 | 4.0 | 7500 | 0.1040 | 0.8181 | 0.8435 | 0.9683 | 0.8306 | 0.8526 | 0.8906 | 0.8712 | 0.7652 | 0.7736 | 0.7694 |
| 0.0525 | 5.0 | 9375 | 0.1126 | 0.8150 | 0.8633 | 0.9689 | 0.8385 | 0.8495 | 0.9064 | 0.8770 | 0.7629 | 0.7993 | 0.7807 |
| 0.0386 | 6.0 | 11250 | 0.1183 | 0.8374 | 0.8678 | 0.9719 | 0.8523 | 0.8709 | 0.9020 | 0.8862 | 0.7877 | 0.8170 | 0.8021 |
| 0.03 | 7.0 | 13125 | 0.1237 | 0.8369 | 0.8707 | 0.9723 | 0.8535 | 0.8657 | 0.9079 | 0.8863 | 0.7934 | 0.8155 | 0.8043 |
| 0.0244 | 8.0 | 15000 | 0.1296 | 0.8439 | 0.8722 | 0.9727 | 0.8578 | 0.8723 | 0.9077 | 0.8896 | 0.8010 | 0.8196 | 0.8102 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
yanekyuk/bert-uncased-keyword-extractor
|
yanekyuk
| 2022-06-06T09:27:10Z
| 10,461
| 46
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-05T22:37:41Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
language:
- en
widget:
- text: "Broadcom agreed to acquire cloud computing company VMware in a $61 billion (€57bn) cash-and stock deal, massively diversifying the chipmaker’s business and almost tripling its software-related revenue to about 45% of its total sales. By the numbers: VMware shareholders will receive either $142.50 in cash or 0.2520 of a Broadcom share for each VMware stock. Broadcom will also assume $8 billion of VMware's net debt."
- text: "Canadian Natural Resources Minister Jonathan Wilkinson told Bloomberg that the country could start supplying Europe with liquefied natural gas (LNG) in as soon as three years by converting an existing LNG import facility on Canada’s Atlantic coast into an export terminal. Bottom line: Wilkinson said what Canada cares about is that the new LNG facility uses a low-emission process for the gas and is capable of transitioning to exporting hydrogen later on."
- text: "Google is being investigated by the UK’s antitrust watchdog for its dominance in the \"ad tech stack,\" the set of services that facilitate the sale of online advertising space between advertisers and sellers. Google has strong positions at various levels of the ad tech stack and charges fees to both publishers and advertisers. A step back: UK Competition and Markets Authority has also been investigating whether Google and Meta colluded over ads, probing into the advertising agreement between the two companies, codenamed Jedi Blue."
- text: "Shares in Twitter closed 6.35% up after an SEC 13D filing revealed that Elon Musk pledged to put up an additional $6.25 billion of his own wealth to fund the $44 billion takeover deal, lifting the total to $33.5 billion from an initial $27.25 billion. In other news: Former Twitter CEO Jack Dorsey announced he's stepping down, but would stay on Twitter’s board \\“until his term expires at the 2022 meeting of stockholders.\""
model-index:
- name: bert-uncased-keyword-extractor
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-uncased-keyword-extractor
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1247
- Precision: 0.8547
- Recall: 0.8825
- Accuracy: 0.9741
- F1: 0.8684
## 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: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|
| 0.165 | 1.0 | 1875 | 0.1202 | 0.7109 | 0.7766 | 0.9505 | 0.7423 |
| 0.1211 | 2.0 | 3750 | 0.1011 | 0.7801 | 0.8186 | 0.9621 | 0.7989 |
| 0.0847 | 3.0 | 5625 | 0.0945 | 0.8292 | 0.8044 | 0.9667 | 0.8166 |
| 0.0614 | 4.0 | 7500 | 0.0927 | 0.8409 | 0.8524 | 0.9711 | 0.8466 |
| 0.0442 | 5.0 | 9375 | 0.1057 | 0.8330 | 0.8738 | 0.9712 | 0.8529 |
| 0.0325 | 6.0 | 11250 | 0.1103 | 0.8585 | 0.8743 | 0.9738 | 0.8663 |
| 0.0253 | 7.0 | 13125 | 0.1204 | 0.8453 | 0.8825 | 0.9735 | 0.8635 |
| 0.0203 | 8.0 | 15000 | 0.1247 | 0.8547 | 0.8825 | 0.9741 | 0.8684 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Nawaphong-zax/wangchanberta-base-att-spm-uncased-finetuned-cosme
|
Nawaphong-zax
| 2022-06-06T08:52:29Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"camembert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-06T07:12:12Z
|
---
tags:
- generated_from_trainer
model-index:
- name: wangchanberta-base-att-spm-uncased-finetuned-cosme
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. -->
# wangchanberta-base-att-spm-uncased-finetuned-cosme
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9973
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1386 | 1.0 | 391 | 1.9939 |
| 2.1301 | 2.0 | 782 | 1.9974 |
| 2.1296 | 3.0 | 1173 | 2.0013 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.11.0+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
RuiqianLi/wav2vec2-large-xls-r-300m-chinese-taiwan-colab
|
RuiqianLi
| 2022-06-06T08:12:49Z
| 7
| 1
|
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
| 2022-05-17T15:38:10Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-chinese-taiwan-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-chinese-taiwan-colab
!!!this model has just been trained with very high learning rate and small epochs, please do not use this to do the speech to text.
!!!It's just a test, I'll retrain this model with more time later when I have time.
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: 0.1
- 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: 2
- 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
|
anlausch/aq_bert_ibm
|
anlausch
| 2022-06-06T08:10:46Z
| 10
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-06T07:28:24Z
|
---
license: mit
---
Model trained on IBMArgRank30k for 2 epochs with a learning rate of 3e-5 (optimised via grid search) in a similar way as in Lauscher et al. 2020 (see below). The original model was Tensorflow-based. This model corresponds to a reimplementation with Transformers & PyTorch.
```
@inproceedings{lauscher-etal-2020-rhetoric,
title = "Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing",
author = "Lauscher, Anne and
Ng, Lily and
Napoles, Courtney and
Tetreault, Joel",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.402",
doi = "10.18653/v1/2020.coling-main.402",
pages = "4563--4574",
abstract = "Though preceding work in computational argument quality (AQ) mostly focuses on assessing overall AQ, researchers agree that writers would benefit from feedback targeting individual dimensions of argumentation theory. However, a large-scale theory-based corpus and corresponding computational models are missing. We fill this gap by conducting an extensive analysis covering three diverse domains of online argumentative writing and presenting GAQCorpus: the first large-scale English multi-domain (community Q{\&}A forums, debate forums, review forums) corpus annotated with theory-based AQ scores. We then propose the first computational approaches to theory-based assessment, which can serve as strong baselines for future work. We demonstrate the feasibility of large-scale AQ annotation, show that exploiting relations between dimensions yields performance improvements, and explore the synergies between theory-based prediction and practical AQ assessment.",
}
```
|
HWJin/SMU-NLP-assignment2-finetuned-best
|
HWJin
| 2022-06-06T08:05:31Z
| 4
| 0
|
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-06T07:55:04Z
|
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: HWJin/SMU-NLP-assignment2-finetuned-best
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. -->
# HWJin/SMU-NLP-assignment2-finetuned-best
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9936
- Validation Loss: 0.9867
- Epoch: 13
## 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': 990, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 10, '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 |
|:----------:|:---------------:|:-----:|
| 1.6490 | 1.2199 | 0 |
| 1.2679 | 1.1622 | 1 |
| 1.1796 | 1.0931 | 2 |
| 1.1200 | 1.0274 | 3 |
| 1.0841 | 1.0739 | 4 |
| 1.0567 | 1.0317 | 5 |
| 1.0164 | 0.9895 | 6 |
| 0.9819 | 1.0365 | 7 |
| 0.9960 | 0.9857 | 8 |
| 1.0143 | 0.9954 | 9 |
| 1.0156 | 1.0173 | 10 |
| 0.9915 | 1.0391 | 11 |
| 1.0246 | 1.0288 | 12 |
| 0.9936 | 0.9867 | 13 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Ripsky/Poop
|
Ripsky
| 2022-06-06T06:39:37Z
| 0
| 0
| null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-06-06T06:32:10Z
|
---
title: Poop
emoji: 🥑
colorFrom: yellow
colorTo: green
sdk: static
pinned: True
license: apache-2.0
---
|
mindwrapped/dqn-MountainCar-v0
|
mindwrapped
| 2022-06-06T06:07:52Z
| 1
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-06T06:07:19Z
|
---
library_name: stable-baselines3
tags:
- MountainCar-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: -104.89 +/- 20.36
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MountainCar-v0
type: MountainCar-v0
---
# **DQN** Agent playing **MountainCar-v0**
This is a trained model of a **DQN** agent playing **MountainCar-v0**
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
...
```
|
nestoralvaro/mT5_multilingual_XLSum-finetuned-xsum-mlsum___summary_text
|
nestoralvaro
| 2022-06-06T03:26:11Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:mlsum",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-05T17:24:08Z
|
---
tags:
- generated_from_trainer
datasets:
- mlsum
metrics:
- rouge
model-index:
- name: mT5_multilingual_XLSum-finetuned-xsum-mlsum___summary_text
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: mlsum
type: mlsum
args: es
metrics:
- name: Rouge1
type: rouge
value: 0.0
---
<!-- 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. -->
# mT5_multilingual_XLSum-finetuned-xsum-mlsum___summary_text
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on the mlsum dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 66592 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Chetan1997/layoutlmv2-finetuned-funsd-test
|
Chetan1997
| 2022-06-06T03:20:00Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-06T02:23:11Z
|
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2-finetuned-funsd-test
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. -->
# layoutlmv2-finetuned-funsd-test
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.8.0+cu101
- Datasets 2.2.2
- Tokenizers 0.12.1
|
mindwrapped/ppo-BipedalWalker-v3
|
mindwrapped
| 2022-06-06T03:16:20Z
| 3
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"BipedalWalker-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-10T23:46:10Z
|
---
library_name: stable-baselines3
tags:
- BipedalWalker-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 185.82 +/- 92.04
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BipedalWalker-v3
type: BipedalWalker-v3
---
# **PPO** Agent playing **BipedalWalker-v3**
This is a trained model of a **PPO** agent playing **BipedalWalker-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
0xrushi/q-FrozenLake-v1-4x4-noSlippery
|
0xrushi
| 2022-06-06T02:14:51Z
| 0
| 0
| null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-06T02:13:07Z
|
---
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="rushic24/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"])
```
|
gokuls/tiny-bert-sst2-distilled-model
|
gokuls
| 2022-06-06T01:31:55Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-05T22:07:15Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: tiny-bert-sst2-distilled-model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.838302752293578
---
<!-- 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. -->
# tiny-bert-sst2-distilled-model
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2592
- Accuracy: 0.8383
## 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: 6e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5303 | 1.0 | 4210 | 1.2542 | 0.8222 |
| 0.4503 | 2.0 | 8420 | 1.1260 | 0.8211 |
| 0.3689 | 3.0 | 12630 | 1.2325 | 0.8234 |
| 0.3122 | 4.0 | 16840 | 1.2533 | 0.8337 |
| 0.2764 | 5.0 | 21050 | 1.2726 | 0.8337 |
| 0.254 | 6.0 | 25260 | 1.2609 | 0.8337 |
| 0.2358 | 7.0 | 29470 | 1.2592 | 0.8383 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.10.1+cu113
- Datasets 1.15.1
- Tokenizers 0.12.1
|
jppaolim/v55_Large_2E
|
jppaolim
| 2022-06-06T01:24:38Z
| 11
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-06T00:33:50Z
|
# My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur is bored and wanted to go the beach. His friends suggest he drive to the beach. Arthur gets a ride and they take off. Arthur takes a nap and has a good time. He has so much fun at the beach he doesn't want to leave.
Arthur goes to the beach. Arthur is feeling very hungry. He decides to go to the beach. Arthur gets some food. Arthur puts his food in his cooler. Arthur goes home and doesn't feel hungry any more.
Arthur goes to the beach. Arthur always wanted to go to the beach. He saved up money so he could take his dream trip. Finally he went to the beach and it was so beautiful. He loved his trip to the beach and decided he would go again. Arthur packed his bags and went to the beach.
Arthur goes to the beach. Arthur went to the beach last weekend. He swam on the sand and looked at the ocean. He saw several people walking around on the beach. Arthur stopped to talk to them. Arthur went home and told his mother about his trip.
Arthur goes to the beach. Arthur is so excited for the weekend. He knows he needs a new bathing suit. He finds the perfect one at the beach. He spends the day relaxing and exploring the shore. Arthur cannot wait for the next trip to the beach.
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1.05}
Arthur goes to the beach. Arthur is playing with his friends in the sand at the beach. His friend Tom comes by and invites him to join them. Arthur loves the beach. Arthur spends the afternoon playing in the sand. Arthur and Tom have a great day at the beach.
Arthur goes to the beach. Arthur was going to the beach. He packed his towel and his sunscreen. He drove his car to the beach. Arthur swam in the ocean. Arthur had fun at the beach.
Arthur goes to the beach. Arthur is bored one day and decides he wants to go to the beach. He packs up his surfboard, towel, and sunscreen. Arthur goes to the ocean and spends the day there. He goes home and tells his mom about his day. Arthur is happy that he took a trip to the beach.
Arthur goes to the beach. Arthur loved the beach. He got his towel and sandals. He went out into the ocean. Arthur was shocked by the cold ocean. He decided he needed to go back home.
Arthur goes to the beach. Arthur really wants to go to the beach. His friend tells him it is too hot out. Arthur convinces his friend to come with him. They drive to the beach. Arthur spends the day playing in the ocean.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.8, 'repetition_penalty': 1.1}
Arthur goes to the beach. Arthur is going to the beach. He has packed his beach towel and sunscreen. Once he gets to the beach he finds a spot to sit down. He relaxes for a while and then swims in the water. Arthur loves the beach!
Arthur goes to the beach. Arthur is very bored. He decides to head to the beach. At the beach he relaxes on the sand. Then he gets out of his car and checks out. Arthur has spent the day at the beach.
Arthur goes to the beach. Arthur had always wanted to visit the ocean. He has saved his money for many Years. Finally he saves up enough money. Arthur takes a trip to the beach. He spends the whole day in the ocean.
Arthur goes to the beach. Arthur was so excited that he had packed his swimming trunks. He was going to the beach and he couldn't wait to swim! When he got to the beach, he saw it was closed for cleaning. He asked his mom if she would take him to the beach anyway. She said yes, but Arthur could have a picnic instead.
Arthur goes to the beach. Arthur is going to the beach with his friends today. He needs a bathing suit but doesn't have one. He decides to go without a bathing suit. When he gets there, he sees that they have a long line. Arthur finally finds a nice one and swims in the water.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.6, 'repetition_penalty': 1.15}
Arthur goes to the beach. Arthur is going on vacation with his family. He asks if they want to go to the beach. They agree and he drives them there. When they get to the beach, Arthur falls in love with a beautiful girl. Arthur and his family spend the rest of their trip together.
Arthur goes to the beach. Arthur is very bored on a hot day. He decides he needs something to do. He heads down to the local beach. He spends all day playing in the sand and sun. Arthur is happy that he no longer feels bored.
Arthur goes to the beach. Arthur was bored one day. He decided to go to the beach. Arthur packed a towel and sunscreen. Then, he went out into the ocean. Arthur had fun at the beach.
Arthur goes to the beach. Arthur was bored at home one day. He decided he would go to the beach. Arthur packed up his car and drove to the beach. Arthur laid on the sand enjoying the sun. Afterwards, Arthur went back home.
Arthur goes to the beach. Arthur was bored one afternoon so he decided to go to the beach. He packed his cooler and drove to the beach. Arthur found a spot on the sand that looked nice. He laid out his towel and sunblock and went for a swim. Arthur had such a great time at the beach!
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.4, 'repetition_penalty': 1.2}
Arthur goes to the beach. Arthur was bored one day and wanted something to do. He decided to go to the beach. At the beach he played in the sand. Then he went swimming in the ocean. Finally, he came back home exhausted but happy.
Arthur goes to the beach. Arthur is bored one day and wants something to do. He decides he would like to go to the beach. Arthur packs up his car and drives to the beach. Once there, he spends a few hours playing in the sand. Afterwards, Arthur has a good time at the beach.
Arthur goes to the beach. Arthur is bored one day and decides to go to the beach. He packs up his towel, swims in the ocean, and gets out of his car. When he arrives at the beach it's very sunny and nice. Arthur spends all day playing in the water. Afterwards, he comes home and rests for a bit.
Arthur goes to the beach. Arthur is bored one day. He decides he needs something to do. He calls his friend Steve and asks if they want to go to the beach. Steve tells Arthur that it's not a good idea to go to the beach. Now Arthur knows that he should have asked Steve for advice.
Arthur goes to the beach. Arthur is bored at home one day. He decides he needs something to do. He heads out to the local beach and plays in the sand. At the beach, Arthur sees many beautiful people. Arthur feels happy that he no longer feels bored.
|
TinySuitStarfish/q-Taxi-v3
|
TinySuitStarfish
| 2022-06-06T00:23:40Z
| 0
| 0
| null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-06T00:23:34Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.48 +/- 2.65
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="TinySuitStarfish/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"])
```
|
kabelomalapane/En-Af
|
kabelomalapane
| 2022-06-05T23:47:35Z
| 76
| 0
|
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-06-05T20:04:44Z
|
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: En-Af
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. -->
# En-Af
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-af](https://huggingface.co/Helsinki-NLP/opus-mt-en-af) on the None dataset.
It achieves the following results on the evaluation set:
Before training:
- 'eval_bleu': 35.055184951449
- 'eval_loss': 2.225693941116333
After training:
- Loss: 2.0057
- Bleu: 44.2309
## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
phyous/ppo-LunarLander-v2
|
phyous
| 2022-06-05T23:44:11Z
| 0
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-05T23:43:39Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 183.81 +/- 70.78
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
...
```
|
limsc/reqroberta-tapt-epoch43
|
limsc
| 2022-06-05T23:29:26Z
| 6
| 0
|
transformers
|
[
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-05T23:29:13Z
|
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: reqroberta-tapt-epoch43
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. -->
# reqroberta-tapt-epoch43
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:
## 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': 37100, '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: float32
### Training results
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
limsc/reqroberta-tapt-epoch33
|
limsc
| 2022-06-05T23:18:27Z
| 5
| 0
|
transformers
|
[
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-05T23:18:14Z
|
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: reqroberta-tapt-epoch33
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. -->
# reqroberta-tapt-epoch33
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:
## 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': 37100, '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: float32
### Training results
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
limsc/reqroberta-tapt-epoch20
|
limsc
| 2022-06-05T23:07:33Z
| 4
| 0
|
transformers
|
[
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-05T23:07:19Z
|
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: reqroberta-tapt-epoch20
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. -->
# reqroberta-tapt-epoch20
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:
## 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': 37100, '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: float32
### Training results
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
poltoran/RL-course-1-unit-ppo-LunarLander-v2-v1
|
poltoran
| 2022-06-05T21:33:23Z
| 1
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-05T21:16:23Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 284.67 +/- 17.68
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
...
```
|
AlphaZetta/finetuning-sentiment-model-finefood
|
AlphaZetta
| 2022-06-05T20:08:23Z
| 10
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-05T19:53:56Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-finefood
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. -->
# finetuning-sentiment-model-finefood
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2269
- Accuracy: 0.95
- F1: 0.9696
## 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: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
jppaolim/v54_Large_AdaMW
|
jppaolim
| 2022-06-05T19:38:49Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-05T18:46:40Z
|
# My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur was at the beach. His parents got him a towel for the trip. He lay down and got out of the sand. Arthur put on his towel and went to the ocean. He felt very refreshed as he surfed and swam for a bit.
Arthur goes to the beach. Arthur has always been scared to go to the beach. But his friends convinced him to go. Arthur decided to try it. He found the water to be really cold. He turned around and went back to the car.
Arthur goes to the beach. Arthur was very lonely. He decided to go to the beach. He packed his bathing suit and towel. He got ready to go to the beach. Arthur arrived at the beach and relaxed on his chair.
Arthur goes to the beach. Arthur loved to surf and was always looking for new places to surf. He decided to head to the beach with his friends. Arthur drove for hours to find the spot and found it. Arthur and his friends went in and made it their new place. Arthur and his friends spent all day playing in the sun.
Arthur goes to the beach. Arthur really wanted to go to the beach. Arthur was afraid of the cold water. Arthur called a friend for a swim meetup. Arthur met up with his friend. Arthur had a fun time at the beach at the end of the day.
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1.05}
Arthur goes to the beach. Arthur loves to swim. He decides to go swimming at the beach. Arthur gets a towel and a water bottle. He swam all afternoon. At the end of the day, he was soaked!
Arthur goes to the beach. Arthur always wanted to go to the beach. One day his friends told him he had to go. Arthur called the beach and made plans. The next morning he drove to the beach. Arthur had a great time at the beach that day!
Arthur goes to the beach. Arthur was always bored with life. He had no idea where to go on vacation. Arthur decided to go to the beach. He packed up his bag and drove to the beach. Arthur found it so much fun that he left the city.
Arthur goes to the beach. Arthur went to the beach with his friends. They decided to go swimming. Arthur thought it would be fun to jump in the water. He splashed around until the sun was shining in the sky. After the sun came up, Arthur swam out into the ocean.
Arthur goes to the beach. Arthur was feeling lonely one day. He decided to go to the beach. He packed his bag and drove to the beach. He walked to the beach and looked for many people. The people were nice and he met a new friend.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.8, 'repetition_penalty': 1.1}
Arthur goes to the beach. Arthur is going to the beach. His family tells him not to go because they have been looking forward to it. He decides to go anyway. Arthur finds the beach very relaxing. He is glad he went to the beach.
Arthur goes to the beach. Arthur had never been to the beach before. He decided to go one day. Arthur packed a bag of snacks for the trip. He made his way to the beach. When he got there, he found out it was very sunny.
Arthur goes to the beach. Arthur was having a great time at the beach with his family. He was playing in the water when he saw an angry turtle. The turtle had attacked the boat that Arthur was on. Arthur ran away as fast as he could, hoping no one would see him. But then, a huge wave crashed against the shore!
Arthur goes to the beach. Arthur is bored and decides he wants to go to the beach. He arrives at the beach and sets up his tent. He then sets up a chair and a picnic table for himself. Finally, he lays down and gets ready to go. Arthur has a great time at the beach at the end of the day!
Arthur goes to the beach. Arthur always wanted to go to the beach. His friends told him he was too old to go. Finally his parents took him out of school and took him. He drove to the beach and got his sandals and towels ready. When Arthur went to the beach, he realized it was not as bad as he thought.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.6, 'repetition_penalty': 1.15}
Arthur goes to the beach. Arthur was going to go to the beach with his friends. He packed up his things and drove to the beach. When he got there, it was very crowded. Arthur had to wait a long time to get his sandals. Finally, he finally arrived at the beach and played in the water.
Arthur goes to the beach. Arthur was very excited about going on a trip to the beach. He packed up his car and drove to the beach. When he arrived, he saw that it was very crowded. Arthur realized that he had forgotten his sunscreen! Arthur decided not to go to the beach.
Arthur goes to the beach. Arthur was out on a date with his girlfriend. They went to the beach and had fun swimming in the water. Afterwards, they walked around the beach for awhile. After walking, they saw a beautiful sunset. Finally, they left the beach and went home.
Arthur goes to the beach. Arthur was excited for his trip to the beach. He packed up his car and drove out to the beach. Once he got there, Arthur realized it was really hot outside. The air conditioning in his car was broken. Arthur decided to leave without going to the beach.
Arthur goes to the beach. Arthur wanted to go to the beach. He got his friends together and they all went to the beach. They played in the sand for a while then swam in the water. Finally, Arthur was tired but still had fun. Arthur decided he would go back next summer.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.4, 'repetition_penalty': 1.2}
Arthur goes to the beach. Arthur is feeling very bored one day. He decides he needs something to do. He heads out to the beach and finds a spot. He plays in the sand for hours. Finally, he is happy that he no longer feels bored.
Arthur goes to the beach. Arthur was going to go to the beach with his friends. He had never been before but he decided to try it. They all packed up their things and headed out. When they got there, Arthur realized that he forgot his sunscreen! Luckily, his friend brought him a bottle of water so he could use it.
Arthur goes to the beach. Arthur had always wanted to go to the beach. He saved up his money for a week and finally went on vacation. On the day of his trip, he was so excited that he forgot all about work! He spent hours at the beach and even more when he got home. Afterwards, he decided he would never forget to pay attention to work again.
Arthur goes to the beach. Arthur is feeling very tired one day. He decides he needs something to do. He calls his friend and asks him if he wants to go to the beach. His friend says yes. They spend the afternoon playing in the sand.
Arthur goes to the beach. Arthur had always wanted to go to the beach. He saved up for a few months so he could take his trip. Finally, Arthur went to the beach and spent all day playing in the water. Afterwards, he was very tired but happy that he finally got to the beach. The next morning, he decided it would be best to go back home.
|
AlphaZetta/finetuning-sentiment-model-3000-samples
|
AlphaZetta
| 2022-06-05T19:32:45Z
| 17
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-04T18:00:44Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4338
- Accuracy: 0.85
- F1: 0.9189
## 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: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
limsc/reqscibert-tapt-epoch10
|
limsc
| 2022-06-05T18:40:32Z
| 7
| 0
|
transformers
|
[
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-05T18:40:20Z
|
---
tags:
- generated_from_keras_callback
model-index:
- name: reqscibert-tapt-epoch10
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. -->
# reqscibert-tapt-epoch10
This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_scivocab_cased) 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': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 34950, '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: float32
### Training results
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
limsc/reqscibert-tapt-epoch31
|
limsc
| 2022-06-05T18:21:49Z
| 4
| 0
|
transformers
|
[
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-05T18:20:21Z
|
---
tags:
- generated_from_keras_callback
model-index:
- name: reqscibert-tapt-epoch31
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. -->
# reqscibert-tapt-epoch31
This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_scivocab_cased) 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': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 34950, '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: float32
### Training results
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
anvay/finetuning-cardiffnlp-sentiment-model
|
anvay
| 2022-06-05T17:46:13Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-05T16:44:10Z
|
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuning-cardiffnlp-sentiment-model
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. -->
# finetuning-cardiffnlp-sentiment-model
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2685
- Accuracy: 0.9165
## 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
|
Edric111/distilbert-base-uncased-finetuned-ner
|
Edric111
| 2022-06-05T16:32:56Z
| 14
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-03T17:07:49Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9273854328093868
- name: Recall
type: recall
value: 0.9372413021590782
- name: F1
type: f1
value: 0.9322873198686918
- name: Accuracy
type: accuracy
value: 0.9840341874910639
---
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0599
- Precision: 0.9274
- Recall: 0.9372
- F1: 0.9323
- Accuracy: 0.9840
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2378 | 1.0 | 878 | 0.0719 | 0.9107 | 0.9200 | 0.9154 | 0.9801 |
| 0.0509 | 2.0 | 1756 | 0.0620 | 0.9156 | 0.9311 | 0.9233 | 0.9821 |
| 0.0307 | 3.0 | 2634 | 0.0599 | 0.9274 | 0.9372 | 0.9323 | 0.9840 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
yanekyuk/berturk-128k-keyword-discriminator
|
yanekyuk
| 2022-06-05T12:54:08Z
| 8
| 1
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"tr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-04T23:57:09Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
language:
- tr
widget:
- text: "İngiltere'de düzenlenen Avrupa Tekvando ve Para Tekvando Şampiyonası’nda millî tekvandocular 5 altın, 2 gümüş ve 4 bronz olmak üzere 11, millî para tekvandocular ise 4 altın, 3 gümüş ve 1 bronz olmak üzere 8 madalya kazanarak takım halinde Avrupa şampiyonu oldu."
- text: "Füme somon dedik ama aslında lox salamuralanmış somon anlamına geliyor, füme etme opsiyonel. Lox bagel, 1930'larda Eggs Benedict furyasında New Yorklu Yahudi cemaati tarafından koşer bir alternatif olarak çıkan bir lezzet. Günümüzde benim hangover yüreğim dâhil dünyanın birçok yerinde enfes bir kahvaltı sandviçi."
- text: "Türkiye'de son aylarda sıklıkla tartışılan konut satışı karşılığında yabancılara vatandaşlık verilmesi konusunu beyin göçü kapsamında ele almak mümkün. Daha önce 250 bin dolar olan vatandaşlık bedeli yükselen tepkiler üzerine 400 bin dolara çıkarılmıştı. Türkiye'den göç eden iyi eğitimli kişilerin , gittikleri ülkelerde 250 bin dolar tutarında yabancı yatırıma denk olduğu göz önüne alındığında nitelikli insan gücünün yabancılara konut karşılığında satılan vatandaşlık bedelin eş olduğunu görüyoruz. Yurt dışına giden her bir vatandaşın yüksek teknolojili katma değer üreten sektörlere yapacağı katkılar göz önünde bulundurulduğunda bu açığın inşaat sektörüyle kapatıldığını da görüyoruz. Beyin göçü konusunda sadece ekonomik perspektiften bakıldığında bile kısa vadeli döviz kaynağı yaratmak için kullanılan vatandaşlık satışı yerine beyin göçünü önleyecek önlemler alınmasının ülkemize çok daha faydalı olacağı sonucunu çıkarıyoruz."
- text: "Türkiye’de resmî verilere göre, 15 ve daha yukarı yaştaki kişilerde mevsim etkisinden arındırılmış işsiz sayısı, bu yılın ilk çeyreğinde bir önceki çeyreğe göre 50 bin kişi artarak 3 milyon 845 bin kişi oldu. Mevsim etkisinden arındırılmış işsizlik oranı ise 0,1 puanlık artışla %11,4 seviyesinde gerçekleşti. İşsizlik oranı, ilk çeyrekte geçen yılın aynı çeyreğine göre 1,7 puan azaldı."
- text: "Boeing’in insansız uzay aracı Starliner, birtakım sorunlara rağmen Uluslararası Uzay İstasyonuna (ISS) ulaşarak ilk kez başarılı bir şekilde kenetlendi. Aracın ISS’te beş gün kalmasını takiben sorunsuz bir şekilde New Mexico’ya inmesi halinde Boeing, sonbaharda astronotları yörüngeye göndermek için Starliner’ı kullanabilir.\n\nNeden önemli? NASA’nın personal aracı üretmeyi durdurmasından kaynaklı olarak görevli astronotlar ve kozmonotlar, ISS’te Rusya’nın ürettiği uzay araçları ile taşınıyordu. Starliner’ın kendini kanıtlaması ise bu konuda Rusya’ya olan bağımlılığın potansiyel olarak ortadan kalkabileceği anlamına geliyor."
model-index:
- name: berturk-128k-keyword-discriminator
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. -->
# berturk-128k-keyword-discriminator
This model is a fine-tuned version of [dbmdz/bert-base-turkish-128k-cased](https://huggingface.co/dbmdz/bert-base-turkish-128k-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3828
- Precision: 0.6791
- Recall: 0.7234
- Accuracy: 0.9294
- F1: 0.7006
- Ent/precision: 0.6931
- Ent/accuracy: 0.7715
- Ent/f1: 0.7302
- Con/precision: 0.6473
- Con/accuracy: 0.6282
- Con/f1: 0.6376
## 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: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------------:|:------------:|:------:|:-------------:|:------------:|:------:|
| 0.1632 | 1.0 | 1875 | 0.1637 | 0.6661 | 0.6900 | 0.9320 | 0.6778 | 0.6649 | 0.7401 | 0.7005 | 0.6692 | 0.5907 | 0.6275 |
| 0.1151 | 2.0 | 3750 | 0.1709 | 0.6538 | 0.7446 | 0.9292 | 0.6963 | 0.6682 | 0.7864 | 0.7225 | 0.6223 | 0.6619 | 0.6415 |
| 0.0817 | 3.0 | 5625 | 0.1931 | 0.6667 | 0.7292 | 0.9294 | 0.6965 | 0.6843 | 0.7677 | 0.7236 | 0.6290 | 0.6529 | 0.6407 |
| 0.057 | 4.0 | 7500 | 0.2375 | 0.6578 | 0.7486 | 0.9277 | 0.7002 | 0.6708 | 0.7950 | 0.7277 | 0.6284 | 0.6567 | 0.6422 |
| 0.041 | 5.0 | 9375 | 0.2765 | 0.6683 | 0.7390 | 0.9284 | 0.7019 | 0.6834 | 0.7821 | 0.7294 | 0.6351 | 0.6538 | 0.6444 |
| 0.0297 | 6.0 | 11250 | 0.3128 | 0.6811 | 0.7249 | 0.9295 | 0.7023 | 0.6979 | 0.7710 | 0.7327 | 0.6438 | 0.6334 | 0.6386 |
| 0.0211 | 7.0 | 13125 | 0.3633 | 0.6780 | 0.7236 | 0.9290 | 0.7001 | 0.6919 | 0.7722 | 0.7299 | 0.6463 | 0.6273 | 0.6366 |
| 0.0165 | 8.0 | 15000 | 0.3828 | 0.6791 | 0.7234 | 0.9294 | 0.7006 | 0.6931 | 0.7715 | 0.7302 | 0.6473 | 0.6282 | 0.6376 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
sriiikar/wav2vec2-hbtest-2
|
sriiikar
| 2022-06-05T12:50:35Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-05T06:34:46Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-hbtest-2
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-hbtest-2
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: 5.9927
- Wer: 1.1562
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.6105 | 6.41 | 1000 | 4.9969 | 1.2600 |
| 0.3723 | 12.82 | 2000 | 5.1370 | 1.1185 |
| 0.1537 | 19.23 | 3000 | 5.5541 | 1.1419 |
| 0.0992 | 25.64 | 4000 | 5.9309 | 1.1269 |
| 0.0722 | 32.05 | 5000 | 5.9545 | 1.1628 |
| 0.0593 | 38.46 | 6000 | 5.9927 | 1.1562 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.3.dev0
- Tokenizers 0.12.1
|
huggingtweets/drsweety303
|
huggingtweets
| 2022-06-05T12:49:47Z
| 6
| 2
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z
|
---
language: en
thumbnail: http://www.huggingtweets.com/drsweety303/1654433312789/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/1377788200199065611/vkwcelvm_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">Dr. Roberta Bobby</div>
<div style="text-align: center; font-size: 14px;">@drsweety303</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 Dr. Roberta Bobby.
| Data | Dr. Roberta Bobby |
| --- | --- |
| Tweets downloaded | 3227 |
| Retweets | 415 |
| Short tweets | 346 |
| Tweets kept | 2466 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3s665txj/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 @drsweety303's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2eynd20x) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2eynd20x/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/drsweety303')
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)
|
haritzpuerto/MiniLM-L12-H384-uncased-squad
|
haritzpuerto
| 2022-06-05T12:25:22Z
| 11
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-30T21:35:09Z
|
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- exact_match: 77.57805108798486
- f1: 85.73943867549627
- Loss: 1.0744
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1738 | 1.0 | 5475 | 1.0744 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
kzvdar42/ppo-LunarLander-v2
|
kzvdar42
| 2022-06-05T12:03:25Z
| 0
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-05T11:24:26Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 272.45 +/- 17.12
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
...
```
|
jppaolim/v52_Large
|
jppaolim
| 2022-06-05T11:04:46Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-05T10:25:54Z
|
# My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur was driving home from work. He had to stop at a gas station. He bought a lottery ticket and bought some tickets. He bought some lottery tickets and played them all. He got really lucky and was won the jack jack jack jack jack prize.
Arthur goes to the beach. Arthur decides to go to the beach. First he has to sit on the sand. Next, he would walk the beach. Finally, he can go to the park. Once they get to the beach, he'll go swimming.
Arthur goes to the beach. Arthur goes to the beach. He is bored while watching the sand. He takes a nap. Finally he goes to the water. He gets a nap and heads home.
Arthur goes to the beach. Arthur decides to go to the beach. He begins to put on his shoes and walk to the beach. Finally he comes home to find his dad with him. He is happy he got to see his dad and his dad were together. He decides to go home to rest.
Arthur goes to the beach. Arthur went to the beach to play volleyball. He was excited to be there playing. After playing, his foot broke his hip. His dad had to take him to the hospital. Luckily, the injury was minimal and he went back to playing.
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1.05}
Arthur goes to the beach. Arthur decides to go to the beach. He begins his day by going to the beach with his dad. At the beach, he played for his friends and watched a movie. Afterwards, they went to a local bar after the movie. They had a good time.
Arthur goes to the beach. Arthur decided to go to the beach with his friends. They had a fun day there and played volleyball all day. He had a good time playing in the beach. His friend ended up taking him out for dinner that night. He had a nice meal with friends.
Arthur goes to the beach. Arthur was going to the beach on Friday. He decided to take his son to the beach. His son spent the weekend playing. In the afternoon his mom went to the mall to see the ocean. She loved watching the ocean.
Arthur goes to the beach. A friend invited me to go to the beach. I agreed and put on my favorite outfit. It took me forever to find my bathing suit, and it was a lot difficult. The ride to the beach was worth a great day!
Arthur goes to the beach. Arthur decided that he wanted to go to the beach. He went to the beach. After a few hours, he left the beach and went to the water. While swimming he found his mother asleep and was able to see the world. When he woke up, he felt very happy.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.8, 'repetition_penalty': 1.1}
Arthur goes to the beach. Arthur was on vacation in Hawaii. He decided to go to the beach. He rented a car and drove to the beach. He went for an hour relaxing in the water. After the hours, he went home.
Arthur goes to the beach. Arthur wanted to go to the beach with his friends. They drove to the beach and spent the day relaxing and relaxing. When they got home, they decided to play volleyball. Afterwards, they all played volleyball and had a great time. After that, they had a nice dinner and had a wonderful time.
Arthur goes to the beach. Arthur is going to the beach with his family. He decides to take his family and his family to the beach. While there, they watch the ocean and relax. Afterwards, they spent the day playing volleyball. After the sun was over, they headed home.
Arthur goes to the beach. Arthur was going to go to the beach with his friend. They decided to take a road trip to Las Vegas. Once they arrived, the friends began to spend a day relaxing and playing. After a few hours of relaxing, he went home after playing video games. When he got home, his friend let him stay out on the beach.
Arthur goes to the beach. Arthur wanted to go to the beach with his friends. He invited some friends. The friends played volleyball and the football. They had fun. At the end of the day, they all left to go home.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.6, 'repetition_penalty': 1.15}
Arthur goes to the beach. Arthur went to the beach with his family. He played volleyball and had fun playing. The other kids decided to play volleyball. They made fun of him for not being able to play. His dad was happy that he got a chance to go to the beach.
Arthur goes to the beach. Arthur is going to the beach with his dad. His father takes him to a different park. He spends hours playing volleyball. After that, he heads home and relax on his couch. When he gets home, his mom tells him he can't play.
Arthur goes to the beach. Arthur is out on a boat with his friends. He decides to go to the beach. While there, he sees a bunch of people. He spends time talking and playing volleyball. He has fun at the beach all day.
Arthur goes to the beach. Arthur was going to go to the beach with his friends. They wanted to spend time together. He decided to take them to the beach for a swim. When they arrived, he had a blast relaxing on the sand. His friends swam and played until he went home.
Arthur goes to the beach. Arthur had never been to the beach before. He decided to go to the beach with his friends. When they got there, he was amazed by all of the beauty and sea life. He decided that going to the beach would be the most fun he had! After a few hours of fun, he decided to go home.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.4, 'repetition_penalty': 1.2}
Arthur goes to the beach. Arthur was invited to go to the beach with his friends. He decided that he would like to swim. He spent a few hours swimming and playing volleyball. When he got home, he realized that he forgot his sunscreen. He went back to the beach without any more sunblock.
Arthur goes to the beach. Arthur is going to the beach with his friends. He wants to go swimming and play volleyball. His friends decide to take him to a park. The boys spend the day playing volleyball and playing volleyball. After the game, he decides to stay home from school.
Arthur goes to the beach. Arthur was invited to go to the beach with his friends. He wanted to spend time with them and play volleyball. His friends told him that it would be a good idea to go. The day of the game, Arthur decided to go to the beach. After playing for his friends, he went home.
Arthur goes to the beach. Arthur is going to go to the beach with his family. He wants to spend time playing volleyball but he doesn't have enough money. He decides to get a job and earn money by working hard at his local mall. He begins to work as a mechanic and gets paid for it. He goes home and plays volleyball every day.
Arthur goes to the beach. Arthur was invited to go to the beach with his friends. He decided to go by himself. When he got there, everyone said that he was too hot. The weather was so cold that he had to leave. After that day, Arthur went home and watched tv instead of going.
|
wogkr810/mnm
|
wogkr810
| 2022-06-05T05:03:24Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-03T04:33:50Z
|
# Model
---
## [KLUE MRC 대회](https://github.com/boostcampaitech3/level2-mrc-level2-nlp-09)에서 사용한 Reader SOTA모델을 사용하여 파인튜닝을 진행했습니다.
- 데이터셋 : 태깅 이후 구축한 데이터셋에서 전처리 및 Augmentation 적용
- [Huggingface : MRC Reader SOTA 모델](https://huggingface.co/Nonegom/roberta_finetune_twice)
- [Github Issue : MRC Redaer SOTA 모델 설명](https://github.com/boostcampaitech3/level2-mrc-level2-nlp-09/issues/38)
|
ITESM/st_demo_2
|
ITESM
| 2022-06-05T04:38:02Z
| 2
| 0
|
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-06-05T04:37:57Z
|
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
license: apache-2.0
---
# all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## 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('sentence-transformers/all-MiniLM-L6-v2')
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
import torch.nn.functional as F
#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('sentence-transformers/all-MiniLM-L6-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
# 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
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
# Normalize embeddings
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L6-v2)
------
## Background
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a
1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developped this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developped this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
## Intended uses
Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures
the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
By default, input text longer than 256 word pieces is truncated.
## Training procedure
### Pre-training
We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
### Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
#### Hyper parameters
We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
#### Training data
We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
| Dataset | Paper | Number of training tuples |
|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 |
| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| **Total** | | **1,170,060,424** |
|
nitishkumargundapu793/autotrain-chat-bot-responses-949231426
|
nitishkumargundapu793
| 2022-06-05T03:16:21Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:nitishkumargundapu793/autotrain-data-chat-bot-responses",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-05T03:13:45Z
|
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- nitishkumargundapu793/autotrain-data-chat-bot-responses
co2_eq_emissions: 0.01123534537751425
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 949231426
- CO2 Emissions (in grams): 0.01123534537751425
## Validation Metrics
- Loss: 0.26922607421875
- Accuracy: 1.0
- Macro F1: 1.0
- Micro F1: 1.0
- Weighted F1: 1.0
- Macro Precision: 1.0
- Micro Precision: 1.0
- Weighted Precision: 1.0
- Macro Recall: 1.0
- Micro Recall: 1.0
- Weighted Recall: 1.0
## 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/nitishkumargundapu793/autotrain-chat-bot-responses-949231426
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("nitishkumargundapu793/autotrain-chat-bot-responses-949231426", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("nitishkumargundapu793/autotrain-chat-bot-responses-949231426", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
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