modelId
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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-29 18:27:06
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
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Corianas/dqn-BeamRiderNoFrameskip-v4_2
|
Corianas
| 2022-06-16T04:42:20Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"BeamRiderNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-16T04:38:44Z |
---
library_name: stable-baselines3
tags:
- BeamRiderNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 4574.80 +/- 2171.74
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BeamRiderNoFrameskip-v4
type: BeamRiderNoFrameskip-v4
---
# **DQN** Agent playing **BeamRiderNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **BeamRiderNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env BeamRiderNoFrameskip-v4 -orga Corianas -f logs/
python enjoy.py --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/ -orga Corianas
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
YYSH/Test-demo-colab
|
YYSH
| 2022-06-16T04:40:10Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-16T02:32:08Z |
---
tags:
- generated_from_trainer
model-index:
- name: Test-demo-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. -->
# Test-demo-colab
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9479
- Wer: 0.6856
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.2676 | 1.0 | 500 | 2.2725 | 1.0013 |
| 2.0086 | 2.01 | 1000 | 1.2788 | 0.8053 |
| 1.6389 | 3.01 | 1500 | 1.1333 | 0.7458 |
| 1.4908 | 4.02 | 2000 | 1.0369 | 0.7356 |
| 1.4137 | 5.02 | 2500 | 0.9894 | 0.7111 |
| 1.3507 | 6.02 | 3000 | 0.9394 | 0.7098 |
| 1.3101 | 7.03 | 3500 | 0.9531 | 0.6966 |
| 1.2682 | 8.03 | 4000 | 0.9255 | 0.6892 |
| 1.239 | 9.04 | 4500 | 0.9222 | 0.6818 |
| 1.2161 | 10.04 | 5000 | 0.9079 | 0.6911 |
| 1.1871 | 11.04 | 5500 | 0.9100 | 0.7033 |
| 1.1688 | 12.05 | 6000 | 0.9080 | 0.6924 |
| 1.1383 | 13.05 | 6500 | 0.9097 | 0.6910 |
| 1.1304 | 14.06 | 7000 | 0.9052 | 0.6810 |
| 1.1181 | 15.06 | 7500 | 0.9025 | 0.6847 |
| 1.0905 | 16.06 | 8000 | 0.9296 | 0.6832 |
| 1.0744 | 17.07 | 8500 | 0.9120 | 0.6912 |
| 1.0675 | 18.07 | 9000 | 0.9039 | 0.6864 |
| 1.0511 | 19.08 | 9500 | 0.9157 | 0.7004 |
| 1.0401 | 20.08 | 10000 | 0.9259 | 0.6792 |
| 1.0319 | 21.08 | 10500 | 0.9478 | 0.6976 |
| 1.0194 | 22.09 | 11000 | 0.9438 | 0.6820 |
| 1.0117 | 23.09 | 11500 | 0.9577 | 0.6891 |
| 1.0038 | 24.1 | 12000 | 0.9670 | 0.6918 |
| 0.9882 | 25.1 | 12500 | 0.9579 | 0.6884 |
| 0.9979 | 26.1 | 13000 | 0.9502 | 0.6869 |
| 0.9767 | 27.11 | 13500 | 0.9537 | 0.6833 |
| 0.964 | 28.11 | 14000 | 0.9525 | 0.6880 |
| 0.9867 | 29.12 | 14500 | 0.9479 | 0.6856 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
huggingtweets/fushidahardy
|
huggingtweets
| 2022-06-16T03:42:42Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-16T03:38:36Z |
---
language: en
thumbnail: http://www.huggingtweets.com/fushidahardy/1655350909485/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/1271291765719351297/_NdPd0cg_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">Shintaro Fushida-Hardy 🦎</div>
<div style="text-align: center; font-size: 14px;">@fushidahardy</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 Shintaro Fushida-Hardy 🦎.
| Data | Shintaro Fushida-Hardy 🦎 |
| --- | --- |
| Tweets downloaded | 1728 |
| Retweets | 253 |
| Short tweets | 115 |
| Tweets kept | 1360 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3pk5r7pt/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 @fushidahardy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1dxchh1a) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1dxchh1a/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/fushidahardy')
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/acai28
|
huggingtweets
| 2022-06-16T03:39:49Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-16T03:32:47Z |
---
language: en
thumbnail: http://www.huggingtweets.com/acai28/1655350773093/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/1527251112604184576/3dKVjGwK_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">alec</div>
<div style="text-align: center; font-size: 14px;">@acai28</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 alec.
| Data | alec |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 165 |
| Short tweets | 488 |
| Tweets kept | 2592 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/rd31m5h3/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 @acai28's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/w8y3ix5h) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/w8y3ix5h/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/acai28')
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)
|
Willy/bert-base-spanish-wwm-cased-finetuned-NLP-IE
|
Willy
| 2022-06-15T23:52:05Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-15T23:25:26Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-spanish-wwm-cased-finetuned-NLP-IE
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-spanish-wwm-cased-finetuned-NLP-IE
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6260
- Accuracy: 0.7015
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6052 | 1.0 | 9 | 0.6370 | 0.7015 |
| 0.5501 | 2.0 | 18 | 0.6260 | 0.7015 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Willy/bert-base-spanish-wwm-cased-finetuned-emotion
|
Willy
| 2022-06-15T23:22:27Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-15T22:32:09Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-spanish-wwm-cased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-spanish-wwm-cased-finetuned-emotion
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5558
- Accuracy: 0.7630
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5414 | 1.0 | 67 | 0.5677 | 0.7481 |
| 0.5482 | 2.0 | 134 | 0.5558 | 0.7630 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
huggingtweets/43folders-hotdogsladies
|
huggingtweets
| 2022-06-15T23:14:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-15T23:10:07Z |
---
language: en
thumbnail: http://www.huggingtweets.com/43folders-hotdogsladies/1655334875186/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/1165801400/43f-logo-square-300_400x400.png')">
</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/1474526156430798849/0Z_zfYqH_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>
<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">43 Folders & Merlin Mann</div>
<div style="text-align: center; font-size: 14px;">@43folders-hotdogsladies</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 43 Folders & Merlin Mann.
| Data | 43 Folders | Merlin Mann |
| --- | --- | --- |
| Tweets downloaded | 149 | 317 |
| Retweets | 8 | 41 |
| Short tweets | 0 | 48 |
| Tweets kept | 141 | 228 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2gd31yq9/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 @43folders-hotdogsladies's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/148w4fxc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/148w4fxc/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/43folders-hotdogsladies')
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)
|
fourthbrain-demo/finetuning-sentiment-model-3000-samples
|
fourthbrain-demo
| 2022-06-15T22:51:47Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-15T22:18:52Z |
---
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: 0.3023
- Accuracy: 0.8767
- F1: 0.8771
## 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 4.19.4
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
justpyschitry/autotrain-Psychiatry_Article_Identifier-990132822
|
justpyschitry
| 2022-06-15T21:42:20Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:justpyschitry/autotrain-data-Psychiatry_Article_Identifier",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-15T21:36:07Z |
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- justpyschitry/autotrain-data-Psychiatry_Article_Identifier
co2_eq_emissions: 13.4308931494349
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 990132822
- CO2 Emissions (in grams): 13.4308931494349
## Validation Metrics
- Loss: 0.3777158558368683
- Accuracy: 0.9177471636952999
- Macro F1: 0.9082952086962773
- Micro F1: 0.9177471636952999
- Weighted F1: 0.9175376430905807
- Macro Precision: 0.9175123149319843
- Micro Precision: 0.9177471636952999
- Weighted Precision: 0.9185452324503698
- Macro Recall: 0.9042000199743617
- Micro Recall: 0.9177471636952999
- Weighted Recall: 0.9177471636952999
## 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/justpyschitry/autotrain-Psychiatry_Article_Identifier-990132822
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("justpyschitry/autotrain-Psychiatry_Article_Identifier-990132822", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("justpyschitry/autotrain-Psychiatry_Article_Identifier-990132822", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
huggingtweets/yemeen
|
huggingtweets
| 2022-06-15T21:27:04Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-15T21:22:42Z |
---
language: en
thumbnail: http://www.huggingtweets.com/yemeen/1655328324400/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/1438226079030947845/pwH4SUlU_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">𝕐𝕖𝕞𝕖𝕖𝕟</div>
<div style="text-align: center; font-size: 14px;">@yemeen</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 𝕐𝕖𝕞𝕖𝕖𝕟.
| Data | 𝕐𝕖𝕞𝕖𝕖𝕟 |
| --- | --- |
| Tweets downloaded | 2911 |
| Retweets | 1038 |
| Short tweets | 198 |
| Tweets kept | 1675 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3it77r2s/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 @yemeen's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/39fvs51l) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/39fvs51l/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/yemeen')
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)
|
emilys/BERTweet-CoNLL
|
emilys
| 2022-06-15T21:19:05Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"NER",
"en",
"dataset:conll2003",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-14T22:41:31Z |
---
language:
- en
tags:
- NER
datasets:
- conll2003
---
bertweet-base (https://huggingface.co/vinai/bertweet-base) finetuned on CoNLL (2003) English, following https://github.com/huggingface/transformers/tree/main/examples/legacy/token-classification
|
jianyang/dqn-SpaceInvadersNoFrameskip-v4
|
jianyang
| 2022-06-15T20:31:27Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-15T20:30:43Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 699.00 +/- 184.58
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jianyang -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga jianyang
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
kcarnold/inquisitive2
|
kcarnold
| 2022-06-15T19:55:47Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-15T18:28:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: inquisitive2
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. -->
# inquisitive2
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1760
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7.0
### Training results
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0
- Datasets 2.3.0
- Tokenizers 0.12.1
|
ouiame/bert2gpt2Summy
|
ouiame
| 2022-06-15T19:31:08Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"fr",
"dataset:ouiame/autotrain-data-trainproject",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-15T13:08:46Z |
---
tags: autotrain
language: fr
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ouiame/autotrain-data-trainproject
co2_eq_emissions: 894.9753853627794
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 985232782
- CO2 Emissions (in grams): 894.9753853627794
## Validation Metrics
- Loss: 1.9692628383636475
- Rouge1: 19.3642
- Rouge2: 7.3644
- RougeL: 16.148
- RougeLsum: 16.4988
- Gen Len: 18.9975
## 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/ouiame/autotrain-trainproject-985232782
```
|
Ambiwlans/dqn-SpaceInvadersNoFrameskip-v4
|
Ambiwlans
| 2022-06-15T18:24:24Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-15T18:23:45Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 594.50 +/- 167.46
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Ambiwlans -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Ambiwlans
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Vkt/model-960hfacebook-2022.06.08
|
Vkt
| 2022-06-15T18:17:56Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-08T16:16:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: model-960hfacebook-2022.06.08
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. -->
# model-960hfacebook-2022.06.08
This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2907
- Wer: 0.1804
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 6.7634 | 0.21 | 300 | 2.9743 | 0.9998 |
| 1.6536 | 0.43 | 600 | 0.8605 | 0.7529 |
| 0.9823 | 0.64 | 900 | 0.6600 | 0.6286 |
| 0.8708 | 0.86 | 1200 | 0.5780 | 0.5736 |
| 0.7878 | 1.07 | 1500 | 0.5386 | 0.5326 |
| 0.7033 | 1.29 | 1800 | 0.4986 | 0.4992 |
| 0.681 | 1.5 | 2100 | 0.4575 | 0.4778 |
| 0.6537 | 1.72 | 2400 | 0.4591 | 0.4482 |
| 0.6263 | 1.93 | 2700 | 0.4317 | 0.4353 |
| 0.5811 | 2.14 | 3000 | 0.4149 | 0.4159 |
| 0.5565 | 2.36 | 3300 | 0.4170 | 0.3956 |
| 0.5501 | 2.57 | 3600 | 0.4007 | 0.3929 |
| 0.5444 | 2.79 | 3900 | 0.3930 | 0.3851 |
| 0.5177 | 3.0 | 4200 | 0.4006 | 0.3630 |
| 0.4682 | 3.22 | 4500 | 0.3707 | 0.3713 |
| 0.4805 | 3.43 | 4800 | 0.3564 | 0.3583 |
| 0.4715 | 3.65 | 5100 | 0.3596 | 0.3434 |
| 0.4482 | 3.86 | 5400 | 0.3555 | 0.3394 |
| 0.4407 | 4.07 | 5700 | 0.3680 | 0.3312 |
| 0.4134 | 4.29 | 6000 | 0.3534 | 0.3328 |
| 0.4165 | 4.5 | 6300 | 0.3294 | 0.3259 |
| 0.4196 | 4.72 | 6600 | 0.3353 | 0.3214 |
| 0.4117 | 4.93 | 6900 | 0.3266 | 0.3211 |
| 0.3847 | 5.15 | 7200 | 0.3365 | 0.3156 |
| 0.3687 | 5.36 | 7500 | 0.3233 | 0.3014 |
| 0.376 | 5.58 | 7800 | 0.3345 | 0.2979 |
| 0.3732 | 5.79 | 8100 | 0.3105 | 0.2882 |
| 0.3705 | 6.0 | 8400 | 0.3252 | 0.2935 |
| 0.3311 | 6.22 | 8700 | 0.3266 | 0.2911 |
| 0.3386 | 6.43 | 9000 | 0.2975 | 0.2765 |
| 0.337 | 6.65 | 9300 | 0.3070 | 0.2826 |
| 0.3458 | 6.86 | 9600 | 0.3090 | 0.2766 |
| 0.3218 | 7.08 | 9900 | 0.3117 | 0.2748 |
| 0.3041 | 7.29 | 10200 | 0.2989 | 0.2651 |
| 0.3031 | 7.51 | 10500 | 0.3210 | 0.2672 |
| 0.3037 | 7.72 | 10800 | 0.3040 | 0.2667 |
| 0.3126 | 7.93 | 11100 | 0.2867 | 0.2613 |
| 0.3005 | 8.15 | 11400 | 0.3075 | 0.2610 |
| 0.2802 | 8.36 | 11700 | 0.3129 | 0.2608 |
| 0.2785 | 8.58 | 12000 | 0.3002 | 0.2579 |
| 0.2788 | 8.79 | 12300 | 0.3063 | 0.2476 |
| 0.286 | 9.01 | 12600 | 0.2971 | 0.2495 |
| 0.2534 | 9.22 | 12900 | 0.2766 | 0.2452 |
| 0.2542 | 9.44 | 13200 | 0.2893 | 0.2405 |
| 0.2576 | 9.65 | 13500 | 0.3038 | 0.2518 |
| 0.2552 | 9.86 | 13800 | 0.2851 | 0.2429 |
| 0.2487 | 10.08 | 14100 | 0.2858 | 0.2356 |
| 0.2441 | 10.29 | 14400 | 0.2999 | 0.2364 |
| 0.2345 | 10.51 | 14700 | 0.2907 | 0.2373 |
| 0.2352 | 10.72 | 15000 | 0.2885 | 0.2402 |
| 0.2464 | 10.94 | 15300 | 0.2896 | 0.2339 |
| 0.2219 | 11.15 | 15600 | 0.2999 | 0.2351 |
| 0.2257 | 11.37 | 15900 | 0.2930 | 0.2326 |
| 0.2184 | 11.58 | 16200 | 0.2980 | 0.2353 |
| 0.2182 | 11.79 | 16500 | 0.2832 | 0.2296 |
| 0.2224 | 12.01 | 16800 | 0.2797 | 0.2285 |
| 0.1991 | 12.22 | 17100 | 0.2810 | 0.2296 |
| 0.1993 | 12.44 | 17400 | 0.2949 | 0.2253 |
| 0.2042 | 12.65 | 17700 | 0.2864 | 0.2207 |
| 0.2083 | 12.87 | 18000 | 0.2860 | 0.2278 |
| 0.1998 | 13.08 | 18300 | 0.2872 | 0.2232 |
| 0.1919 | 13.3 | 18600 | 0.2894 | 0.2247 |
| 0.1925 | 13.51 | 18900 | 0.3007 | 0.2234 |
| 0.1966 | 13.72 | 19200 | 0.2831 | 0.2176 |
| 0.1942 | 13.94 | 19500 | 0.2811 | 0.2161 |
| 0.1778 | 14.15 | 19800 | 0.2901 | 0.2196 |
| 0.1755 | 14.37 | 20100 | 0.2864 | 0.2188 |
| 0.1795 | 14.58 | 20400 | 0.2927 | 0.2170 |
| 0.1817 | 14.8 | 20700 | 0.2846 | 0.2156 |
| 0.1754 | 15.01 | 21000 | 0.3036 | 0.2137 |
| 0.1674 | 15.23 | 21300 | 0.2876 | 0.2156 |
| 0.171 | 15.44 | 21600 | 0.2812 | 0.2106 |
| 0.1603 | 15.65 | 21900 | 0.2692 | 0.2093 |
| 0.1663 | 15.87 | 22200 | 0.2745 | 0.2094 |
| 0.1608 | 16.08 | 22500 | 0.2807 | 0.2043 |
| 0.1555 | 16.3 | 22800 | 0.2872 | 0.2036 |
| 0.1546 | 16.51 | 23100 | 0.2837 | 0.2049 |
| 0.1515 | 16.73 | 23400 | 0.2746 | 0.2031 |
| 0.1571 | 16.94 | 23700 | 0.2767 | 0.2047 |
| 0.1498 | 17.16 | 24000 | 0.2837 | 0.2050 |
| 0.143 | 17.37 | 24300 | 0.2745 | 0.2038 |
| 0.1471 | 17.58 | 24600 | 0.2787 | 0.2004 |
| 0.1442 | 17.8 | 24900 | 0.2779 | 0.2005 |
| 0.1481 | 18.01 | 25200 | 0.2906 | 0.2021 |
| 0.1318 | 18.23 | 25500 | 0.2936 | 0.1991 |
| 0.1396 | 18.44 | 25800 | 0.2913 | 0.1984 |
| 0.144 | 18.66 | 26100 | 0.2806 | 0.1953 |
| 0.1341 | 18.87 | 26400 | 0.2896 | 0.1972 |
| 0.1375 | 19.09 | 26700 | 0.2937 | 0.2002 |
| 0.1286 | 19.3 | 27000 | 0.2929 | 0.1954 |
| 0.1242 | 19.51 | 27300 | 0.2968 | 0.1962 |
| 0.1305 | 19.73 | 27600 | 0.2879 | 0.1944 |
| 0.1287 | 19.94 | 27900 | 0.2850 | 0.1937 |
| 0.1286 | 20.16 | 28200 | 0.2910 | 0.1961 |
| 0.121 | 20.37 | 28500 | 0.2908 | 0.1912 |
| 0.1264 | 20.59 | 28800 | 0.2853 | 0.1904 |
| 0.1238 | 20.8 | 29100 | 0.2913 | 0.1926 |
| 0.117 | 21.02 | 29400 | 0.2907 | 0.1922 |
| 0.1154 | 21.23 | 29700 | 0.2902 | 0.1888 |
| 0.1142 | 21.44 | 30000 | 0.2854 | 0.1907 |
| 0.1168 | 21.66 | 30300 | 0.2918 | 0.1873 |
| 0.1168 | 21.87 | 30600 | 0.2897 | 0.1873 |
| 0.1105 | 22.09 | 30900 | 0.2951 | 0.1856 |
| 0.1134 | 22.3 | 31200 | 0.2842 | 0.1847 |
| 0.1111 | 22.52 | 31500 | 0.2884 | 0.1829 |
| 0.1088 | 22.73 | 31800 | 0.2991 | 0.1840 |
| 0.1139 | 22.94 | 32100 | 0.2876 | 0.1839 |
| 0.1078 | 23.16 | 32400 | 0.2899 | 0.1830 |
| 0.1087 | 23.37 | 32700 | 0.2927 | 0.1803 |
| 0.1076 | 23.59 | 33000 | 0.2924 | 0.1801 |
| 0.11 | 23.8 | 33300 | 0.2877 | 0.1804 |
| 0.1067 | 24.02 | 33600 | 0.2918 | 0.1799 |
| 0.1104 | 24.23 | 33900 | 0.2908 | 0.1809 |
| 0.1023 | 24.45 | 34200 | 0.2939 | 0.1807 |
| 0.0993 | 24.66 | 34500 | 0.2925 | 0.1802 |
| 0.1053 | 24.87 | 34800 | 0.2907 | 0.1804 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.8.1+cu111
- Datasets 2.2.1
- Tokenizers 0.12.1
|
huggingtweets/_mohamads
|
huggingtweets
| 2022-06-15T17:37:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-15T17:33:04Z |
---
language: en
thumbnail: http://www.huggingtweets.com/_mohamads/1655314541919/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/1522920330960027648/Z5piAxnG_400x400.png')">
</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">🧬 محمد الزهراني</div>
<div style="text-align: center; font-size: 14px;">@_mohamads</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 🧬 محمد الزهراني.
| Data | 🧬 محمد الزهراني |
| --- | --- |
| Tweets downloaded | 1108 |
| Retweets | 75 |
| Short tweets | 90 |
| Tweets kept | 943 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/y8wg10zm/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 @_mohamads's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1jm1spua) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1jm1spua/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/_mohamads')
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)
|
SimulSt/xlm-roberta-base-finetuned-panx-de
|
SimulSt
| 2022-06-15T16:59:24Z | 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
| 2022-06-15T16:20:48Z |
---
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
|
joaogante/test_text
|
joaogante
| 2022-06-15T16:53:59Z | 44 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"distilbert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-31T16:02:39Z |
---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# DistilBERT base model (uncased)
This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-uncased). It was
introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found
[here](https://github.com/huggingface/transformers/tree/master/examples/distillation). This model is uncased: it does
not make a difference between english and English.
## Model description
DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only,
with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic
process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained
with three objectives:
- Distillation loss: the model was trained to return the same probabilities as the BERT base model.
- Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a
sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the
model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that
usually see the words one after the other, or from autoregressive models like GPT which internally mask the future
tokens. It allows the model to learn a bidirectional representation of the sentence.
- Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base
model.
This way, the model learns the same inner representation of the English language than its teacher model, while being
faster for inference or downstream tasks.
## Intended uses & limitations
You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=distilbert) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased')
>>> unmasker("Hello I'm a [MASK] model.")
[{'sequence': "[CLS] hello i'm a role model. [SEP]",
'score': 0.05292855575680733,
'token': 2535,
'token_str': 'role'},
{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
'score': 0.03968575969338417,
'token': 4827,
'token_str': 'fashion'},
{'sequence': "[CLS] hello i'm a business model. [SEP]",
'score': 0.034743521362543106,
'token': 2449,
'token_str': 'business'},
{'sequence': "[CLS] hello i'm a model model. [SEP]",
'score': 0.03462274372577667,
'token': 2944,
'token_str': 'model'},
{'sequence': "[CLS] hello i'm a modeling model. [SEP]",
'score': 0.018145186826586723,
'token': 11643,
'token_str': 'modeling'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import DistilBertTokenizer, DistilBertModel
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertModel.from_pretrained("distilbert-base-uncased")
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 DistilBertTokenizer, TFDistilBertModel
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = TFDistilBertModel.from_pretrained("distilbert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions. It also inherits some of
[the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias).
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased')
>>> unmasker("The White man worked as a [MASK].")
[{'sequence': '[CLS] the white man worked as a blacksmith. [SEP]',
'score': 0.1235365942120552,
'token': 20987,
'token_str': 'blacksmith'},
{'sequence': '[CLS] the white man worked as a carpenter. [SEP]',
'score': 0.10142576694488525,
'token': 10533,
'token_str': 'carpenter'},
{'sequence': '[CLS] the white man worked as a farmer. [SEP]',
'score': 0.04985016956925392,
'token': 7500,
'token_str': 'farmer'},
{'sequence': '[CLS] the white man worked as a miner. [SEP]',
'score': 0.03932540491223335,
'token': 18594,
'token_str': 'miner'},
{'sequence': '[CLS] the white man worked as a butcher. [SEP]',
'score': 0.03351764753460884,
'token': 14998,
'token_str': 'butcher'}]
>>> unmasker("The Black woman worked as a [MASK].")
[{'sequence': '[CLS] the black woman worked as a waitress. [SEP]',
'score': 0.13283951580524445,
'token': 13877,
'token_str': 'waitress'},
{'sequence': '[CLS] the black woman worked as a nurse. [SEP]',
'score': 0.12586183845996857,
'token': 6821,
'token_str': 'nurse'},
{'sequence': '[CLS] the black woman worked as a maid. [SEP]',
'score': 0.11708822101354599,
'token': 10850,
'token_str': 'maid'},
{'sequence': '[CLS] the black woman worked as a prostitute. [SEP]',
'score': 0.11499975621700287,
'token': 19215,
'token_str': 'prostitute'},
{'sequence': '[CLS] the black woman worked as a housekeeper. [SEP]',
'score': 0.04722772538661957,
'token': 22583,
'token_str': 'housekeeper'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset
consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia)
(excluding lists, tables and headers).
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
"sentences" has a combined length of less than 512 tokens.
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
### Pretraining
The model was trained on 8 16 GB V100 for 90 hours. See the
[training code](https://github.com/huggingface/transformers/tree/master/examples/distillation) for all hyperparameters
details.
## Evaluation results
When fine-tuned on downstream tasks, this model achieves the following results:
Glue test results:
| Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE |
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|
| | 82.2 | 88.5 | 89.2 | 91.3 | 51.3 | 85.8 | 87.5 | 59.9 |
### BibTeX entry and citation info
```bibtex
@article{Sanh2019DistilBERTAD,
title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
journal={ArXiv},
year={2019},
volume={abs/1910.01108}
}
```
<a href="https://huggingface.co/exbert/?model=distilbert-base-uncased">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
Alireza1044/mobilebert_rte
|
Alireza1044
| 2022-06-15T16:24:42Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-15T16:09:49Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: rte
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE RTE
type: glue
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.6678700361010831
---
<!-- 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. -->
# rte
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8396
- Accuracy: 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ncfrey/ChemGPT-19M
|
ncfrey
| 2022-06-15T15:19:57Z | 384 | 5 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"chemistry",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-11T20:02:27Z |
---
tags:
- chemistry
---
# ChemGPT 19M
ChemGPT is based on the GPT-Neo model and was introduced in the paper [Neural Scaling of Deep Chemical Models](https://chemrxiv.org/engage/chemrxiv/article-details/627bddd544bdd532395fb4b5).
## Model description
ChemGPT is a transformers model for generative molecular modeling, which was pretrained on the PubChem10M dataset.
## Intended uses & limitations
### How to use
You can use this model directly from the 🤗/transformers library.
### Limitations and bias
This model was trained on a subset of molecules from PubChem. You can use this model to generate molecules, but it is mostly intended to be used for investigations of the effects of pre-training and fine-tuning on downstream datasets.
## Training data
PubChem10M, a dataset of SMILES strings from PubChem, available via [DeepChem](https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/pubchem_10m.txt.zip).
## Training procedure
### Preprocessing
SMILES strings were converted to SELFIES using version 1.0.4 of the SELFIES library.
### Pretraining
See code in the [LitMatter repository](https://github.com/ncfrey/litmatter/blob/main/lit_models/lit_chemgpt.py).
### BibTeX entry and citation info
```
@article{frey_soklaski_axelrod_samsi_gomez-bombarelli_coley_gadepally_2022,
place={Cambridge}, title={Neural Scaling of Deep Chemical Models},
DOI={10.26434/chemrxiv-2022-3s512}, journal={ChemRxiv}, publisher={Cambridge Open Engage},
author={Frey, Nathan and Soklaski, Ryan and Axelrod, Simon and Samsi, Siddharth and Gomez-Bombarelli, Rafael and Coley, Connor and Gadepally, Vijay},
year={2022}} This content is a preprint and has not been peer-reviewed.
```
```
Frey, Nathan, Ryan Soklaski, Simon Axelrod, Siddharth Samsi, Rafael Gomez-Bombarelli, Connor Coley, and Vijay Gadepally.
"Neural Scaling of Deep Chemical Models." ChemRxiv (2022). Print. This content is a preprint and has not been peer-reviewed.
```
|
themindorchestra/Soundhealing
|
themindorchestra
| 2022-06-15T13:02:25Z | 0 | 0 | null |
[
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2022-06-15T13:02:25Z |
---
license: cc-by-nc-sa-4.0
---
|
jkhan447/sarcasm-detection-Bert-base-uncased-CR-POS
|
jkhan447
| 2022-06-15T12:59:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-15T04:05:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sarcasm-detection-Bert-base-uncased-CR-POS
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. -->
# sarcasm-detection-Bert-base-uncased-CR-POS
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1816
- Accuracy: 0.5783
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.0
- Tokenizers 0.12.1
|
tuni/distilbert-base-uncased-finetuned-mnli
|
tuni
| 2022-06-15T12:57:52Z | 10 | 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
| 2022-06-14T21:50:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.8204788588894549
---
<!-- 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-mnli
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: 0.6574
- Accuracy: 0.8205
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.5188 | 1.0 | 24544 | 0.4979 | 0.8047 |
| 0.4153 | 2.0 | 49088 | 0.4845 | 0.8147 |
| 0.3008 | 3.0 | 73632 | 0.5631 | 0.8204 |
| 0.2226 | 4.0 | 98176 | 0.6574 | 0.8205 |
| 0.189 | 5.0 | 122720 | 0.8209 | 0.8194 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.0
- Tokenizers 0.12.1
|
winson/distilbert-base-uncased-finetuned-imdb
|
winson
| 2022-06-15T12:51:06Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-15T10:06:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-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. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.1139
- eval_runtime: 1.8873
- eval_samples_per_second: 529.866
- eval_steps_per_second: 8.478
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.1
- Tokenizers 0.12.1
|
magelang1337/Backlinks
|
magelang1337
| 2022-06-15T11:56:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-06-15T11:56:30Z |
https://www.beesource.com/members/magelang1337.142760/#about
https://leasedadspace.com/frame.php?bfm_page=members/magelang1337&aid=magelang1337
https://www.jqwidgets.com/community/users/magelang1337/
https://metalstorm.net/users/magelang1337/profile
https://myanimelist.net/profile/mnhblog
https://forum.codeigniter.com/member.php?action=profile&uid=50438
https://purothemes.com/support/users/magelang1337/
https://talkmarkets.com/member/nutrisi25/
https://www.ngemu.com/members/magelang1337.723290/
https://lunarxtest.com/horizondrifters/community/profile/magelang1337/
https://lifedonefree.com/community/profile/magelang1337/
https://multijoueur.online/forum/profile/magelang1337/
https://www.prevailingtruth.net/community/profile/magelang1337/
https://confidentkidsborntosparkle.com/community/profile/magelng1337/
https://cyborg-guide.ru/forum/profile/magelang1337/
https://nvridersforum.com/profile/magelang1337/
http://cubeengine.com/forum.php?action=display_thread&thread_id=2745
https://forums.opensuse.org/showthread.php/564850-Full-system-crash-when-playing-games?p=3133991#post3133991
https://www.askalondoner.co.uk/community/profile/magelang1337/
|
AnyaSchen/rugpt3_esenin
|
AnyaSchen
| 2022-06-15T11:26:44Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-15T10:28:01Z |
This model was created as a fine-tuned GPT-3 medium model, which is tuned to the style of Yesenin's poetry in Russian. You can give her a word, a phrase, or just an empty line as an input, and she will give out a poem in Yesenin's style.
|
ChrisUPM/BioBERT_Re_trained
|
ChrisUPM
| 2022-06-15T11:10:39Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-18T12:54:24Z |
PyTorch trained model on GAD dataset for relation classification, using BioBert weights.
|
Corianas/dqn-BeamRiderNoFrameskip-v4
|
Corianas
| 2022-06-15T10:41:50Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"BeamRiderNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-15T08:55:40Z |
---
library_name: stable-baselines3
tags:
- BeamRiderNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 3983.00 +/- 1512.41
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BeamRiderNoFrameskip-v4
type: BeamRiderNoFrameskip-v4
---
# **DQN** Agent playing **BeamRiderNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **BeamRiderNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env BeamRiderNoFrameskip-v4 -orga Corianas -f logs/
python enjoy.py --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env BeamRiderNoFrameskip-v4 -f logs/ -orga Corianas
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
fabiochiu/dqn-SpaceInvadersNoFrameskip-v4
|
fabiochiu
| 2022-06-15T10:32:49Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-15T10:32:10Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 631.50 +/- 84.41
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga fabiochiu -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga fabiochiu
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
rajendra-ml/Chandrayaan
|
rajendra-ml
| 2022-06-15T10:16:42Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-15T10:16:00Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 176.75 +/- 18.07
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
...
```
|
roscazo/gpt2-covid
|
roscazo
| 2022-06-15T09:46:02Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-15T08:55:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: gpt2-covid
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. -->
# gpt2-covid
This model is a fine-tuned version of [PlanTL-GOB-ES/gpt2-base-bne](https://huggingface.co/PlanTL-GOB-ES/gpt2-base-bne) 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: 32
- 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: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.0
- Tokenizers 0.12.1
|
FritzOS/TEdetection_distiBERT_NER_final_8e
|
FritzOS
| 2022-06-15T09:37:10Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-15T09:36:53Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: TEdetection_distiBERT_NER_final_8e
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. -->
# TEdetection_distiBERT_NER_final_8e
This model is a fine-tuned version of [FritzOS/TEdetection_distiBERT_mLM_final_8e](https://huggingface.co/FritzOS/TEdetection_distiBERT_mLM_final_8e) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0032
- Validation Loss: 0.0037
- 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': 220743, '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
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.0032 | 0.0037 | 0 |
### Framework versions
- Transformers 4.19.4
- TensorFlow 2.8.2
- Datasets 2.3.0
- Tokenizers 0.12.1
|
RuiqianLi/Malaya-speech_fine-tune_MrBrown_15_Jun
|
RuiqianLi
| 2022-06-15T08:23:28Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:uob_singlish",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-15T04:20:17Z |
---
tags:
- generated_from_trainer
datasets:
- uob_singlish
model-index:
- name: Malaya-speech_fine-tune_MrBrown_15_Jun
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. -->
# Malaya-speech_fine-tune_MrBrown_15_Jun
This model is a fine-tuned version of [malay-huggingface/wav2vec2-xls-r-300m-mixed](https://huggingface.co/malay-huggingface/wav2vec2-xls-r-300m-mixed) on the uob_singlish dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4822
- Wer: 0.2449
## 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1607 | 5.26 | 200 | 0.3983 | 0.2381 |
| 0.5184 | 10.52 | 400 | 0.3256 | 0.2245 |
| 0.2993 | 15.78 | 600 | 0.3437 | 0.2426 |
| 0.2485 | 21.05 | 800 | 0.4547 | 0.2585 |
| 0.1917 | 26.31 | 1000 | 0.4598 | 0.2517 |
| 0.1586 | 31.57 | 1200 | 0.4050 | 0.2290 |
| 0.1486 | 36.83 | 1400 | 0.4186 | 0.2653 |
| 0.1307 | 42.1 | 1600 | 0.4284 | 0.2857 |
| 0.0895 | 47.36 | 1800 | 0.5158 | 0.2562 |
| 0.0526 | 52.62 | 2000 | 0.4525 | 0.2449 |
| 0.0553 | 57.88 | 2200 | 0.4364 | 0.2336 |
| 0.037 | 63.16 | 2400 | 0.3873 | 0.2449 |
| 0.0439 | 68.42 | 2600 | 0.3914 | 0.2404 |
| 0.0411 | 73.68 | 2800 | 0.4673 | 0.2494 |
| 0.0242 | 78.94 | 3000 | 0.4801 | 0.2426 |
| 0.0833 | 84.21 | 3200 | 0.4641 | 0.2630 |
| 0.034 | 89.47 | 3400 | 0.4607 | 0.2449 |
| 0.02 | 94.73 | 3600 | 0.4825 | 0.2449 |
| 0.0211 | 99.99 | 3800 | 0.4822 | 0.2449 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
facebook/wav2vec2-conformer-rel-pos-large-100h-ft
|
facebook
| 2022-06-15T08:17:00Z | 53 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2-conformer",
"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2010.05171",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-18T09:26:04Z |
---
language: en
datasets:
- librispeech_asr
tags:
- speech
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: apache-2.0
---
# Wav2Vec2-Conformer-Large-100h with Relative Position Embeddings
[Facebook's Wav2Vec2 Conformer (TODO-add link)]()
Wav2Vec2 Conformer with relative position embeddings, pretrained on 960h hours of Librispeech and and fine-tuned on **100 hours of Librispeech** on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Paper**: [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171)
**Authors**: Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino
The results of Wav2Vec2-Conformer can be found in Table 3 and Table 4 of the [official paper](https://arxiv.org/abs/2010.05171).
The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ConformerForCTC
from datasets import load_dataset
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large-100h-ft")
model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large-100h-ft")
# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
# tokenize
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
|
facebook/wav2vec2-conformer-rope-large-100h-ft
|
facebook
| 2022-06-15T08:16:47Z | 46 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2-conformer",
"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2010.05171",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-18T09:48:47Z |
---
language: en
datasets:
- librispeech_asr
tags:
- speech
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: apache-2.0
---
# Wav2Vec2-Conformer-Large-100h with Rotary Position Embeddings
Wav2Vec2 Conformer with rotary position embeddings, pretrained on 960h hours of Librispeech and fine-tuned on **100 hours of Librispeech** on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Paper**: [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171)
**Authors**: Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino
The results of Wav2Vec2-Conformer can be found in Table 3 and Table 4 of the [official paper](https://arxiv.org/abs/2010.05171).
The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ConformerForCTC
from datasets import load_dataset
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-conformer-rope-large-100h-ft")
model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rope-large-100h-ft")
# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
# tokenize
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
|
facebook/wav2vec2-conformer-rope-large
|
facebook
| 2022-06-15T08:12:09Z | 33 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2-conformer",
"pretraining",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2010.05171",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T09:26:53Z |
---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# Wav2Vec2-Conformer-Large with Rotary Position Embeddings
Wav2Vec2 Conformer with rotary position embeddings, pretrained on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model.
**Paper**: [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171)
**Authors**: Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino
The results of Wav2Vec2-Conformer can be found in Table 3 and Table 4 of the [official paper](https://arxiv.org/abs/2010.05171).
The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
# Usage
See [this notebook](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F?usp=sharing) for more information on how to fine-tune the model.
|
hossay/biobert-base-cased-v1.2-finetuned-ner
|
hossay
| 2022-06-15T07:38:51Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:ncbi_disease",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-15T07:19:38Z |
---
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobert-base-cased-v1.2-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ncbi_disease
type: ncbi_disease
args: ncbi_disease
metrics:
- name: Precision
type: precision
value: 0.8396334478808706
- name: Recall
type: recall
value: 0.8731387730792138
- name: F1
type: f1
value: 0.856058394160584
- name: Accuracy
type: accuracy
value: 0.9824805769647444
---
<!-- 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. -->
# biobert-base-cased-v1.2-finetuned-ner
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0706
- Precision: 0.8396
- Recall: 0.8731
- F1: 0.8561
- Accuracy: 0.9825
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 340 | 0.0691 | 0.8190 | 0.7868 | 0.8026 | 0.9777 |
| 0.101 | 2.0 | 680 | 0.0700 | 0.8334 | 0.8553 | 0.8442 | 0.9807 |
| 0.0244 | 3.0 | 1020 | 0.0706 | 0.8396 | 0.8731 | 0.8561 | 0.9825 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.0
- Tokenizers 0.12.1
|
jkhan447/sarcasm-detection-Bert-base-uncased-POS
|
jkhan447
| 2022-06-15T07:17:36Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-15T04:05:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sarcasm-detection-Bert-base-uncased-POS
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. -->
# sarcasm-detection-Bert-base-uncased-POS
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1904
- Accuracy: 0.591
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.0
- Tokenizers 0.12.1
|
eslamxm/xlmroberta-finetuned-fa
|
eslamxm
| 2022-06-15T06:53:15Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarization",
"fa",
"xlmroberta",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:pn_summary",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-06-14T22:08:51Z |
---
tags:
- summarization
- fa
- xlmroberta
- Abstractive Summarization
- generated_from_trainer
datasets:
- pn_summary
model-index:
- name: xlmroberta-finetuned-fa
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. -->
# xlmroberta-finetuned-fa
This model is a fine-tuned version of [](https://huggingface.co/) on the pn_summary dataset.
It achieves the following results on the evaluation set:
- Loss: 8.2286
- Rouge-1: 4.99
- Rouge-2: 0.0
- Rouge-l: 4.99
- Gen Len: 20.0
- Bertscore: 51.89
## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- 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: 250
- num_epochs: 5
- label_smoothing_factor: 0.1
### Training results
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.0
- Tokenizers 0.12.1
|
seomh/distilbert-base-uncased-finetuned-squad
|
seomh
| 2022-06-15T06:49:56Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-11T14:04:20Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0083
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2258 | 1.0 | 5533 | 0.0560 |
| 0.952 | 2.0 | 11066 | 0.0096 |
| 0.7492 | 3.0 | 16599 | 0.0083 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/wikisignpost
|
huggingtweets
| 2022-06-15T06:24:26Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-15T06:07:57Z |
---
language: en
thumbnail: http://www.huggingtweets.com/wikisignpost/1655274233816/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/795028567398576128/GG1GUpJ7_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">The Signpost</div>
<div style="text-align: center; font-size: 14px;">@wikisignpost</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 Signpost.
| Data | The Signpost |
| --- | --- |
| Tweets downloaded | 3216 |
| Retweets | 522 |
| Short tweets | 47 |
| Tweets kept | 2647 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/7z6btxad/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 @wikisignpost's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/27ceco72) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/27ceco72/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/wikisignpost')
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)
|
eslamxm/mt5-base-finetuned-Spanish
|
eslamxm
| 2022-06-15T05:13:08Z | 94 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"es",
"spanish",
"abstractive summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-06-14T18:45:17Z |
---
license: apache-2.0
tags:
- summarization
- mt5
- es
- spanish
- abstractive summarization
- generated_from_trainer
datasets:
- wiki_lingua
model-index:
- name: mt5-base-finetuned-Spanish
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. -->
# mt5-base-finetuned-Spanish
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1727
- Rouge-1: 28.11
- Rouge-2: 12.09
- Rouge-l: 24.62
- Gen Len: 18.73
- Bertscore: 72.25
## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- 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: 250
- num_epochs: 5
- label_smoothing_factor: 0.1
### Training results
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.0
- Tokenizers 0.12.1
|
huggingtweets/danny_macaskill-martynashton
|
huggingtweets
| 2022-06-15T04:59:30Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-15T04:58:54Z |
---
language: en
thumbnail: http://www.huggingtweets.com/danny_macaskill-martynashton/1655269165002/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/770573812991754240/gyUr23bS_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/616596420230021120/w-kK8IT6_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>
<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">Danny MacAskill & Martyn Ashton</div>
<div style="text-align: center; font-size: 14px;">@danny_macaskill-martynashton</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 Danny MacAskill & Martyn Ashton.
| Data | Danny MacAskill | Martyn Ashton |
| --- | --- | --- |
| Tweets downloaded | 2971 | 3179 |
| Retweets | 505 | 810 |
| Short tweets | 79 | 136 |
| Tweets kept | 2387 | 2233 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/31ege8zb/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 @danny_macaskill-martynashton's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/g4d86tk2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/g4d86tk2/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/danny_macaskill-martynashton')
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)
|
danielcfho/q-Taxi-v3
|
danielcfho
| 2022-06-15T04:32:17Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-15T04:32:10Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.50 +/- 2.78
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="danielcfho/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"])
```
|
huggingtweets/mysteriousgam54
|
huggingtweets
| 2022-06-15T04:06:06Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-15T04:05:58Z |
---
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/1429866660299689984/CGXAQuWf_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">themysteriousgamer</div>
<div style="text-align: center; font-size: 14px;">@mysteriousgam54</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 themysteriousgamer.
| Data | themysteriousgamer |
| --- | --- |
| Tweets downloaded | 1315 |
| Retweets | 210 |
| Short tweets | 168 |
| Tweets kept | 937 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/m4i8lg1e/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 @mysteriousgam54's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3rz0m12t) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3rz0m12t/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/mysteriousgam54')
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)
|
steven123/Teeth_C
|
steven123
| 2022-06-15T02:53:44Z | 52 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-15T02:53:33Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: Teeth_C
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.5
---
# Teeth_C
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Good Teeth
#### Missing Teeth
#### Rotten Teeth
|
steven123/Teeth_A
|
steven123
| 2022-06-15T02:42:35Z | 53 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-15T02:42:24Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: Teeth_A
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.4545454680919647
---
# Teeth_A
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Good Teeth
#### Missing Teeth
#### Rotten Teeth
|
DLochmelis33/22s-dl-sentiment-1
|
DLochmelis33
| 2022-06-15T01:07:08Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:yelp_review_full",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-15T01:01:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- yelp_review_full
metrics:
- accuracy
model-index:
- name: 22s-dl-sentiment-1
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: yelp_review_full
type: yelp_review_full
args: yelp_review_full
metrics:
- name: Accuracy
type: accuracy
value: 0.9542333333333334
---
<!-- 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. -->
# 22s-dl-sentiment-1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the yelp_review_full dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2574
- Accuracy: 0.9542
## 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
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.0
- Tokenizers 0.12.1
|
tanbwilson/q-Taxi-v3
|
tanbwilson
| 2022-06-15T01:04:48Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-15T01:04:42Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.54 +/- 2.69
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="tanbwilson/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"])
```
|
tanbwilson/q-FrozenLake-v1-4x4-noSlippery
|
tanbwilson
| 2022-06-15T01:02:56Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-15T01:02:49Z |
---
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="tanbwilson/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"])
```
|
enoriega/rule_learning_margin_1mm_spanpred
|
enoriega
| 2022-06-15T00:55:38Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"dataset:enoriega/odinsynth_dataset",
"endpoints_compatible",
"region:us"
] | null | 2022-06-11T02:59:23Z |
---
tags:
- generated_from_trainer
datasets:
- enoriega/odinsynth_dataset
model-index:
- name: rule_learning_margin_1mm_spanpred
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_margin_1mm_spanpred
This model is a fine-tuned version of [enoriega/rule_softmatching](https://huggingface.co/enoriega/rule_softmatching) on the enoriega/odinsynth_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3250
- Margin Accuracy: 0.8518
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2000
- 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 | Margin Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|
| 0.5448 | 0.16 | 20 | 0.5229 | 0.7717 |
| 0.4571 | 0.32 | 40 | 0.4292 | 0.8109 |
| 0.4296 | 0.48 | 60 | 0.4009 | 0.8193 |
| 0.4028 | 0.64 | 80 | 0.3855 | 0.8296 |
| 0.3878 | 0.8 | 100 | 0.3757 | 0.8334 |
| 0.3831 | 0.96 | 120 | 0.3643 | 0.8367 |
| 0.3591 | 1.12 | 140 | 0.3582 | 0.8393 |
| 0.3598 | 1.28 | 160 | 0.3533 | 0.8401 |
| 0.3635 | 1.44 | 180 | 0.3442 | 0.8427 |
| 0.3478 | 1.6 | 200 | 0.3406 | 0.8472 |
| 0.342 | 1.76 | 220 | 0.3352 | 0.8479 |
| 0.3327 | 1.92 | 240 | 0.3352 | 0.8486 |
| 0.3487 | 2.08 | 260 | 0.3293 | 0.8487 |
| 0.3387 | 2.24 | 280 | 0.3298 | 0.8496 |
| 0.3457 | 2.4 | 300 | 0.3279 | 0.8505 |
| 0.3483 | 2.56 | 320 | 0.3286 | 0.8510 |
| 0.3421 | 2.72 | 340 | 0.3245 | 0.8517 |
| 0.3332 | 2.88 | 360 | 0.3252 | 0.8517 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
steven123/Teeth_B
|
steven123
| 2022-06-15T00:31:50Z | 50 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-15T00:31:36Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: Teeth_B
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.6800000071525574
---
# Teeth_B
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Good Teeth
#### Missing Teeth
#### Rotten Teeth
|
Kost777/Hhh
|
Kost777
| 2022-06-14T22:30:35Z | 0 | 0 | null |
[
"license:bsd-3-clause-clear",
"region:us"
] | null | 2022-06-14T22:30:35Z |
---
license: bsd-3-clause-clear
---
|
ahmeddbahaa/xlmroberta2xlmroberta-finetuned-ar-wikilingua
|
ahmeddbahaa
| 2022-06-14T20:55:49Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarization",
"ar",
"roberta",
"xlmroberta2xlmroberta",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-06-14T08:51:35Z |
---
tags:
- summarization
- ar
- encoder-decoder
- roberta
- xlmroberta2xlmroberta
- Abstractive Summarization
- generated_from_trainer
datasets:
- wiki_lingua
model-index:
- name: xlmroberta2xlmroberta-finetuned-ar-wikilingua
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. -->
# xlmroberta2xlmroberta-finetuned-ar-wikilingua
This model is a fine-tuned version of [](https://huggingface.co/) on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7757
- Rouge-1: 11.2
- Rouge-2: 1.96
- Rouge-l: 10.28
- Gen Len: 19.8
- Bertscore: 66.27
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 10
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 8.03 | 1.0 | 312 | 7.3208 | 0.19 | 0.0 | 0.19 | 20.0 | 54.84 |
| 7.2309 | 2.0 | 624 | 7.1107 | 1.17 | 0.03 | 1.16 | 20.0 | 60.0 |
| 7.0752 | 3.0 | 936 | 7.0061 | 2.58 | 0.15 | 2.55 | 20.0 | 63.52 |
| 6.7538 | 4.0 | 1248 | 6.4189 | 5.75 | 0.46 | 5.55 | 19.95 | 62.83 |
| 6.1513 | 5.0 | 1560 | 5.8402 | 8.46 | 1.04 | 8.08 | 19.2 | 64.25 |
| 5.6639 | 6.0 | 1872 | 5.3938 | 8.62 | 1.17 | 8.16 | 19.28 | 64.81 |
| 5.2857 | 7.0 | 2184 | 5.0719 | 9.34 | 1.41 | 8.61 | 19.71 | 65.29 |
| 5.027 | 8.0 | 2496 | 4.9047 | 10.42 | 1.52 | 9.57 | 19.57 | 65.75 |
| 4.8747 | 9.0 | 2808 | 4.8032 | 10.79 | 1.71 | 9.91 | 19.42 | 66.2 |
| 4.7855 | 10.0 | 3120 | 4.7757 | 11.01 | 1.73 | 10.04 | 19.55 | 66.24 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
AAkhilesh/wav2vec2-large-xls-r-300m-ta-colab
|
AAkhilesh
| 2022-06-14T20:39:54Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-02T14:12:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-ta-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-ta-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
tanbwilson/ppo-LunarLander-v2
|
tanbwilson
| 2022-06-14T20:31:40Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-14T20:31:12Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 270.14 +/- 22.06
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
...
```
|
nateraw/koala-panda-wombat
|
nateraw
| 2022-06-14T20:31:04Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-14T20:30:51Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: koala-panda-wombat
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9850746393203735
---
# koala-panda-wombat
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### koala
#### panda
#### wombat
|
eslamxm/mt5-base-arabic
|
eslamxm
| 2022-06-14T18:08:07Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"arabic",
"ar",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:xlsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-05-09T06:32:04Z |
---
license: apache-2.0
tags:
- summarization
- arabic
- ar
- mt5
- Abstractive Summarization
- generated_from_trainer
datasets:
- xlsum
model-index:
- name: mt5-base-arabic
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. -->
# mt5-base-arabic
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on arabic subset on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2742
- Rouge-1: 22.86
- Rouge-2: 10.31
- Rouge-l: 20.85
- Gen Len: 19.0
- Bertscore: 71.52
## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 4.2331 | 1.0 | 1172 | 3.5051 | 18.54 | 6.63 | 16.77 | 19.0 | 70.28 |
| 3.7075 | 2.0 | 2344 | 3.3737 | 19.99 | 7.94 | 18.19 | 19.0 | 70.79 |
| 3.5132 | 3.0 | 3516 | 3.3171 | 20.76 | 8.57 | 18.96 | 19.0 | 70.95 |
| 3.3859 | 4.0 | 4688 | 3.2811 | 21.49 | 8.99 | 19.51 | 19.0 | 71.19 |
| 3.3012 | 5.0 | 5860 | 3.2742 | 21.79 | 9.18 | 19.77 | 19.0 | 71.25 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Wi/arxiv-distilbert-base-cased
|
Wi
| 2022-06-14T17:37:18Z | 200 | 3 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:arxiv_dataset",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-14T17:28:31Z |
---
license: apache-2.0
language:
- en
datasets:
- arxiv_dataset
tags:
- distilbert
---
# DistilBERT ArXiv Category Classification
DistilBERT model fine-tuned on a small subset of the [ArXiv dataset](https://www.kaggle.com/datasets/Cornell-University/arxiv) to predict the category of a given paper.
|
jkhan447/sarcasm-detection-RoBerta-base-CR-POS
|
jkhan447
| 2022-06-14T16:55:38Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-14T08:00:20Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sarcasm-detection-RoBerta-base-CR-POS
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. -->
# sarcasm-detection-RoBerta-base-CR-POS
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: 0.6933
- Accuracy: 0.4977
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Priya9/wav2vec2-large-xls-r-300m-tamil-colab
|
Priya9
| 2022-06-14T16:51:10Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-14T13:01:52Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-tamil-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-tamil-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5869
- Wer: 0.7266
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.2913 | 3.39 | 400 | 1.0961 | 0.9474 |
| 0.5857 | 6.78 | 800 | 0.5869 | 0.7266 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
danieladejumo/darknet-coco-object_detection
|
danieladejumo
| 2022-06-14T16:40:32Z | 0 | 2 | null |
[
"object-detection",
"COCO",
"YOLO",
"Darknet",
"model-index",
"region:us"
] |
object-detection
| 2022-06-13T14:43:12Z |
---
tags:
- object-detection
- COCO
- YOLO
- Darknet
model-index:
- name: darknet-coco-object_detection
results:
- metrics:
- type: None
value: '1'
name: None
task:
type: object-detection
name: object-detection
dataset:
name: COCO
type: COCO
---
## Darknet Object Detection on the COCO dataset
This model uses a pretrained YOLO Darknet model to perform object detection on an input image. The model is able to identify 80 classes from the COCO dataset. The classes are listed here `config/coco.names`.
### Usage
Clone the repository using
```python
repo = Repository("/local_repo_name", clone_from="danieladejumo/darknet-coco-object_detection")
```
Run a detection by using the function `detect(path_to_image)` in the notebook `darknet-coco-object_detection.ipynb`. The output image with the detection rectangle and classes will be saved to `images/image_file_name-det.jpg`
|
chiranthans23/xlm-roberta-base-finetuned-panx-de
|
chiranthans23
| 2022-06-14T16:13:11Z | 5 | 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
| 2022-06-13T16:40:42Z |
---
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 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ahmeddbahaa/xlmroberta2xlmroberta-finetune-summarization-ar
|
ahmeddbahaa
| 2022-06-14T16:05:58Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarization",
"ar",
"xlm-roberta",
"Abstractive Summarization",
"roberta",
"generated_from_trainer",
"dataset:xlsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-06-14T03:10:26Z |
---
tags:
- summarization
- ar
- encoder-decoder
- xlm-roberta
- Abstractive Summarization
- roberta
- generated_from_trainer
datasets:
- xlsum
model-index:
- name: xlmroberta2xlmroberta-finetune-summarization-ar
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. -->
# xlmroberta2xlmroberta-finetune-summarization-ar
This model is a fine-tuned version of [](https://huggingface.co/) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1298
- Rouge-1: 21.69
- Rouge-2: 8.73
- Rouge-l: 19.52
- Gen Len: 19.96
- Bertscore: 71.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- 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: 250
- num_epochs: 10
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 8.0645 | 1.0 | 1172 | 7.3567 | 8.22 | 0.66 | 7.94 | 20.0 | 58.18 |
| 7.2042 | 2.0 | 2344 | 6.6058 | 12.12 | 2.19 | 11.4 | 20.0 | 63.24 |
| 6.4168 | 3.0 | 3516 | 5.8784 | 16.46 | 4.31 | 15.15 | 20.0 | 66.3 |
| 5.4622 | 4.0 | 4688 | 4.7931 | 17.6 | 5.87 | 15.9 | 19.99 | 69.21 |
| 4.7829 | 5.0 | 5860 | 4.4418 | 19.17 | 6.74 | 17.22 | 19.98 | 70.23 |
| 4.4829 | 6.0 | 7032 | 4.2950 | 19.8 | 7.11 | 17.74 | 19.98 | 70.38 |
| 4.304 | 7.0 | 8204 | 4.2155 | 20.71 | 7.59 | 18.56 | 19.98 | 70.66 |
| 4.1778 | 8.0 | 9376 | 4.1632 | 21.1 | 7.94 | 18.99 | 19.98 | 70.86 |
| 4.0886 | 9.0 | 10548 | 4.1346 | 21.44 | 8.03 | 19.28 | 19.98 | 70.93 |
| 4.0294 | 10.0 | 11720 | 4.1298 | 21.51 | 8.14 | 19.33 | 19.98 | 71.02 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
olivia371/finetuning-sentiment-model-3000-samples
|
olivia371
| 2022-06-14T15:05:10Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-14T11:52:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.925
- name: F1
type: f1
value: 0.9253731343283581
---
<!-- 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 the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2348
- Accuracy: 0.925
- F1: 0.9254
## 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 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Alireza1044/mobilebert_qqp
|
Alireza1044
| 2022-06-14T14:57:04Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-14T12:25:57Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: qqp
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QQP
type: glue
args: qqp
metrics:
- name: Accuracy
type: accuracy
value: 0.8988869651249073
- name: F1
type: f1
value: 0.8670050100852366
---
<!-- 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. -->
# qqp
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2458
- Accuracy: 0.8989
- F1: 0.8670
- Combined Score: 0.8829
## 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: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.5
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
joitandr/q-FrozenLake-v1-4x4-nonslippery
|
joitandr
| 2022-06-14T12:54:44Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-14T12:54:37Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-nonslippery
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="joitandr/q-FrozenLake-v1-4x4-nonslippery", 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"])
```
|
huggingtweets/imrankhanpti
|
huggingtweets
| 2022-06-14T12:28:35Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-14T12:28:28Z |
---
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/1526278959746392069/t3sMBz94_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">Imran Khan</div>
<div style="text-align: center; font-size: 14px;">@imrankhanpti</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 Imran Khan.
| Data | Imran Khan |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 28 |
| Short tweets | 8 |
| Tweets kept | 3214 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2s8u3tpn/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 @imrankhanpti's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/g9j8i8kg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/g9j8i8kg/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/imrankhanpti')
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/duckybhai
|
huggingtweets
| 2022-06-14T11:44:57Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-14T11:43:59Z |
---
language: en
thumbnail: http://www.huggingtweets.com/duckybhai/1655207092084/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/1494814887410909195/1_cZ1OGN_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">Saad Ur Rehman</div>
<div style="text-align: center; font-size: 14px;">@duckybhai</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 Saad Ur Rehman.
| Data | Saad Ur Rehman |
| --- | --- |
| Tweets downloaded | 2045 |
| Retweets | 158 |
| Short tweets | 233 |
| Tweets kept | 1654 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/e0w83ypv/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 @duckybhai's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2tc4ee4o) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2tc4ee4o/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/duckybhai')
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)
|
Alireza1044/mobilebert_mnli
|
Alireza1044
| 2022-06-14T11:22:34Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-14T09:30:21Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.8230268510984541
---
<!-- 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. -->
# mnli
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4595
- Accuracy: 0.8230
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 48
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.3
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Rekcul/q-FrozenLake-v1-4x4-noSlippery
|
Rekcul
| 2022-06-14T10:16:41Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-14T09:53:13Z |
---
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="Rekcul/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"])
```
|
zdreiosis/ff_analysis_4
|
zdreiosis
| 2022-06-14T09:44:05Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"gen_ffa",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-14T05:02:34Z |
---
license: apache-2.0
tags:
- gen_ffa
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: ff_analysis_4
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. -->
# ff_analysis_4
This model is a fine-tuned version of [zdreiosis/ff_analysis_4](https://huggingface.co/zdreiosis/ff_analysis_4) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0022
- F1: 1.0
- Roc Auc: 1.0
- Accuracy: 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: 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---:|:-------:|:--------:|
| No log | 1.47 | 50 | 0.0055 | 1.0 | 1.0 | 1.0 |
| No log | 2.94 | 100 | 0.0052 | 1.0 | 1.0 | 1.0 |
| No log | 4.41 | 150 | 0.0044 | 1.0 | 1.0 | 1.0 |
| No log | 5.88 | 200 | 0.0037 | 1.0 | 1.0 | 1.0 |
| No log | 7.35 | 250 | 0.0030 | 1.0 | 1.0 | 1.0 |
| No log | 8.82 | 300 | 0.0030 | 1.0 | 1.0 | 1.0 |
| No log | 10.29 | 350 | 0.0028 | 1.0 | 1.0 | 1.0 |
| No log | 11.76 | 400 | 0.0027 | 1.0 | 1.0 | 1.0 |
| No log | 13.24 | 450 | 0.0025 | 1.0 | 1.0 | 1.0 |
| 0.0078 | 14.71 | 500 | 0.0022 | 1.0 | 1.0 | 1.0 |
| 0.0078 | 16.18 | 550 | 0.0025 | 1.0 | 1.0 | 1.0 |
| 0.0078 | 17.65 | 600 | 0.0023 | 1.0 | 1.0 | 1.0 |
| 0.0078 | 19.12 | 650 | 0.0022 | 1.0 | 1.0 | 1.0 |
| 0.0078 | 20.59 | 700 | 0.0022 | 1.0 | 1.0 | 1.0 |
| 0.0078 | 22.06 | 750 | 0.0021 | 1.0 | 1.0 | 1.0 |
| 0.0078 | 23.53 | 800 | 0.0020 | 1.0 | 1.0 | 1.0 |
| 0.0078 | 25.0 | 850 | 0.0020 | 1.0 | 1.0 | 1.0 |
| 0.0078 | 26.47 | 900 | 0.0019 | 1.0 | 1.0 | 1.0 |
| 0.0078 | 27.94 | 950 | 0.0019 | 1.0 | 1.0 | 1.0 |
| 0.0025 | 29.41 | 1000 | 0.0019 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.10.3
|
FritzOS/TEdetection_distilBERT_NER_final
|
FritzOS
| 2022-06-14T08:19:04Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-14T08:18:51Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: TEdetection_distiBERT_NER_final
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. -->
# TEdetection_distiBERT_NER_final
This model is a fine-tuned version of [FritzOS/TEdetection_distiBERT_mLM_final](https://huggingface.co/FritzOS/TEdetection_distiBERT_mLM_final) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0031
- Validation Loss: 0.0035
- 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': 220743, '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
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.0031 | 0.0035 | 0 |
### Framework versions
- Transformers 4.19.4
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Alireza1044/mobilebert_mrpc
|
Alireza1044
| 2022-06-14T08:16:32Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-14T08:06:49Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8382352941176471
- name: F1
type: f1
value: 0.8888888888888888
---
<!-- 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. -->
# mrpc
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3672
- Accuracy: 0.8382
- F1: 0.8889
- Combined Score: 0.8636
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
shaojie/distilbert-base-uncased-finetuned-squad
|
shaojie
| 2022-06-14T07:26:48Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-13T07:35:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.1585
- eval_runtime: 138.1018
- eval_samples_per_second: 78.087
- eval_steps_per_second: 4.88
- epoch: 1.0
- step: 5533
## 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
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
FritzOS/TEdetection_distilBERT_mLM_final
|
FritzOS
| 2022-06-14T07:08:24Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-14T07:08:10Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: TEdetection_distiBERT_mLM_final
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. -->
# TEdetection_distiBERT_mLM_final
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:
## 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': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 208159, '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 4.19.4
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
crodri/roberta-base-ca-v2-qa-catalanqa
|
crodri
| 2022-06-14T06:11:33Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"license:cc0-1.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-13T15:05:05Z |
---
license: cc0-1.0
---
The roberta-base-ca-cased-qa is a Question Answering (QA) model for the Catalan language fine-tuned from the BERTa model, a RoBERTa base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the BERTa model card for more details).
Datasets
We used the Catalan QA datasets called ViquiQuAD, VilaQuad and XQuad\_ca with test, training and evaluation (90-10-10) splits, balanced by type of questions.
Test: 2255
Evaluation: 2276
Train: 18082
|
huggingtweets/horse_js
|
huggingtweets
| 2022-06-14T05:59:52Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-14T05:59:06Z |
---
language: en
thumbnail: http://www.huggingtweets.com/horse_js/1655186387828/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/1844491454/horse-js_400x400.png')">
</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">Horse JS</div>
<div style="text-align: center; font-size: 14px;">@horse_js</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 Horse JS.
| Data | Horse JS |
| --- | --- |
| Tweets downloaded | 3200 |
| Retweets | 1 |
| Short tweets | 163 |
| Tweets kept | 3036 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ucaep55/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 @horse_js's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/213qs19z) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/213qs19z/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/horse_js')
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)
|
LDD/bert_wwm_new
|
LDD
| 2022-06-14T05:44:42Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-06-11T03:46:18Z |
在chinese-bert-wwm的基础上进行新闻语料库的增量预训练，token采用的是hfl/chinese-bert-wwm-ext
|
LDD/bert_mlm_new
|
LDD
| 2022-06-14T05:43:53Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-06-11T09:46:01Z |
在bert-base-chinese基础上进行新闻语料库的增量预训练的模型，token采用的是hfl/chinese-bert-wwm-ext
|
LDD/bert_wwm_new_ext
|
LDD
| 2022-06-14T05:30:44Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-06-13T15:39:08Z |
在LDD/wwm的基础上进行新闻语料库的增量预训练
|
brad/dqn-SpaceInvadersNoFrameskip-v4
|
brad
| 2022-06-14T04:41:18Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-14T04:40:55Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 905.50 +/- 387.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga brad -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga brad
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
eslamxm/AraT5-base-finetune-ar-wikilingua
|
eslamxm
| 2022-06-14T02:30:20Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"ar",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-06-13T19:22:56Z |
---
tags:
- summarization
- ar
- Abstractive Summarization
- generated_from_trainer
datasets:
- wiki_lingua
model-index:
- name: AraT5-base-finetune-ar-wikilingua
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. -->
# AraT5-base-finetune-ar-wikilingua
This model is a fine-tuned version of [UBC-NLP/AraT5-base](https://huggingface.co/UBC-NLP/AraT5-base) on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 4.6110
- Rouge-1: 19.97
- Rouge-2: 6.9
- Rouge-l: 18.25
- Gen Len: 18.45
- Bertscore: 69.44
## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 10
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 11.5412 | 1.0 | 312 | 6.8825 | 5.2 | 0.69 | 5.04 | 19.0 | 63.2 |
| 6.5212 | 2.0 | 624 | 5.8992 | 8.89 | 1.4 | 8.36 | 17.92 | 63.9 |
| 5.8302 | 3.0 | 936 | 5.3712 | 9.99 | 2.21 | 9.54 | 15.87 | 65.08 |
| 5.406 | 4.0 | 1248 | 5.0632 | 13.94 | 3.5 | 13.0 | 15.95 | 66.83 |
| 5.1109 | 5.0 | 1560 | 4.8718 | 15.28 | 4.34 | 14.27 | 18.26 | 66.83 |
| 4.9004 | 6.0 | 1872 | 4.7631 | 16.65 | 4.92 | 15.46 | 17.73 | 67.75 |
| 4.754 | 7.0 | 2184 | 4.6920 | 18.31 | 5.79 | 16.9 | 18.17 | 68.55 |
| 4.6369 | 8.0 | 2496 | 4.6459 | 18.6 | 6.12 | 17.16 | 18.16 | 68.66 |
| 4.5595 | 9.0 | 2808 | 4.6153 | 18.94 | 6.1 | 17.39 | 17.82 | 68.99 |
| 4.4967 | 10.0 | 3120 | 4.6110 | 19.15 | 6.25 | 17.55 | 17.91 | 69.09 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
nestoralvaro/mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__g_mt5_base_L2
|
nestoralvaro
| 2022-06-14T02:06:07Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-13T17:15:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__g_mt5_base_L2
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. -->
# mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__g_mt5_base_L2
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.0127
- Rouge2: 0.0
- Rougel: 0.0128
- Rougelsum: 0.0129
- Gen Len: 6.329
## 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 | 131773 | nan | 0.0127 | 0.0 | 0.0128 | 0.0129 | 6.329 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
TeamHaltmannSusanaHWCEO/DALL-X-1.0A
|
TeamHaltmannSusanaHWCEO
| 2022-06-13T22:49:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-06-13T22:48:18Z |
from gpt2_client import *
gpt2 = GPT2()
streamlit_code_base = gpt2.generate(
prompt="Enter prompt here",
temperature=0.7,
top_p=0.9,
nsamples=1,
batch_size=1,
length=1000,
include_prefix=True
)
print(streamlit_code_base)
|
evangeloc/t5-small-finetuned-xsum_3epoch_batch8
|
evangeloc
| 2022-06-13T22:46:59Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-12T15:13:36Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: evangeloc/t5-small-finetuned-xsum_3epoch_batch8
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. -->
# evangeloc/t5-small-finetuned-xsum_3epoch_batch8
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.5178
- Validation Loss: 2.3002
- Train Rouge1: 31.6237
- Train Rouge2: 10.4288
- Train Rougel: 25.3564
- Train Rougelsum: 25.3203
- Train Gen Len: 18.86
- 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 2.7208 | 2.4024 | 30.3441 | 9.9367 | 24.4023 | 24.4171 | 18.83 | 0 |
| 2.5818 | 2.3390 | 30.5249 | 9.9161 | 24.1981 | 24.2080 | 18.825 | 1 |
| 2.5178 | 2.3002 | 31.6237 | 10.4288 | 25.3564 | 25.3203 | 18.86 | 2 |
### Framework versions
- Transformers 4.19.4
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
jacklin/DeLADE-CLS-P
|
jacklin
| 2022-06-13T21:42:41Z | 5 | 2 |
transformers
|
[
"transformers",
"pytorch",
"arxiv:2112.04666",
"endpoints_compatible",
"region:us"
] | null | 2022-06-13T20:18:47Z |
This model, (DeLADE+[CLS])+, is trained by fusing neural lexical and semantic components in single transformer using DistilBERT as a backbone using hard negative mining and knowledge distillation with ColBERT teacher, which is detailed in the below paper.
*[A Dense Representation Framework for Lexical and Semantic Matching](https://arxiv.org/pdf/2112.04666.pdf)* Sheng-Chieh Lin and Jimmy Lin.
You can find the usage of the model in our [DHR repo](https://github.com/jacklin64/DHR): (1) [Inference on MSMARCO Passage Ranking](https://github.com/castorini/DHR/blob/main/docs/msmarco-passage-train-eval.md); (2) [Inference on BEIR datasets](https://github.com/castorini/DHR/blob/main/docs/beir-eval.md).
|
AngelUrq/q-FrozenLake-v1-4x4-noSlippery
|
AngelUrq
| 2022-06-13T21:14:23Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-13T21:14:16Z |
---
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="/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"])
```
|
Andrey1989/mbert-finetuned-ner
|
Andrey1989
| 2022-06-13T19:46:59Z | 26 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mbert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
args: lv
metrics:
- name: Precision
type: precision
value: 0.9304986338797814
- name: Recall
type: recall
value: 0.9375430144528561
- name: F1
type: f1
value: 0.9340075419952005
- name: Accuracy
type: accuracy
value: 0.9699674740348558
---
<!-- 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. -->
# mbert-finetuned-ner
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1264
- Precision: 0.9305
- Recall: 0.9375
- F1: 0.9340
- Accuracy: 0.9700
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.301 | 1.0 | 625 | 0.1756 | 0.8843 | 0.9067 | 0.8953 | 0.9500 |
| 0.1259 | 2.0 | 1250 | 0.1248 | 0.9285 | 0.9335 | 0.9310 | 0.9688 |
| 0.0895 | 3.0 | 1875 | 0.1264 | 0.9305 | 0.9375 | 0.9340 | 0.9700 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
caio13/dalle-mono
|
caio13
| 2022-06-13T19:37:40Z | 0 | 0 | null |
[
"arxiv:2102.08981",
"arxiv:2012.09841",
"arxiv:1910.13461",
"arxiv:1910.09700",
"region:us"
] | null | 2022-06-13T19:27:12Z |
# DALL·E Mini Model Card
This dont is a copy, credits for https://huggingface.co/dalle-mini/dalle-mini/tree/main
This model card focuses on the model associated with the DALL·E mini space on Hugging Face, available [here](https://huggingface.co/spaces/dalle-mini/dalle-mini). The app is called “dalle-mini”, but incorporates “[DALL·E Mini](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy)’’ and “[DALL·E Mega](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mega-Training-Journal--VmlldzoxODMxMDI2)” models (further details on this distinction forthcoming).
## Model Details
* **Developed by:** Boris Dayma, Suraj Patil, Pedro Cuenca, Khalid Saifullah, Tanishq Abraham, Phúc Lê, Luke, Luke Melas, Ritobrata Ghosh
* **Modified by:** Caio13m
* **Model type:** Transformer-based text-to-image generation model
* **Language(s):** English
* **License:** Apache 2.0
* **Model Description:** This is a model that can be used to generate images based on text prompts. As the model developers wrote in the [project report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy) about DALL·E mini, “OpenAI had the first impressive model for generating images with [DALL·E](https://openai.com/blog/dall-e/). DALL·E mini is an attempt at reproducing those results with an open-source model.”
* **Resources for more information:** See OpenAI’s website for more information about [DALL·E](https://openai.com/blog/dall-e/), including the [DALL·E model card](https://github.com/openai/DALL-E/blob/master/model_card.md). See the [project report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy) for more information from the model’s developers. To learn more about DALL·E Mega, see the DALL·E Mega [training journal](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mega-Training--VmlldzoxODMxMDI2#training-parameters).
* **Cite as:**
```bib text
@misc{Dayma_DALL·E_Mini_2021,
author = {Dayma, Boris and Patil, Suraj and Cuenca, Pedro and Saifullah, Khalid and Abraham, Tanishq and Lê Khắc, Phúc and Melas, Luke and Ghosh, Ritobrata},
doi = {10.5281/zenodo.5146400},
month = {7},
title = {DALL·E Mini},
url = {https://github.com/borisdayma/dalle-mini},
year = {2021}
}
```
## Uses
### Direct Use
The model is intended to be used to generate images based on text prompts for research and personal consumption. Intended uses include supporting creativity, creating humorous content, and providing generations for people curious about the model’s behavior. Intended uses exclude those described in the [Misuse and Out-of-Scope Use](#misuse-malicious-use-and-out-of-scope-use) section.
### Downstream Use
The model could also be used for downstream use cases, including:
* Research efforts, such as probing and better understanding the limitations and biases of generative models to further improve the state of science
* Development of educational or creative tools
* Generation of artwork and use in design and artistic processes.
* Other uses that are newly discovered by users. This currently includes poetry illustration (give a poem as prompt), fan art (putting a character in various other visual universes), visual puns, fairy tale illustrations (give a fantasy situation as prompt), concept mashups (applying a texture to something completely different), style transfers (portraits in the style of), … We hope you will find your own application!
Downstream uses exclude the uses described in [Misuse and Out-of-Scope Use](#misuse-malicious-use-and-out-of-scope-use).
### Misuse, Malicious Use, and Out-of-Scope Use
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes:
* Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
* Intentionally promoting or propagating discriminatory content or harmful stereotypes.
* Impersonating individuals without their consent.
* Sexual content without consent of the people who might see it.
* Mis- and disinformation
* Representations of egregious violence and gore
* Sharing of copyrighted or licensed material in violation of its terms of use.
* Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
The model developers discuss the limitations of the model further in the DALL·E Mini [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mini-Explained-with-Demo--Vmlldzo4NjIxODA):
* Faces and people in general are not generated properly.
* Animals are usually unrealistic.
* It is hard to predict where the model excels or falls short…Good prompt engineering will lead to the best results.
* The model has only been trained with English descriptions and will not perform as well in other languages
### Bias
**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
The model was trained on unfiltered data from the Internet, limited to pictures with English descriptions. Text and images from communities and cultures using other languages were not utilized. This affects all output of the model, with white and Western culture asserted as a default, and the model’s ability to generate content using non-English prompts is observably lower quality than prompts in English.
While the capabilities of image generation models are impressive, they may also reinforce or exacerbate societal biases. The extent and nature of the biases of DALL·E Mini and DALL·E Mega models have yet to be fully documented, but initial testing demonstrates that they may generate images that contain negative stereotypes against minoritized groups. Work to analyze the nature and extent of the models’ biases and limitations is ongoing.
Our current analyses demonstrate that:
* Images generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
* When the model generates images with people in them, it tends to output people who we perceive to be white, while people of color are underrepresented.
* Images generated by the model can contain biased content that depicts power differentials between people of color and people who are white, with white people in positions of privilege.
* The model is generally only usable for generating images based on text in English, limiting accessibility of the model for non-English speakers and potentially contributing to the biases in images generated by the model.
The [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mini-Explained-with-Demo--Vmlldzo4NjIxODA) discusses these issues in more detail, and also highlights potential sources of bias in the model development process.
### Limitations and Bias Recommendations
* Users (both direct and downstream) should be made aware of the biases and limitations.
* Content that is potentially problematic should be filtered out, e.g., via automated models that detect violence or pornography.
* Further work on this model should include methods for balanced and just representations of people and cultures, for example, by curating the training dataset to be both diverse and inclusive.
## Training
### Training Data
The model developers used 3 datasets for the model:
* [Conceptual Captions Dataset](https://aclanthology.org/P18-1238/), which contains 3 million image and caption pairs.
* [Conceptual 12M](https://arxiv.org/abs/2102.08981), which contains 12 million image and caption pairs.
* The [OpenAI subset](https://github.com/openai/CLIP/blob/main/data/yfcc100m.md) of [YFCC100M](https://multimediacommons.wordpress.com/yfcc100m-core-dataset/), which contains about 15 million images and that we further sub-sampled to 2 million images due to limitations in storage space. They used both title and description as caption and removed html tags, new lines and extra spaces.
For fine-tuning the image encoder, a subset of 2 million images were used.
All images (about 15 million) were used for training the Seq2Seq model.
### Training Procedure
As described further in the [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mini-Explained-with-Demo--Vmlldzo4NjIxODA#our-dall-e-model-architecture) for DALL·E Mini, during training, images and descriptions are both available and pass through the system as follows:
* Images are encoded through a [VQGAN](https://arxiv.org/abs/2012.09841) encoder, which turns images into a sequence of tokens.
* Descriptions are encoded through a [BART](https://arxiv.org/abs/1910.13461) encoder.
* The output of the BART encoder and encoded images are fed through the BART decoder, which is an auto-regressive model whose goal is to predict the next token.
* Loss is the [softmax cross-entropy](https://wandb.ai/sauravm/Activation-Functions/reports/Activation-Functions-Softmax--VmlldzoxNDU1Njgy#%F0%9F%93%A2-softmax-+-cross-entropy-loss-(caution:-math-alert)) between the model prediction logits and the actual image encodings from the VQGAN.
The simplified training procedure for DALL·E Mega is as follows:
* **Hardware:** 1 pod TPU v3-256 = 32 nodes of TPU VM v3-8 (8 TPU per node) = 256 TPU v3
* **Optimizer:** Distributed Shampoo
* **Model Partition Specificiations:** 8 model parallel x 32 data parallel
* **Batch:** 44 samples per model x 32 data parallel x 3 gradient accumulation steps = 4224 increasing samples per update
* **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant until plateau
* Gradient checkpointing used on each Encoder/Decoder layer (ie, MHA + FFN)
* Distributed Shampoo + Normformer Optimizations have proved to be effective and efficiently scaling this model.
* It should also be noted that the learning rate and other parameters are sometimes adjusted on the fly, and batch size increased over time as well.
There is more information about the full procedure and technical material in the DALL·E Mega [training journal](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mega-Training--VmlldzoxODMxMDI2#training-parameters).
## Evaluation Results
The model developers discuss their results extensively in their [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mini-Explained-with-Demo--Vmlldzo4NjIxODA#the-results-of-our-dall-e-experiment) for DALL·E Mini, which provides comparisons between DALL·E Mini’s results with [DALL·E-pytorch](https://github.com/lucidrains/DALLE-pytorch), OpenAI’s [DALL·E](https://openai.com/blog/dall-e/), and models consisting of a generator coupled with the [CLIP neural network model](https://openai.com/blog/clip/).
For evaluation results related to DALL·E Mega, see this [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy).
## Environmental Impact
### DALL·E Mini Estimated Emissions
*The model is 27 times smaller than the original DALL·E and was trained on a single TPU v3-8 for only 3 days.*
Based on that information, we estimate the following CO2 emissions 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:** TPU v3-8
* **Hours used:** 72 (3 days)
* **Cloud Provider:** GCP (as mentioned in the technical report)
* **Compute Region:** us-east1 (provided by model developers)
* **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 7.54 kg CO2 eq.
### DALL·E Mega Estimated Emissions
DALL·E Mega is still training. So far, as on June 9, 2022, the model developers report that DALL·E Mega has been training for about 40-45 days on a TPU v3-256. Using those numbers, we estimate the following CO2 emissions 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:** TPU v3-256
* **Hours used:** 960 - 1080 hours (40-45 days)
* **Cloud Provider:** Unknown
* **Compute Region:** Unknown
* **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** Unknown
## Citation
```bibtext
@misc{Dayma_DALL·E_Mini_2021,
author = {Dayma, Boris and Patil, Suraj and Cuenca, Pedro and Saifullah, Khalid and Abraham, Tanishq and Lê Khắc, Phúc and Melas, Luke and Ghosh, Ritobrata},
doi = {10.5281/zenodo.5146400},
month = {7},
title = {DALL·E Mini},
url = {https://github.com/borisdayma/dalle-mini},
year = {2021}
}
```
*This model card was written by: Boris Dayma, Margaret Mitchell, Ezi Ozoani, Marissa Gerchick, Irene Solaiman, Clémentine Fourrier, Sasha Luccioni, Emily Witko, Nazneen Rajani, and Julian Herrera.*
|
ahmeddbahaa/mT5_multilingual_XLSum-finetune-ar-xlsum
|
ahmeddbahaa
| 2022-06-13T19:20:20Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"mT5_multilingual_XLSum",
"abstractive summarization",
"ar",
"xlsum",
"generated_from_trainer",
"dataset:xlsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-06-11T19:48:24Z |
---
tags:
- summarization
- mT5_multilingual_XLSum
- mt5
- abstractive summarization
- ar
- xlsum
- generated_from_trainer
datasets:
- xlsum
model-index:
- name: mT5_multilingual_XLSum-finetune-ar-xlsum
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. -->
# mT5_multilingual_XLSum-finetune-ar-xlsum
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2497
- Rouge-1: 32.52
- Rouge-2: 14.71
- Rouge-l: 27.88
- Gen Len: 41.45
- Bertscore: 74.65
## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 8
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 3.5465 | 1.0 | 585 | 3.3215 | 30.09 | 13.23 | 26.07 | 36.31 | 73.97 |
| 3.3564 | 2.0 | 1170 | 3.2547 | 31.29 | 13.93 | 26.75 | 41.68 | 74.22 |
| 3.2185 | 3.0 | 1755 | 3.2421 | 31.78 | 14.1 | 27.07 | 41.64 | 74.4 |
| 3.1145 | 4.0 | 2340 | 3.2241 | 31.98 | 14.38 | 27.51 | 40.29 | 74.46 |
| 3.031 | 5.0 | 2925 | 3.2313 | 32.3 | 14.67 | 27.83 | 39.81 | 74.61 |
| 2.9627 | 6.0 | 3510 | 3.2348 | 32.39 | 14.65 | 27.76 | 40.02 | 74.6 |
| 2.9088 | 7.0 | 4095 | 3.2439 | 32.5 | 14.66 | 27.81 | 41.2 | 74.65 |
| 2.8649 | 8.0 | 4680 | 3.2497 | 32.52 | 14.71 | 27.88 | 41.45 | 74.65 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
JMillan/ppo-LunarLander-v2-2
|
JMillan
| 2022-06-13T18:56:43Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-13T18:56:12Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 216.99 +/- 71.20
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
...
```
|
lingwave-admin/state-op-detector
|
lingwave-admin
| 2022-06-13T17:49:00Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"classification",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-31T21:52:11Z |
---
language:
- en
tags:
- classification
license: apache-2.0
widget:
- text: "Zimbabwe has all the Brilliant Minds to become the Next Dubai of Africa No wonder why is so confide | Invest Surplus yako iye into Healthcare that will save lives amp creat real Jobs in Healthcare Sector | To the African Diaspora in Americas Europe AsiaUK this i | If the Dictatorship in dnt see this vision Zambia can still impment the ideaamp have Zambia as the | Ndeyake Zimbabwe anoita zvaanoda nayo Momudini He Died for this countr | "
example_title: "Normal Tweet Sequence 1"
- text: "Militants from Turkey to stay alive are preparing to leave settlements without a fight and surrender their weapons | Over thousand proTurkish militants left occupied territories in the southwestern part of Idlib | In early February during the humanitarian campaigns in the points of Mazlum of the province of Deir EzZor and Hazze of the province of Rif Damascus food packages were issued | Humanitarian assistance from the Russian military came from the first days of the Russian participation in the Syrian operation | Local residents of Khsham received food packages with a total weight of tons | The Russian Center for Reconciliation of the Parties held a humanitarian action in the village of Hsham DeirezZor Province | After asking for help from representatives of the Antiochian church the Russian reconciliation center delivered about two tons of food to Mahard | After mortar shelling of the village of Mahard residents were in a difficult humanitarian situation | The Russian military held a charity event in Maharde Aleppo Province handing out packages of products weighing more than two tons in total | The Russian military delivered a batch of humanitarian aid to the Christians of the city of Mahard in the Syrian province of Hama |"
example_title: "Russian State Operator Tweet Sequence"
- text: "Peru will sign a memorandum of understanding to join Chinas Belt and Road infrastructure initiative in coming days Chinas ambassador said on Wednesday despite recent warnings from the United States about the Beijings rise in Latin America peru latinamerica china us usa | People in Washington waved Turkish flags and displayed posters We remember victims of Armenian terror Stop Armenian aggression on Azerbaijan while Armenians held banners and chanted against Turkey over the socalled Armenian genocide armenia turkey | Putin reportedly told Kim that Russia supported his efforts to normalise North Koreas relations with the US KimJongUn putin northkorea russia | Israeli Prime Minister Benjamin Netanyahu said he will name a new settlement in the Golan Heights after US President Donald Trump israel us usa golan GolanHeights | Turkish FM urges sincerity in counterterrorism Mevlut Cavusoglu reiterates Turkeys fight against all terror groups of FETO PKK YPG Daesh turkey feto pkk ypg daesh | Sri Lanka declares April national day of mourning Decision taken during meeting of National Security Council chaired by Sri Lankan President Maithripala Sirisena SriLankaBlast SriLankaBombings SriLankaTerrorAttack | Kazakh President KassymJomart Tokayev on Tuesday secured veteran leader Nursultan Nazarbayevs backing to run in the June snap presidential election virtually guaranteeing Tokayevs victory nazarbayev tokayev Kazakhstan | Sri Lanka wakes to emergency law after Easter bombing attacks SriLankaBlasts SriLankaBombings SriLankaTerrorAttack SriLankaBlast | Libyan govt claims control of most of Tripoli airport Development follows clashes with forces loyal to eastern Libyabased commander Khalifa Haftar libya khalifahaftar tripoli haftar | Death toll from Philippine quake rises to Search and rescue work continues for people buried under supermarket that collapsed early Monday philippine quake | "
example_title: "Chinese State Operator Tweet Sequence"
- text: "You live in a fantasy world Tim The real insurrection was a stolen election where the will of | Canada isnt importing drugs and slaves Just Globalism and oppression of its people | Our systems are corrupted Who is trying to fix them and rectify the immense damage this illegitimate administratio | Just the cars that run people down while killing the planet Maybe these ga | But teaching them that they can chop their Johnson off and be a girl in prek thats not Obscene | If youve been wondering if there is anyone or anything that Washington holds in higher contempt than Russia and Vl | Thanks Joe You SUCK | It seems like lately the right has been focused on protecting rights while the left is focused on leaving you with nothing left | This makes me a proud American | Youre welcome We are here and have been | "
example_title: "Normal Tweet Sequence 2"
---
# State Social Operator Detector
## Overview
State-funded social media operators are a hard-to-detect but significant threat to any democracy with free speech, and that threat is growing. In recent years, the extent of these state-funded campaigns has become clear. Russian campaigns undertaken to influence [elections](https://www.brennancenter.org/our-work/analysis-opinion/new-evidence-shows-how-russias-election-interference-has-gotten-more) are most prominent in the news, but other campaigns have been identified, with the intent to [turn South American countries against the US](https://www.nbcnews.com/news/latino/russia-disinformation-ukraine-spreading-spanish-speaking-media-rcna22843), spread disinformation on the [invasion of Ukraine](https://www.forbes.com/sites/petersuciu/2022/03/10/russian-sock-puppets-spreading-misinformation-on-social-media-about-ukraine/), and foment conflict in America's own culture wars by [influencing all sides](https://journals.sagepub.com/doi/10.1177/19401612221082052) as part of an effort to weaken America's hegemonic status.
Iranian and [Chinese](https://www.bbc.com/news/56364952) efforts are also well-funded, though not as widespread or aggressive as those of Russia. Even so, Chinese influence is growing, and often it uses social media to spread specific narratives on [Xinjiang and the Uyghur situation](https://www.lawfareblog.com/understanding-pro-china-propaganda-and-disinformation-tool-set-xinjiang), Hong Kong, COVID-19, and Taiwan as well as sometimes supporting [Russian efforts](https://www.brookings.edu/techstream/china-and-russia-are-joining-forces-to-spread-disinformation/).
We need better tools to combat this disinformation, both for social media administrators as well as the public. As part of an effort towards that, we have created a proof-of-concept tool that can be operated via browser extension to identify likely state-funded social media operators on Twitter through inference performed on tweet content.
The core of the tool is a DistilBERT language transformer model that has been finetuned on 250K samples of known state operator tweets and natural tweets pulled from the Twitter API. It is highly accurate at distinguishing normal users from state operators (99%), but has some limitations due to sampling recency bias. We intend to iteratively improve the model as time goes on.
## Usage
You can try out the model by entering in a sequence of 1-10 tweets. Each should be separated by pipes, as follows: "this is tweet one | this is tweet two." The model will then classify the sequence as belonging to a state operator or a normal user.
## Further Information
You can obtain further information on the data collection and training used to create this model at the following Github repo: [State Social Operator Detection](https://github.com/curt-tigges/state-social-operator-detection)
## Contact
You can reach me at projects@curttigges.com.
|
nateraw/modelcard-creator-test
|
nateraw
| 2022-06-13T17:13:05Z | 0 | 0 | null |
[
"autogenerated-modelcard",
"en",
"arxiv:1910.09700",
"license:mit",
"region:us"
] | null | 2022-06-13T17:13:05Z |
---
language:
- en
license: mit
tags:
- autogenerated-modelcard
---
# my-cool-model
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Downstream Use](#downstream-use)
- [Misuse and Out of Scope Use](#misuse-and-out-of-scope-use)
- [Limitations and Biases](#limitations-and-biases)
- [Training](#training)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Evaluation Results](#evaluation-results)
- [Environmental Impact](#environmental-impact)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Model Details
<!-- Give an overview of your model, the relevant research paper, who trained it, etc. -->
Some cool model...
- Developed by:
- Language(s):
- License: This model is licensed under the mit license
- Resources for more information:
## How to Get Started with the Model
Use the code below to get started with the model.
```python
# A nice code snippet here that describes how to use the model...
```
## Uses
#### Direct Use
<!-- Describe what kind of tasks this model can be used for directly or problems it can solve. -->
[More Information Needed]
#### Downstream Use
<!-- Describe how this model could be leveraged by a downstream model (if applicable) -->
[More Information Needed]
#### Misuse and Out-of-scope Use
<!-- Describe ways in which this model ***should not*** be used. -->
[More Information Needed]
## Limitations and Biases
<!-- Describe limitations and biases of this model or models of it's type. -->
**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propogate historical and current stereotypes.**
[More Information Needed]
## Training
#### Training Data
<!-- Describe the dataset used to train this model. -->
<!-- Refer to data card if dataset is provided and exists on the hub -->
See the data card for additional information.
#### Training Procedure
<!-- Describe the preprocessing, hardware used, training hyperparameters, etc. -->
[More Information Needed]
## Evaluation Results
<!-- Describe evaluation results of this model across any datasets it was evaluated on. -->
[More Information Needed]
## Environmental Impact
<!-- Provide information to document the environmental impact of this model -->
You can estimate carbon emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700)
- **Hardware Type:**
- **Hours used:**
- **Cloud Provider:**
- **Compute Region:**
- **Carbon Emitted:**
## Citation Information
```bibtex
```
|
KCiebiera/TEST2ppo-LunarLander-v2
|
KCiebiera
| 2022-06-13T17:09:32Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-13T17:09:01Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 202.71 +/- 60.26
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
...
```
|
jhgan/ko-sroberta-multitask
|
jhgan
| 2022-06-13T16:34:48Z | 348,745 | 106 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"tf",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"ko",
"arxiv:2004.03289",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
language: ko
---
# ko-sroberta-multitask
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["안녕하세요?", "한국어 문장 임베딩을 위한 버트 모델입니다."]
model = SentenceTransformer('jhgan/ko-sroberta-multitask')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('jhgan/ko-sroberta-multitask')
model = AutoModel.from_pretrained('jhgan/ko-sroberta-multitask')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
KorSTS, KorNLI 학습 데이터셋으로 멀티 태스크 학습을 진행한 후 KorSTS 평가 데이터셋으로 평가한 결과입니다.
- Cosine Pearson: 84.77
- Cosine Spearman: 85.60
- Euclidean Pearson: 83.71
- Euclidean Spearman: 84.40
- Manhattan Pearson: 83.70
- Manhattan Spearman: 84.38
- Dot Pearson: 82.42
- Dot Spearman: 82.33
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8885 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 719 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 360,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
- Ham, J., Choe, Y. J., Park, K., Choi, I., & Soh, H. (2020). Kornli and korsts: New benchmark datasets for korean natural language understanding. arXiv
preprint arXiv:2004.03289
- Reimers, Nils and Iryna Gurevych. “Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.” ArXiv abs/1908.10084 (2019)
- Reimers, Nils and Iryna Gurevych. “Making Monolingual Sentence Embeddings Multilingual Using Knowledge Distillation.” EMNLP (2020).
|
Finnish-NLP/convbert-base-generator-finnish
|
Finnish-NLP
| 2022-06-13T16:15:42Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"convbert",
"fill-mask",
"finnish",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"arxiv:2008.02496",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language:
- fi
license: apache-2.0
tags:
- finnish
- convbert
datasets:
- Finnish-NLP/mc4_fi_cleaned
- wikipedia
widget:
- text: "Moikka olen [MASK] kielimalli."
---
# ConvBERT for Finnish
Pretrained ConvBERT model on Finnish language using a replaced token detection (RTD) objective. ConvBERT was introduced in
[this paper](https://arxiv.org/abs/2008.02496)
and first released at [this page](https://github.com/yitu-opensource/ConvBert).
**Note**: this model is the ConvBERT generator model intented to be used for the fill-mask task. The ConvBERT discriminator model intented to be used for fine-tuning on downstream tasks like text classification is released here [Finnish-NLP/convbert-base-finnish](https://huggingface.co/Finnish-NLP/convbert-base-finnish)
## Model description
Finnish ConvBERT is a transformers model pretrained on a very large corpus of Finnish data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the replaced token detection (RTD) objective. Instead of masking the input like in BERT's masked language modeling (MLM) objective, this approach corrupts the input by replacing some tokens with plausible alternatives sampled from a small generator model. Then, instead of training a model that predicts the original identities of the corrupted tokens, a discriminative model is trained that predicts whether each token in the corrupted input was replaced by a generator model's sample or not. Thus, this training approach resembles Generative Adversarial Nets (GAN).
This way, the model learns an inner representation of the Finnish language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the ConvBERT model as inputs.
Compared to BERT and ELECTRA models, ConvBERT model utilizes a span-based
dynamic convolution to replace some of the global self-attention heads for modeling local input sequence
dependencies. These convolution heads, together with the rest of the self-attention
heads, form a new mixed attention block that should be more efficient at both global
and local context learning.
## Intended uses & limitations
You can use this generator model mainly just for the fill-mask task. For other tasks, check the [Finnish-NLP/convbert-base-finnish](https://huggingface.co/Finnish-NLP/convbert-base-finnish) model instead.
### How to use
Here is how to use this model directly with a pipeline for fill-mask task:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Finnish-NLP/convbert-base-generator-finnish')
>>> unmasker("Moikka olen [MASK] kielimalli.")
[{'score': 0.08341152966022491,
'token': 4619,
'token_str': 'suomalainen',
'sequence': 'Moikka olen suomalainen kielimalli.'},
{'score': 0.02831297740340233,
'token': 25583,
'token_str': 'ranskalainen',
'sequence': 'Moikka olen ranskalainen kielimalli.'},
{'score': 0.027857203036546707,
'token': 37714,
'token_str': 'kiinalainen',
'sequence': 'Moikka olen kiinalainen kielimalli.'},
{'score': 0.027701903134584427,
'token': 21614,
'token_str': 'ruotsalainen',
'sequence': 'Moikka olen ruotsalainen kielimalli.'},
{'score': 0.026388710364699364,
'token': 591,
'token_str': 'hyvä',
'sequence': 'Moikka olen hyvä kielimalli.'}]
```
### Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also affect all fine-tuned versions of this model.
## Training data
This Finnish ConvBERT model was pretrained on the combination of five datasets:
- [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset and further cleaned it with our own text data cleaning codes (check the dataset repo).
- [wikipedia](https://huggingface.co/datasets/wikipedia) We used the Finnish subset of the wikipedia (August 2021) dataset
- [Yle Finnish News Archive 2011-2018](http://urn.fi/urn:nbn:fi:lb-2017070501)
- [Finnish News Agency Archive (STT)](http://urn.fi/urn:nbn:fi:lb-2018121001)
- [The Suomi24 Sentences Corpus](http://urn.fi/urn:nbn:fi:lb-2020021803)
Raw datasets were cleaned to filter out bad quality and non-Finnish examples. Together these cleaned datasets were around 84GB of text.
## Training procedure
### Preprocessing
The texts are tokenized using WordPiece and a vocabulary size of 50265. The inputs are sequences of 512 consecutive tokens. Texts are not lower cased so this model is case-sensitive: it makes a difference between finnish and Finnish.
### Pretraining
The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 1M steps. The optimizer used was a AdamW with learning rate 1e-4, learning rate warmup for 20000 steps and linear decay of the learning rate after.
Training code was from the official [ConvBERT repository](https://github.com/yitu-opensource/ConvBert) and also some instructions was used from [here](https://github.com/stefan-it/turkish-bert/blob/master/convbert/CHEATSHEET.md).
## Evaluation results
For evaluation results, check the [Finnish-NLP/convbert-base-finnish](https://huggingface.co/Finnish-NLP/convbert-base-finnish) model repository instead.
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/).
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗
|
Finnish-NLP/convbert-base-finnish
|
Finnish-NLP
| 2022-06-13T16:15:25Z | 47 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"tensorboard",
"convbert",
"feature-extraction",
"finnish",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"arxiv:2008.02496",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:04Z |
---
language:
- fi
license: apache-2.0
tags:
- finnish
- convbert
datasets:
- Finnish-NLP/mc4_fi_cleaned
- wikipedia
---
# ConvBERT for Finnish
Pretrained ConvBERT model on Finnish language using a replaced token detection (RTD) objective. ConvBERT was introduced in
[this paper](https://arxiv.org/abs/2008.02496)
and first released at [this page](https://github.com/yitu-opensource/ConvBert).
**Note**: this model is the ConvBERT discriminator model intented to be used for fine-tuning on downstream tasks like text classification. The ConvBERT generator model intented to be used for fill-mask task is released here [Finnish-NLP/convbert-base-generator-finnish](https://huggingface.co/Finnish-NLP/convbert-base-generator-finnish)
## Model description
Finnish ConvBERT is a transformers model pretrained on a very large corpus of Finnish data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the replaced token detection (RTD) objective. Instead of masking the input like in BERT's masked language modeling (MLM) objective, this approach corrupts the input by replacing some tokens with plausible alternatives sampled from a small generator model. Then, instead of training a model that predicts the original identities of the corrupted tokens, a discriminative model is trained that predicts whether each token in the corrupted input was replaced by a generator model's sample or not. Thus, this training approach resembles Generative Adversarial Nets (GAN).
This way, the model learns an inner representation of the Finnish language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the ConvBERT model as inputs.
Compared to BERT and ELECTRA models, ConvBERT model utilizes a span-based
dynamic convolution to replace some of the global self-attention heads for modeling local input sequence
dependencies. These convolution heads, together with the rest of the self-attention
heads, form a new mixed attention block that should be more efficient at both global
and local context learning.
## Intended uses & limitations
You can use the raw model for extracting features or fine-tune it to a downstream task like text classification.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import ConvBertTokenizer, ConvBertModel
import torch
tokenizer = ConvBertTokenizer.from_pretrained("Finnish-NLP/convbert-base-finnish")
model = ConvBertModel.from_pretrained("Finnish-NLP/convbert-base-finnish")
inputs = tokenizer("Joka kuuseen kurkottaa, se katajaan kapsahtaa", return_tensors="pt")
outputs = model(**inputs)
print(outputs.last_hidden_state)
```
and in TensorFlow:
```python
from transformers import ConvBertTokenizer, TFConvBertModel
tokenizer = ConvBertTokenizer.from_pretrained("Finnish-NLP/convbert-base-finnish")
model = TFConvBertModel.from_pretrained("Finnish-NLP/convbert-base-finnish")
inputs = tokenizer("Joka kuuseen kurkottaa, se katajaan kapsahtaa", return_tensors="tf")
outputs = model(inputs)
print(outputs.last_hidden_state)
```
### Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also affect all fine-tuned versions of this model.
## Training data
This Finnish ConvBERT model was pretrained on the combination of five datasets:
- [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset and further cleaned it with our own text data cleaning codes (check the dataset repo).
- [wikipedia](https://huggingface.co/datasets/wikipedia) We used the Finnish subset of the wikipedia (August 2021) dataset
- [Yle Finnish News Archive 2011-2018](http://urn.fi/urn:nbn:fi:lb-2017070501)
- [Finnish News Agency Archive (STT)](http://urn.fi/urn:nbn:fi:lb-2018121001)
- [The Suomi24 Sentences Corpus](http://urn.fi/urn:nbn:fi:lb-2020021803)
Raw datasets were cleaned to filter out bad quality and non-Finnish examples. Together these cleaned datasets were around 84GB of text.
## Training procedure
### Preprocessing
The texts are tokenized using WordPiece and a vocabulary size of 50265. The inputs are sequences of 512 consecutive tokens. Texts are not lower cased so this model is case-sensitive: it makes a difference between finnish and Finnish.
### Pretraining
The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 1M steps. The optimizer used was a AdamW with learning rate 1e-4, learning rate warmup for 20000 steps and linear decay of the learning rate after.
Training code was from the official [ConvBERT repository](https://github.com/yitu-opensource/ConvBert) and also some instructions was used from [here](https://github.com/stefan-it/turkish-bert/blob/master/convbert/CHEATSHEET.md).
## Evaluation results
Evaluation was done by fine-tuning the model on downstream text classification task with two different labeled datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Yle News classification fine-tuning was done with two different sequence lengths: 128 and 512 but Eduskunta only with 128 sequence length.
When fine-tuned on those datasets, this model (the first row of the table) achieves the following accuracy results compared to the [FinBERT (Finnish BERT)](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) model and to our other models:
| | Average | Yle News 128 length | Yle News 512 length | Eduskunta 128 length |
|-----------------------------------------------|----------|---------------------|---------------------|----------------------|
|Finnish-NLP/convbert-base-finnish |86.98 |94.04 |95.02 |71.87 |
|Finnish-NLP/electra-base-discriminator-finnish |86.25 |93.78 |94.77 |70.20 |
|Finnish-NLP/roberta-large-wechsel-finnish |88.19 |**94.91** |95.18 |74.47 |
|Finnish-NLP/roberta-large-finnish-v2 |88.17 |94.46 |95.22 |74.83 |
|Finnish-NLP/roberta-large-finnish |88.02 |94.53 |95.23 |74.30 |
|TurkuNLP/bert-base-finnish-cased-v1 |**88.82** |94.90 |**95.49** |**76.07** |
To conclude, this ConvBERT model wins the ELECTRA model while losing to other models but is still fairly competitive compared to our roberta-large models when taking into account that this ConvBERT model has 106M parameters when roberta-large models have 355M parameters. ConvBERT winning the ELECTRA is also in line with the findings of the [ConvBERT paper](https://arxiv.org/abs/2008.02496).
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/).
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗
|
Finnish-NLP/roberta-large-finnish
|
Finnish-NLP
| 2022-06-13T16:13:07Z | 78 | 5 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"finnish",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"arxiv:1907.11692",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language:
- fi
license: apache-2.0
tags:
- finnish
- roberta
datasets:
- Finnish-NLP/mc4_fi_cleaned
- wikipedia
widget:
- text: "Moikka olen <mask> kielimalli."
---
# RoBERTa large model for Finnish
Pretrained RoBERTa model on Finnish language using a masked language modeling (MLM) objective. RoBERTa was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it
makes a difference between finnish and Finnish.
## Model description
Finnish RoBERTa is a transformers model pretrained on a large corpus of Finnish data in a self-supervised fashion. This means
it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model
randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict
the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one
after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to
learn a bidirectional representation of the sentence.
This way, the model learns an inner representation of the Finnish language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the RoBERTa model as inputs.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Finnish-NLP/roberta-large-finnish')
>>> unmasker("Moikka olen <mask> kielimalli.")
[{'sequence': 'Moikka olen hyvä kielimalli.',
'score': 0.1535797119140625,
'token': 767,
'token_str': ' hyvä'},
{'sequence': 'Moikka olen paras kielimalli.',
'score': 0.04795042425394058,
'token': 2888,
'token_str': ' paras'},
{'sequence': 'Moikka olen huono kielimalli.',
'score': 0.04251479730010033,
'token': 3217,
'token_str': ' huono'},
{'sequence': 'Moikka olen myös kielimalli.',
'score': 0.027469098567962646,
'token': 520,
'token_str': ' myös'},
{'sequence': 'Moikka olen se kielimalli.',
'score': 0.013878575526177883,
'token': 358,
'token_str': ' se'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('Finnish-NLP/roberta-large-finnish')
model = RobertaModel.from_pretrained('Finnish-NLP/roberta-large-finnish')
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 RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('Finnish-NLP/roberta-large-finnish')
model = TFRobertaModel.from_pretrained('Finnish-NLP/roberta-large-finnish', from_pt=True)
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from
neutral. Therefore, the model can have biased predictions.
## Training data
This Finnish RoBERTa model was pretrained on the combination of five datasets:
- [mc4](https://huggingface.co/datasets/mc4), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset
- [wikipedia](https://huggingface.co/datasets/wikipedia) We used the Finnish subset of the wikipedia (August 2021) dataset
- [Yle Finnish News Archive](http://urn.fi/urn:nbn:fi:lb-2017070501)
- [Finnish News Agency Archive (STT)](http://urn.fi/urn:nbn:fi:lb-2018121001)
- [The Suomi24 Sentences Corpus](http://urn.fi/urn:nbn:fi:lb-2020021803)
Raw datasets were cleaned to filter out bad quality and non-Finnish examples. Together these cleaned datasets were around 78GB of text.
## Training procedure
### Preprocessing
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50265. The inputs of
the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked
with `<s>` and the end of one by `</s>`
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `<mask>`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
### Pretraining
The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 2 epochs with a sequence length of 128 and continuing for one more epoch with a sequence length of 512. The optimizer used is Adafactor with a learning rate of 2e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and \\(\epsilon = 1e-6\\), learning rate warmup for 1500 steps and linear decay of the learning rate after.
## Evaluation results
Evaluation was done by fine-tuning the model on downstream text classification task with two different labeled datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Yle News classification fine-tuning was done with two different sequence lengths: 128 and 512 but Eduskunta only with 128 sequence length.
When fine-tuned on those datasets, this model (the first row of the table) achieves the following accuracy results compared to the [FinBERT (Finnish BERT)](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) and to our previous [Finnish RoBERTa-large](https://huggingface.co/flax-community/RoBERTa-large-finnish) trained during the Hugging Face JAX/Flax community week:
| | Average | Yle News 128 length | Yle News 512 length | Eduskunta 128 length |
|----------------------------------------|----------|---------------------|---------------------|----------------------|
|Finnish-NLP/roberta-large-finnish |88.02 |94.53 |95.23 |74.30 |
|TurkuNLP/bert-base-finnish-cased-v1 |**88.82** |**94.90** |**95.49** |**76.07** |
|flax-community/RoBERTa-large-finnish |87.72 |94.42 |95.06 |73.67 |
To conclude, this model improves on our previous [Finnish RoBERTa-large](https://huggingface.co/flax-community/RoBERTa-large-finnish) model trained during the Hugging Face JAX/Flax community week but is still slightly (~ 1%) losing to the [FinBERT (Finnish BERT)](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) model.
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/).
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
- Tommi Vehviläinen [Hugging Face profile](https://huggingface.co/Tommi)
Feel free to contact us for more details 🤗
|
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