modelId
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 00:39:23
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 526
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
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|---|---|---|---|---|---|---|---|---|---|
meghazisofiane/opus-mt-en-ar-finetuned-en-to-ar-test2-instances
|
meghazisofiane
| 2022-06-08T23:32:45Z | 101 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:un_multi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-08T23:31:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- un_multi
model-index:
- name: opus-mt-en-ar-finetuned-en-to-ar-test2-instances
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. -->
# opus-mt-en-ar-finetuned-en-to-ar-test2-instances
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the un_multi dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 1 | 0.8295 | 66.2993 | 37.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
nateraw/nu-wave-x2
|
nateraw
| 2022-06-08T23:03:59Z | 0 | 2 |
pytorch-lightning
|
[
"pytorch-lightning",
"audio-to-audio",
"en",
"dataset:vctk",
"arxiv:2104.02321",
"license:bsd-3-clause",
"region:us"
] |
audio-to-audio
| 2022-06-08T21:12:53Z |
---
language: en
license: bsd-3-clause
library_name: pytorch-lightning
tags:
- pytorch-lightning
- audio-to-audio
datasets: vctk
model_name: nu-wave-x2
---
# nu-wave-x2
## Model description
NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling
- [GitHub Repo](https://github.com/mindslab-ai/nuwave)
- [Paper](https://arxiv.org/pdf/2104.02321.pdf)
This model was trained by contributor [Frederico S. Oliveira](https://huggingface.co/freds0), who graciously [provided the checkpoint](https://github.com/mindslab-ai/nuwave/issues/18) in the original author's GitHub repo.
This model was trained using source code written by Junhyeok Lee and Seungu Han under the BSD 3.0 License. All credit goes to them for this work.
This model takes in audio at 24kHz and upsamples it to 48kHz.
## Intended uses & limitations
#### How to use
You can try out this model here: [](https://colab.research.google.com/gist/nateraw/bd78af284ef78a960e18a75cb13deab1/nu-wave-x2.ipynb)
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data.
## Training procedure
Preprocessing, hardware used, hyperparameters...
## Eval results
You can check out the authors' results at [their project page](https://mindslab-ai.github.io/nuwave/). The project page contains many samples of upsampled audio from the authors' models.
### BibTeX entry and citation info
```bibtex
@inproceedings{lee21nuwave,
author={Junhyeok Lee and Seungu Han},
title={{NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling}},
year=2021,
booktitle={Proc. Interspeech 2021},
pages={1634--1638},
doi={10.21437/Interspeech.2021-36}
}
```
|
huggingtweets/mephytis
|
huggingtweets
| 2022-06-08T22:50:52Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-08T22:50:21Z |
---
language: en
thumbnail: http://www.huggingtweets.com/mephytis/1654728647738/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/1516396570639573002/4WWU_e38_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">mephy✨</div>
<div style="text-align: center; font-size: 14px;">@mephytis</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 mephy✨.
| Data | mephy✨ |
| --- | --- |
| Tweets downloaded | 2959 |
| Retweets | 322 |
| Short tweets | 737 |
| Tweets kept | 1900 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/6sao13mv/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 @mephytis's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/29ayegfb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/29ayegfb/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/mephytis')
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)
|
hiranhsw/q-Taxi-v3
|
hiranhsw
| 2022-06-08T22:44:22Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-08T22:44:15Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="hiranhsw/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"])
```
|
carblacac/twitter-sentiment-analysis
|
carblacac
| 2022-06-08T22:40:26Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:new_dataset",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-08T17:48:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- new_dataset
metrics:
- accuracy
model-index:
- name: sentiment-analysis-twitter
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: new_dataset
type: new_dataset
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7965
---
<!-- 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. -->
# sentiment-analysis-twitter
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the new_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4579
- Accuracy: 0.7965
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5315 | 1.0 | 157 | 0.4517 | 0.788 |
| 0.388 | 2.0 | 314 | 0.4416 | 0.8 |
| 0.3307 | 3.0 | 471 | 0.4579 | 0.7965 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
|
andri/ppo-LunarLander-v2
|
andri
| 2022-06-08T22:22:29Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-08T11:50:01Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 263.23 +/- 15.11
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
...
```
|
Anjoe/kant-gpt2
|
Anjoe
| 2022-06-08T22:08:06Z | 157 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-07T18:51:18Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: kant-gpt2
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. -->
# kant-gpt2
This model is a fine-tuned version of [dbmdz/german-gpt2](https://huggingface.co/dbmdz/german-gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8022
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 22
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.3257 | 1.0 | 1825 | 3.2231 |
| 2.9885 | 2.0 | 3650 | 3.0069 |
| 2.7955 | 3.0 | 5475 | 2.8440 |
| 2.5748 | 4.0 | 7300 | 2.7059 |
| 2.3545 | 5.0 | 9125 | 2.5806 |
| 2.1759 | 6.0 | 10950 | 2.4618 |
| 1.9697 | 7.0 | 12775 | 2.3553 |
| 1.7778 | 8.0 | 14600 | 2.2517 |
| 1.6192 | 9.0 | 16425 | 2.1599 |
| 1.4675 | 10.0 | 18250 | 2.0895 |
| 1.3195 | 11.0 | 20075 | 2.0138 |
| 1.2012 | 12.0 | 21900 | 1.9602 |
| 1.0828 | 13.0 | 23725 | 1.9097 |
| 0.9926 | 14.0 | 25550 | 1.8720 |
| 0.9076 | 15.0 | 27375 | 1.8426 |
| 0.8336 | 16.0 | 29200 | 1.8214 |
| 0.7649 | 17.0 | 31025 | 1.8058 |
| 0.7208 | 18.0 | 32850 | 1.7980 |
| 0.6798 | 19.0 | 34675 | 1.7938 |
| 0.647 | 20.0 | 36500 | 1.7969 |
| 0.6226 | 21.0 | 38325 | 1.7975 |
| 0.601 | 22.0 | 40150 | 1.8022 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
hiranhsw/q-FrozenLake-v1-4x4
|
hiranhsw
| 2022-06-08T21:52:46Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-08T21:52:39Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4
results:
- metrics:
- type: mean_reward
value: 0.75 +/- 0.43
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="hiranhsw/q-FrozenLake-v1-4x4", 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/sun_soony-unjaded_jade-veganhollyg
|
huggingtweets
| 2022-06-08T21:45:56Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-30T21:50:31Z |
---
language: en
thumbnail: http://www.huggingtweets.com/sun_soony-unjaded_jade-veganhollyg/1654724750416/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/1105554414427885569/XkyfcoMJ_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/1290809762637131776/uwGH2mYu_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/900359049061036032/LYf3Ouv__400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Jade Bowler & soony & Holly Gabrielle</div>
<div style="text-align: center; font-size: 14px;">@sun_soony-unjaded_jade-veganhollyg</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 Jade Bowler & soony & Holly Gabrielle.
| Data | Jade Bowler | soony | Holly Gabrielle |
| --- | --- | --- | --- |
| Tweets downloaded | 3170 | 815 | 1802 |
| Retweets | 121 | 260 | 276 |
| Short tweets | 120 | 47 | 253 |
| Tweets kept | 2929 | 508 | 1273 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/afi2j4p2/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 @sun_soony-unjaded_jade-veganhollyg's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3uiqxuec) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3uiqxuec/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/sun_soony-unjaded_jade-veganhollyg')
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/neiltyson
|
huggingtweets
| 2022-06-08T21:26:48Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/neiltyson/1654723603504/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/74188698/NeilTysonOriginsA-Crop_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">Neil deGrasse Tyson</div>
<div style="text-align: center; font-size: 14px;">@neiltyson</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 Neil deGrasse Tyson.
| Data | Neil deGrasse Tyson |
| --- | --- |
| Tweets downloaded | 3234 |
| Retweets | 10 |
| Short tweets | 87 |
| Tweets kept | 3137 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1v949iob/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 @neiltyson's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/kjzq9tjy) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/kjzq9tjy/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/neiltyson')
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)
|
hiranhsw/q-FrozenLake-v1-8x8-noSlippery
|
hiranhsw
| 2022-06-08T21:20:14Z | 0 | 0 | null |
[
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-08T21:20:07Z |
---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="hiranhsw/q-FrozenLake-v1-8x8-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
huggingtweets/kentcdodds-richardbranson-sikiraamer
|
huggingtweets
| 2022-06-08T21:08:46Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-08T21:04:40Z |
---
language: en
thumbnail: http://www.huggingtweets.com/kentcdodds-richardbranson-sikiraamer/1654722520391/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/1496777835062648833/3Ao6Xb2a_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/1529905780542959616/Ibwrp7VJ_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/1410740591483293697/tRbW1XoV_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Amer Sikira & Kent C. Dodds 💿 & Richard Branson</div>
<div style="text-align: center; font-size: 14px;">@kentcdodds-richardbranson-sikiraamer</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 Amer Sikira & Kent C. Dodds 💿 & Richard Branson.
| Data | Amer Sikira | Kent C. Dodds 💿 | Richard Branson |
| --- | --- | --- | --- |
| Tweets downloaded | 3250 | 3249 | 3215 |
| Retweets | 94 | 578 | 234 |
| Short tweets | 214 | 507 | 96 |
| Tweets kept | 2942 | 2164 | 2885 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/jtwa65l2/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 @kentcdodds-richardbranson-sikiraamer's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3vt6qlgf) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3vt6qlgf/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/kentcdodds-richardbranson-sikiraamer')
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)
|
Sohaibsyed/wav2vec2-large-xls-r-300m-turkish-colab
|
Sohaibsyed
| 2022-06-08T20:48:31Z | 5 | 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
| 2022-06-08T16:53:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3717
- Wer: 0.2972
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.0139 | 3.67 | 400 | 0.7020 | 0.7112 |
| 0.4129 | 7.34 | 800 | 0.4162 | 0.4503 |
| 0.1869 | 11.01 | 1200 | 0.4174 | 0.3959 |
| 0.1273 | 14.68 | 1600 | 0.4020 | 0.3695 |
| 0.0959 | 18.35 | 2000 | 0.4026 | 0.3545 |
| 0.0771 | 22.02 | 2400 | 0.3904 | 0.3361 |
| 0.0614 | 25.69 | 2800 | 0.3736 | 0.3127 |
| 0.0486 | 29.36 | 3200 | 0.3717 | 0.2972 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
valurank/distilroberta-news-small
|
valurank
| 2022-06-08T20:45:50Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:valurank/news-small",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: other
language: en
datasets:
- valurank/news-small
---
# DistilROBERTA fine-tuned for news classification
This model is based on [distilroberta-base](https://huggingface.co/distilroberta-base) pretrained weights, with a classification head fine-tuned to classify news articles into 3 categories (bad, medium, good).
## Training data
The dataset used to fine-tune the model is [news-small](https://huggingface.co/datasets/valurank/news-small), the 300 article news dataset manually annotated by Alex.
## Inputs
Similar to its base model, this model accepts inputs with a maximum length of 512 tokens.
|
valurank/distilroberta-propaganda-2class
|
valurank
| 2022-06-08T20:39:15Z | 11 | 3 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: other
tags:
- generated_from_trainer
model-index:
- name: distilroberta-propaganda-2class
results: []
---
# distilroberta-propaganda-2class
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the QCRI propaganda dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5087
- Acc: 0.7424
## Training and evaluation data
Training data is the 19-class QCRI propaganda data, with all propaganda classes collapsed to a single catch-all 'prop' class.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 12345
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5737 | 1.0 | 493 | 0.5998 | 0.6515 |
| 0.4954 | 2.0 | 986 | 0.5530 | 0.7080 |
| 0.4774 | 3.0 | 1479 | 0.5331 | 0.7258 |
| 0.4846 | 4.0 | 1972 | 0.5247 | 0.7339 |
| 0.4749 | 5.0 | 2465 | 0.5392 | 0.7199 |
| 0.502 | 6.0 | 2958 | 0.5124 | 0.7466 |
| 0.457 | 7.0 | 3451 | 0.5167 | 0.7432 |
| 0.4899 | 8.0 | 3944 | 0.5160 | 0.7428 |
| 0.4833 | 9.0 | 4437 | 0.5280 | 0.7339 |
| 0.5114 | 10.0 | 4930 | 0.5112 | 0.7436 |
| 0.4419 | 11.0 | 5423 | 0.5060 | 0.7525 |
| 0.4743 | 12.0 | 5916 | 0.5031 | 0.7547 |
| 0.4597 | 13.0 | 6409 | 0.5043 | 0.7517 |
| 0.4861 | 14.0 | 6902 | 0.5055 | 0.7487 |
| 0.499 | 15.0 | 7395 | 0.5091 | 0.7419 |
| 0.501 | 16.0 | 7888 | 0.5037 | 0.7521 |
| 0.4659 | 17.0 | 8381 | 0.5087 | 0.7424 |
### Framework versions
- Transformers 4.11.2
- Pytorch 1.7.1
- Datasets 1.11.0
- Tokenizers 0.10.3
|
valurank/distilroberta-proppy
|
valurank
| 2022-06-08T20:38:27Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: other
tags:
- generated_from_trainer
model-index:
- name: distilroberta-proppy
results: []
---
# distilroberta-proppy
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the proppy corpus.
It achieves the following results on the evaluation set:
- Loss: 0.1838
- Acc: 0.9269
## Training and evaluation data
The training data is the [proppy corpus](https://zenodo.org/record/3271522). See [Proppy: Organizing the News
Based on Their Propagandistic Content](https://propaganda.qcri.org/papers/elsarticle-template.pdf) for details.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 12345
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.3179 | 1.0 | 732 | 0.2032 | 0.9146 |
| 0.2933 | 2.0 | 1464 | 0.2026 | 0.9206 |
| 0.2938 | 3.0 | 2196 | 0.1849 | 0.9252 |
| 0.3429 | 4.0 | 2928 | 0.1983 | 0.9221 |
| 0.2608 | 5.0 | 3660 | 0.2310 | 0.9106 |
| 0.2562 | 6.0 | 4392 | 0.1826 | 0.9270 |
| 0.2785 | 7.0 | 5124 | 0.1954 | 0.9228 |
| 0.307 | 8.0 | 5856 | 0.2056 | 0.9200 |
| 0.28 | 9.0 | 6588 | 0.1843 | 0.9259 |
| 0.2794 | 10.0 | 7320 | 0.1782 | 0.9299 |
| 0.2868 | 11.0 | 8052 | 0.1907 | 0.9242 |
| 0.2789 | 12.0 | 8784 | 0.2031 | 0.9216 |
| 0.2827 | 13.0 | 9516 | 0.1976 | 0.9229 |
| 0.2795 | 14.0 | 10248 | 0.1866 | 0.9255 |
| 0.2895 | 15.0 | 10980 | 0.1838 | 0.9269 |
### Framework versions
- Transformers 4.11.2
- Pytorch 1.7.1
- Datasets 1.11.0
- Tokenizers 0.10.3
|
valurank/distilroberta-mbfc-bias
|
valurank
| 2022-06-08T20:34:29Z | 9 | 2 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: other
tags:
- generated_from_trainer
model-index:
- name: distilroberta-mbfc-bias
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. -->
# distilroberta-mbfc-bias
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the Proppy dataset, using political bias from mediabiasfactcheck.com as labels.
It achieves the following results on the evaluation set:
- Loss: 1.4130
- Acc: 0.6348
## Training and evaluation data
The training data used is the [proppy corpus](https://zenodo.org/record/3271522). Articles are labeled for political bias using the political bias of the source publication, as scored by mediabiasfactcheck.com. See [Proppy: Organizing the News Based on Their Propagandistic Content](https://propaganda.qcri.org/papers/elsarticle-template.pdf) for details.
To create a more balanced training set, common labels are downsampled to have a maximum of 2000 articles. The resulting label distribution in the training data is as follows:
```
extremeright 689
leastbiased 2000
left 783
leftcenter 2000
right 1260
rightcenter 1418
unknown 2000
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 12345
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9493 | 1.0 | 514 | 1.2765 | 0.4730 |
| 0.7376 | 2.0 | 1028 | 1.0003 | 0.5812 |
| 0.6702 | 3.0 | 1542 | 1.1294 | 0.5631 |
| 0.6161 | 4.0 | 2056 | 1.0439 | 0.6058 |
| 0.4934 | 5.0 | 2570 | 1.1196 | 0.6028 |
| 0.4558 | 6.0 | 3084 | 1.0993 | 0.5977 |
| 0.4717 | 7.0 | 3598 | 1.0308 | 0.6373 |
| 0.3961 | 8.0 | 4112 | 1.1291 | 0.6234 |
| 0.3829 | 9.0 | 4626 | 1.1554 | 0.6316 |
| 0.3442 | 10.0 | 5140 | 1.1548 | 0.6465 |
| 0.2505 | 11.0 | 5654 | 1.3605 | 0.6169 |
| 0.2105 | 12.0 | 6168 | 1.3310 | 0.6297 |
| 0.262 | 13.0 | 6682 | 1.2706 | 0.6383 |
| 0.2031 | 14.0 | 7196 | 1.3658 | 0.6378 |
| 0.2021 | 15.0 | 7710 | 1.4130 | 0.6348 |
### Framework versions
- Transformers 4.11.2
- Pytorch 1.7.1
- Datasets 1.11.0
- Tokenizers 0.10.3
|
valurank/distilroberta-offensive
|
valurank
| 2022-06-08T20:31:46Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: other
tags:
- generated_from_trainer
model-index:
- name: distilroberta-offensive
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. -->
# distilroberta-offensive
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4526
- Acc: 0.8975
## 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: 12345
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2321 | 1.0 | 1030 | 0.2404 | 0.9044 |
| 0.2539 | 2.0 | 2060 | 0.2139 | 0.9098 |
| 0.1997 | 3.0 | 3090 | 0.2561 | 0.9090 |
| 0.1663 | 4.0 | 4120 | 0.2409 | 0.9030 |
| 0.1515 | 5.0 | 5150 | 0.3000 | 0.9055 |
| 0.1035 | 6.0 | 6180 | 0.4170 | 0.9027 |
| 0.0466 | 7.0 | 7210 | 0.4526 | 0.8975 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
|
valurank/distilroberta-hatespeech
|
valurank
| 2022-06-08T20:30:04Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: other
tags:
- generated_from_trainer
model-index:
- name: distilroberta-hatespeech
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. -->
# distilroberta-hatespeech
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3619
- Acc: 0.8423
## 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: 12345
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.3096 | 1.0 | 4021 | 0.3375 | 0.8540 |
| 0.3711 | 2.0 | 8042 | 0.3305 | 0.8574 |
| 0.322 | 3.0 | 12063 | 0.3398 | 0.8534 |
| 0.3197 | 4.0 | 16084 | 0.3444 | 0.8504 |
| 0.3332 | 5.0 | 20105 | 0.3619 | 0.8423 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
|
valurank/MiniLM-L6-Keyword-Extraction
|
valurank
| 2022-06-08T20:17:38Z | 11,026 | 13 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:other",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-05-20T16:37:59Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
license: other
---
# all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
# Normalize embeddings
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L6-v2)
------
## Background
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a
1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developped this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developped this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
## Intended uses
Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures
the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
By default, input text longer than 256 word pieces is truncated.
## Training procedure
### Pre-training
We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
### Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
#### Hyper parameters
We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
#### Training data
We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
| Dataset | Paper | Number of training tuples |
|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 |
| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| **Total** | | **1,170,060,424** |
|
jungealexander/distilbert-base-uncased-finetuned-go_emotions_20220608_1
|
jungealexander
| 2022-06-08T20:14:00Z | 14 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:go_emotions",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-08T18:30:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- go_emotions
metrics:
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-go_emotions_20220608_1
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: go_emotions
type: go_emotions
args: simplified
metrics:
- name: F1
type: f1
value: 0.5575026333429091
- name: Accuracy
type: accuracy
value: 0.43641725027644673
---
<!-- 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-go_emotions_20220608_1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the go_emotions dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0857
- F1: 0.5575
- Roc Auc: 0.7242
- Accuracy: 0.4364
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.173 | 1.0 | 679 | 0.1074 | 0.4245 | 0.6455 | 0.2976 |
| 0.0989 | 2.0 | 1358 | 0.0903 | 0.5199 | 0.6974 | 0.3972 |
| 0.0865 | 3.0 | 2037 | 0.0868 | 0.5504 | 0.7180 | 0.4263 |
| 0.0806 | 4.0 | 2716 | 0.0860 | 0.5472 | 0.7160 | 0.4233 |
| 0.0771 | 5.0 | 3395 | 0.0857 | 0.5575 | 0.7242 | 0.4364 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
kalmufti/q-Taxi-v3
|
kalmufti
| 2022-06-08T19:29:47Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-08T19:29:39Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.50 +/- 2.67
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="kalmufti/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/makimasdoggy
|
huggingtweets
| 2022-06-08T19:17:06Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-08T19:15:48Z |
---
language: en
thumbnail: http://www.huggingtweets.com/makimasdoggy/1654715821978/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/1534537330014445569/ql3I-npY_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">Vanser</div>
<div style="text-align: center; font-size: 14px;">@makimasdoggy</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 Vanser.
| Data | Vanser |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 1548 |
| Short tweets | 346 |
| Tweets kept | 1355 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/66wk3fyw/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 @makimasdoggy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2di8hgps) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2di8hgps/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/makimasdoggy')
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)
|
skyfox/dqn-SpaceInvadersNoFrameskip-v4
|
skyfox
| 2022-06-08T18:47:18Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-08T17:15:22Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 767.00 +/- 378.16
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 skyfox -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 skyfox
```
## 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),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
ksabeh/roberta-base-attribute-correction-mlm
|
ksabeh
| 2022-06-08T17:55:09Z | 8 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-08T09:38:06Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: ksabeh/roberta-base-mlm-electronics-attrs-correction
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. -->
# ksabeh/roberta-base-mlm-electronics-attrs-correction
This model is a fine-tuned version of [ksabeh/roberta-base-mlm-electronics](https://huggingface.co/ksabeh/roberta-base-mlm-electronics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1009
- Validation Loss: 0.0936
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36848, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1915 | 0.1100 | 0 |
| 0.1009 | 0.0936 | 1 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
vincentbonnet/q-Taxi-v3
|
vincentbonnet
| 2022-06-08T17:36:29Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-28T03:19:00Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: -99.00 +/- 0.00
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="vincentbonnet/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/ripvillage
|
huggingtweets
| 2022-06-08T16:38:52Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-08T16:35:39Z |
---
language: en
thumbnail: http://www.huggingtweets.com/ripvillage/1654706327179/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/378800000120011180/ffb093c084cfb4b60f70488a7e6355d0_400x400.jpeg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Mathurin Village</div>
<div style="text-align: center; font-size: 14px;">@ripvillage</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 Mathurin Village.
| Data | Mathurin Village |
| --- | --- |
| Tweets downloaded | 3243 |
| Retweets | 118 |
| Short tweets | 335 |
| Tweets kept | 2790 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3e20ev2s/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 @ripvillage's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ecq32lhi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ecq32lhi/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/ripvillage')
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)
|
mmillet/distilrubert-tiny-cased-conversational-v1_finetuned_emotion_experiment_augmented_anger_fear
|
mmillet
| 2022-06-08T16:10:06Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-08T16:03:02Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilrubert-tiny-cased-conversational-v1_finetuned_emotion_experiment_augmented_anger_fear
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilrubert-tiny-cased-conversational-v1_finetuned_emotion_experiment_augmented_anger_fear
This model is a fine-tuned version of [DeepPavlov/distilrubert-tiny-cased-conversational-v1](https://huggingface.co/DeepPavlov/distilrubert-tiny-cased-conversational-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3760
- Accuracy: 0.8758
- F1: 0.8750
- Precision: 0.8753
- Recall: 0.8758
## 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=0.0001
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.2636 | 1.0 | 69 | 1.0914 | 0.6013 | 0.5599 | 0.5780 | 0.6013 |
| 1.029 | 2.0 | 138 | 0.9180 | 0.6514 | 0.6344 | 0.6356 | 0.6514 |
| 0.904 | 3.0 | 207 | 0.8235 | 0.6827 | 0.6588 | 0.6904 | 0.6827 |
| 0.8084 | 4.0 | 276 | 0.7272 | 0.7537 | 0.7477 | 0.7564 | 0.7537 |
| 0.7242 | 5.0 | 345 | 0.6435 | 0.7860 | 0.7841 | 0.7861 | 0.7860 |
| 0.6305 | 6.0 | 414 | 0.5543 | 0.8173 | 0.8156 | 0.8200 | 0.8173 |
| 0.562 | 7.0 | 483 | 0.4860 | 0.8392 | 0.8383 | 0.8411 | 0.8392 |
| 0.5042 | 8.0 | 552 | 0.4474 | 0.8528 | 0.8514 | 0.8546 | 0.8528 |
| 0.4535 | 9.0 | 621 | 0.4213 | 0.8580 | 0.8579 | 0.8590 | 0.8580 |
| 0.4338 | 10.0 | 690 | 0.4106 | 0.8591 | 0.8578 | 0.8605 | 0.8591 |
| 0.4026 | 11.0 | 759 | 0.4064 | 0.8622 | 0.8615 | 0.8632 | 0.8622 |
| 0.3861 | 12.0 | 828 | 0.3874 | 0.8737 | 0.8728 | 0.8733 | 0.8737 |
| 0.3709 | 13.0 | 897 | 0.3841 | 0.8706 | 0.8696 | 0.8701 | 0.8706 |
| 0.3592 | 14.0 | 966 | 0.3841 | 0.8716 | 0.8709 | 0.8714 | 0.8716 |
| 0.3475 | 15.0 | 1035 | 0.3834 | 0.8737 | 0.8728 | 0.8732 | 0.8737 |
| 0.3537 | 16.0 | 1104 | 0.3805 | 0.8727 | 0.8717 | 0.8722 | 0.8727 |
| 0.3317 | 17.0 | 1173 | 0.3775 | 0.8747 | 0.8739 | 0.8741 | 0.8747 |
| 0.323 | 18.0 | 1242 | 0.3759 | 0.8727 | 0.8718 | 0.8721 | 0.8727 |
| 0.3327 | 19.0 | 1311 | 0.3776 | 0.8758 | 0.8750 | 0.8756 | 0.8758 |
| 0.3339 | 20.0 | 1380 | 0.3760 | 0.8758 | 0.8750 | 0.8753 | 0.8758 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
juancavallotti/t5-base-gec
|
juancavallotti
| 2022-06-08T15:26:04Z | 21 | 4 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"onnx",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-05T12:00:40Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-base-gec
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-gec
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 5
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
nicofloresuribe/ndad
|
nicofloresuribe
| 2022-06-08T15:24:15Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2022-06-08T15:24:15Z |
---
license: bigscience-bloom-rail-1.0
---
|
twieland/VN_ja-en_byt5_small
|
twieland
| 2022-06-08T14:53:19Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-08T01:50:21Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: VN_ja-en_byt5_small
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# VN_ja-en_byt5_small
This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0552
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.1687 | 0.1 | 2000 | 1.1805 |
| 0.9685 | 0.19 | 4000 | 1.1384 |
| 0.8989 | 0.29 | 6000 | 1.1207 |
| 0.8583 | 0.39 | 8000 | 1.1046 |
| 0.833 | 0.49 | 10000 | 1.1290 |
| 0.8102 | 0.58 | 12000 | 1.1225 |
| 0.7932 | 0.68 | 14000 | 1.0956 |
| 0.7776 | 0.78 | 16000 | 1.0970 |
| 0.762 | 0.88 | 18000 | 1.0992 |
| 0.7522 | 0.97 | 20000 | 1.0760 |
| 0.7318 | 1.07 | 22000 | 1.0579 |
| 0.7197 | 1.17 | 24000 | 1.0780 |
| 0.7142 | 1.27 | 26000 | 1.0748 |
| 0.7093 | 1.36 | 28000 | 1.0781 |
| 0.7005 | 1.46 | 30000 | 1.0756 |
| 0.6938 | 1.56 | 32000 | 1.0702 |
| 0.6896 | 1.65 | 34000 | 1.0563 |
| 0.6846 | 1.75 | 36000 | 1.0603 |
| 0.6807 | 1.85 | 38000 | 1.0626 |
| 0.6766 | 1.95 | 40000 | 1.0666 |
| 0.6649 | 2.04 | 42000 | 1.0694 |
| 0.6532 | 2.14 | 44000 | 1.0564 |
| 0.6501 | 2.24 | 46000 | 1.0715 |
| 0.6476 | 2.34 | 48000 | 1.0551 |
| 0.646 | 2.43 | 50000 | 1.0601 |
| 0.6445 | 2.53 | 52000 | 1.0595 |
| 0.6404 | 2.63 | 54000 | 1.0494 |
| 0.6378 | 2.72 | 56000 | 1.0584 |
| 0.636 | 2.82 | 58000 | 1.0531 |
| 0.6345 | 2.92 | 60000 | 1.0552 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
anas-awadalla/spanbert-large-cased-compacter-squad
|
anas-awadalla
| 2022-06-08T14:52:47Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"region:us"
] | null | 2022-06-07T23:31:21Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-large-cased-compacter-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. -->
# spanbert-large-cased-compacter-squad
This model is a fine-tuned version of [SpanBERT/spanbert-large-cased](https://huggingface.co/SpanBERT/spanbert-large-cased) on the squad 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: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15.0
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
awalmeida/dqn-SpaceInvadersNoFrameskip-v4
|
awalmeida
| 2022-06-08T14:52:40Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-08T14:52:01Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 657.00 +/- 102.03
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 awalmeida -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 awalmeida
```
## 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)])
```
|
cutten/wav2vec2-large-multilang-cv-ru
|
cutten
| 2022-06-08T14:14:41Z | 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
| 2022-06-08T11:35:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-multilang-cv-ru
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-multilang-cv-ru
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9734
- Wer: 0.7037
## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 6.0328 | 0.79 | 500 | 3.0713 | 1.0 |
| 1.9426 | 1.58 | 1000 | 1.2048 | 0.9963 |
| 1.1285 | 2.37 | 1500 | 0.9825 | 0.9282 |
| 0.9462 | 3.15 | 2000 | 0.8836 | 0.8965 |
| 0.8274 | 3.94 | 2500 | 0.8134 | 0.8661 |
| 0.7106 | 4.73 | 3000 | 0.8033 | 0.8387 |
| 0.6545 | 5.52 | 3500 | 0.8309 | 0.8366 |
| 0.6013 | 6.31 | 4000 | 0.7667 | 0.8240 |
| 0.5599 | 7.1 | 4500 | 0.7740 | 0.8160 |
| 0.5027 | 7.89 | 5000 | 0.7796 | 0.8188 |
| 0.4588 | 8.68 | 5500 | 0.8204 | 0.7968 |
| 0.4448 | 9.46 | 6000 | 0.8277 | 0.7738 |
| 0.4122 | 10.25 | 6500 | 0.8292 | 0.7776 |
| 0.3816 | 11.04 | 7000 | 0.8548 | 0.7907 |
| 0.3587 | 11.83 | 7500 | 0.8245 | 0.7805 |
| 0.3374 | 12.62 | 8000 | 0.8371 | 0.7701 |
| 0.3214 | 13.41 | 8500 | 0.8311 | 0.7822 |
| 0.3072 | 14.2 | 9000 | 0.8940 | 0.7674 |
| 0.2929 | 14.98 | 9500 | 0.8788 | 0.7604 |
| 0.257 | 15.77 | 10000 | 0.8911 | 0.7633 |
| 0.2592 | 16.56 | 10500 | 0.8673 | 0.7604 |
| 0.2392 | 17.35 | 11000 | 0.9582 | 0.7810 |
| 0.232 | 18.14 | 11500 | 0.9340 | 0.7423 |
| 0.2252 | 18.93 | 12000 | 0.8874 | 0.7320 |
| 0.2079 | 19.72 | 12500 | 0.9436 | 0.7483 |
| 0.2003 | 20.5 | 13000 | 0.9573 | 0.7638 |
| 0.194 | 21.29 | 13500 | 0.9361 | 0.7308 |
| 0.188 | 22.08 | 14000 | 0.9704 | 0.7221 |
| 0.1754 | 22.87 | 14500 | 0.9668 | 0.7265 |
| 0.1688 | 23.66 | 15000 | 0.9680 | 0.7246 |
| 0.162 | 24.45 | 15500 | 0.9443 | 0.7066 |
| 0.1617 | 25.24 | 16000 | 0.9664 | 0.7265 |
| 0.1504 | 26.03 | 16500 | 0.9505 | 0.7189 |
| 0.1425 | 26.81 | 17000 | 0.9536 | 0.7112 |
| 0.134 | 27.6 | 17500 | 0.9674 | 0.7047 |
| 0.1301 | 28.39 | 18000 | 0.9852 | 0.7066 |
| 0.1314 | 29.18 | 18500 | 0.9766 | 0.7073 |
| 0.1219 | 29.97 | 19000 | 0.9734 | 0.7037 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
clhuang/albert-news-classification
|
clhuang
| 2022-06-08T14:09:17Z | 15 | 2 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"classification",
"tw",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-07T08:13:42Z |
---
language:
- tw
tags:
- albert
- classification
license: afl-3.0
metrics:
- Accuracy
---
# Traditional Chinese news classification
繁體中文新聞分類任務，使用ckiplab/albert-base-chinese預訓練模型，資料集只有2.6萬筆，做為課程的範例模型。
from transformers import BertTokenizer, AlbertForSequenceClassification
model_path = "clhuang/albert-news-classification"
model = AlbertForSequenceClassification.from_pretrained(model_path)
tokenizer = BertTokenizer.from_pretrained("bert-base-chinese")
# Category index
news_categories=['政治','科技','運動','證卷','產經','娛樂','生活','國際','社會','文化','兩岸']
idx2cate = { i : item for i, item in enumerate(news_categories)}
# get category probability
def get_category_proba( text ):
max_length = 250
# prepare token sequence
inputs = tokenizer([text], padding=True, truncation=True, max_length=max_length, return_tensors="pt")
# perform inference
outputs = model(**inputs)
# get output probabilities by doing softmax
probs = outputs[0].softmax(1)
# executing argmax function to get the candidate label index
label_index = probs.argmax(dim=1)[0].tolist() # convert tensor to int
# get the label name
label = idx2cate[ label_index ]
# get the label probability
proba = round(float(probs.tolist()[0][label_index]),2)
response = {'label': label, 'proba': proba}
return response
get_category_proba('俄羅斯2月24日入侵烏克蘭至今不到3個月，芬蘭已準備好扭轉奉行了75年的軍事不結盟政策，申請加入北約。芬蘭總理馬林昨天表示，「希望我們下星期能與瑞典一起提出申請」。')
{'label': '國際', 'proba': 0.99}
|
fusing/ddim-lsun-church
|
fusing
| 2022-06-08T13:10:39Z | 2 | 0 |
transformers
|
[
"transformers",
"ddim_diffusion",
"arxiv:2010.02502",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T12:43:01Z |
---
tags:
- ddim_diffusion
---
# Denoising Diffusion Implicit Models (DDIM)
**Paper**: [Denoising Diffusion Implicit Models](https://arxiv.org/abs/2010.02502)
**Abstract**:
*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process. We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose reverse process can be much faster to sample from. We empirically demonstrate that DDIMs can produce high quality samples 10× to 50× faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space.*
**Explanation on `eta` and `num_inference_steps`**
- `num_inference_steps` is called *S* in the following table
- `eta` is called *η* in the following table
## Usage
```python
# !pip install diffusers
from diffusers import DiffusionPipeline
import PIL.Image
import numpy as np
model_id = "fusing/ddim-lsun-church"
# load model and scheduler
ddpm = DiffusionPipeline.from_pretrained(model_id)
# run pipeline in inference (sample random noise and denoise)
image = ddpm(eta=0.0, num_inference_steps=50)
# process image to PIL
image_processed = image.cpu().permute(0, 2, 3, 1)
image_processed = (image_processed + 1.0) * 127.5
image_processed = image_processed.numpy().astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])
# save image
image_pil.save("test.png")
```
## Samples
1. 
2. 
3. 
4. 
|
fusing/ddim-lsun-bedroom
|
fusing
| 2022-06-08T13:10:21Z | 41 | 0 |
transformers
|
[
"transformers",
"ddim_diffusion",
"arxiv:2010.02502",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T12:42:50Z |
---
tags:
- ddim_diffusion
---
# Denoising Diffusion Implicit Models (DDIM)
**Paper**: [Denoising Diffusion Implicit Models](https://arxiv.org/abs/2010.02502)
**Abstract**:
*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process. We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose reverse process can be much faster to sample from. We empirically demonstrate that DDIMs can produce high quality samples 10× to 50× faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space.*
**Explanation on `eta` and `num_inference_steps`**
- `num_inference_steps` is called *S* in the following table
- `eta` is called *η* in the following table
## Usage
```python
# !pip install diffusers
from diffusers import DiffusionPipeline
import PIL.Image
import numpy as np
model_id = "fusing/ddim-lsun-bedroom"
# load model and scheduler
ddpm = DiffusionPipeline.from_pretrained(model_id)
# run pipeline in inference (sample random noise and denoise)
image = ddpm(eta=0.0, num_inference_steps=50)
# process image to PIL
image_processed = image.cpu().permute(0, 2, 3, 1)
image_processed = (image_processed + 1.0) * 127.5
image_processed = image_processed.numpy().astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])
# save image
image_pil.save("test.png")
```
## Samples
1. 
2. 
3. 
4. 
|
jcmc/q-FrozenLake-v1-4x4-noSlippery
|
jcmc
| 2022-06-08T12:41:26Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-08T12:41:19Z |
---
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="jcmc/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"])
```
|
FabianWillner/distilbert-base-uncased-finetuned-triviaqa
|
FabianWillner
| 2022-06-08T12:22:36Z | 43 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-10T12:20:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-triviaqa
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-triviaqa
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9949
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0391 | 1.0 | 11195 | 1.0133 |
| 0.8425 | 2.0 | 22390 | 0.9949 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
anas-awadalla/spanbert-large-cased-lora-squad
|
anas-awadalla
| 2022-06-08T12:06:33Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"region:us"
] | null | 2022-06-08T09:27:27Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-large-cased-lora-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. -->
# spanbert-large-cased-lora-squad
This model is a fine-tuned version of [SpanBERT/spanbert-large-cased](https://huggingface.co/SpanBERT/spanbert-large-cased) on the squad 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: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15.0
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
zdreiosis/ff_analysis_3
|
zdreiosis
| 2022-06-08T10:48:09Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"6th",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-08T09:49:46Z |
---
license: apache-2.0
tags:
- 6th
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: ff_analysis_3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ff_analysis_3
This model is a fine-tuned version of [zdreiosis/ff_analysis_2](https://huggingface.co/zdreiosis/ff_analysis_2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0060
- 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 | 1.02 | 50 | 0.0138 | 1.0 | 1.0 | 1.0 |
| No log | 2.04 | 100 | 0.0132 | 0.9966 | 0.9966 | 0.9885 |
| No log | 3.06 | 150 | 0.0097 | 1.0 | 1.0 | 1.0 |
| No log | 4.08 | 200 | 0.0095 | 0.9966 | 0.9966 | 0.9885 |
| No log | 5.1 | 250 | 0.0096 | 1.0 | 1.0 | 1.0 |
| No log | 6.12 | 300 | 0.0079 | 1.0 | 1.0 | 1.0 |
| No log | 7.14 | 350 | 0.0070 | 1.0 | 1.0 | 1.0 |
| No log | 8.16 | 400 | 0.0069 | 1.0 | 1.0 | 1.0 |
| No log | 9.18 | 450 | 0.0065 | 1.0 | 1.0 | 1.0 |
| 0.012 | 10.2 | 500 | 0.0060 | 1.0 | 1.0 | 1.0 |
| 0.012 | 11.22 | 550 | 0.0060 | 0.9966 | 0.9966 | 0.9885 |
| 0.012 | 12.24 | 600 | 0.0054 | 1.0 | 1.0 | 1.0 |
| 0.012 | 13.27 | 650 | 0.0049 | 1.0 | 1.0 | 1.0 |
| 0.012 | 14.29 | 700 | 0.0048 | 1.0 | 1.0 | 1.0 |
| 0.012 | 15.31 | 750 | 0.0046 | 1.0 | 1.0 | 1.0 |
| 0.012 | 16.33 | 800 | 0.0042 | 1.0 | 1.0 | 1.0 |
| 0.012 | 17.35 | 850 | 0.0042 | 1.0 | 1.0 | 1.0 |
| 0.012 | 18.37 | 900 | 0.0040 | 1.0 | 1.0 | 1.0 |
| 0.012 | 19.39 | 950 | 0.0040 | 1.0 | 1.0 | 1.0 |
| 0.0046 | 20.41 | 1000 | 0.0038 | 1.0 | 1.0 | 1.0 |
| 0.0046 | 21.43 | 1050 | 0.0037 | 1.0 | 1.0 | 1.0 |
| 0.0046 | 22.45 | 1100 | 0.0039 | 1.0 | 1.0 | 1.0 |
| 0.0046 | 23.47 | 1150 | 0.0038 | 1.0 | 1.0 | 1.0 |
| 0.0046 | 24.49 | 1200 | 0.0035 | 1.0 | 1.0 | 1.0 |
| 0.0046 | 25.51 | 1250 | 0.0037 | 1.0 | 1.0 | 1.0 |
| 0.0046 | 26.53 | 1300 | 0.0034 | 1.0 | 1.0 | 1.0 |
| 0.0046 | 27.55 | 1350 | 0.0035 | 1.0 | 1.0 | 1.0 |
| 0.0046 | 28.57 | 1400 | 0.0034 | 1.0 | 1.0 | 1.0 |
| 0.0046 | 29.59 | 1450 | 0.0035 | 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
|
epsil/dqn-BreakoutNoFrameskip-v4
|
epsil
| 2022-06-08T10:32:50Z | 7 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"BreakoutNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-08T10:32:02Z |
---
library_name: stable-baselines3
tags:
- BreakoutNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 57.90 +/- 21.41
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BreakoutNoFrameskip-v4
type: BreakoutNoFrameskip-v4
---
# **DQN** Agent playing **BreakoutNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **BreakoutNoFrameskip-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 BreakoutNoFrameskip-v4 -orga epsil -f logs/
python enjoy.py --algo dqn --env BreakoutNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env BreakoutNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env BreakoutNoFrameskip-v4 -f logs/ -orga epsil
```
## 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)])
```
|
pinku/q-FrozenLake-v1-4x4-noSlippery
|
pinku
| 2022-06-08T08:52:51Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-08T08:52:44Z |
---
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="pinku/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"])
```
|
anas-awadalla/roberta-large-lora-squad
|
anas-awadalla
| 2022-06-08T08:22:54Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"region:us"
] | null | 2022-06-08T05:45:46Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-lora-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. -->
# roberta-large-lora-squad
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the squad 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: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15.0
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Kieranm/britishmus_plate_material_classifier
|
Kieranm
| 2022-06-08T06:02:57Z | 0 | 0 |
fastai
|
[
"fastai",
"image-classification",
"region:us"
] |
image-classification
| 2022-06-08T05:01:12Z |
---
tags:
- fastai
- image-classification
---
# Model card
## Model description
A model trained to classify the material of European plates, as found in the British Museum collection. Initial model was trained using basic fastai workflow with timm integration.
## Intended uses & limitations
Should be able to predict the Material used (as defined by the British Museum) if that material was either porcelain,porcelain and gold, or earthenware with ~80% accuracy.
## Training and evaluation data
Was trained on images from the British Museum site, see link below
Examples to test can be found at: https://www.britishmuseum.org/collection/search?keyword=plate&object=plate&place=Europe&image=true&dateFrom=1700&eraFrom=ad&view=grid&sort=object_name__asc&page=1
Architecture: "vit_base_patch16_224_in21k"
|
enoriega/rule_learning_margin_test
|
enoriega
| 2022-06-08T05:00:59Z | 32 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"dataset:enoriega/odinsynth_dataset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-07T16:17:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- enoriega/odinsynth_dataset
model-index:
- name: rule_learning_margin_test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rule_learning_margin_test
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the enoriega/odinsynth_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4104
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6468 | 0.32 | 20 | 0.6191 |
| 0.5185 | 0.64 | 40 | 0.5083 |
| 0.459 | 0.96 | 60 | 0.4521 |
| 0.4352 | 1.29 | 80 | 0.4192 |
| 0.4427 | 1.61 | 100 | 0.4199 |
| 0.4246 | 1.93 | 120 | 0.4131 |
| 0.4301 | 2.26 | 140 | 0.4104 |
| 0.428 | 2.58 | 160 | 0.4099 |
| 0.4161 | 2.9 | 180 | 0.4102 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
ayush1701/my-deberta
|
ayush1701
| 2022-06-08T04:49:14Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"deberta",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-06-08T04:49:00Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: my-deberta
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. -->
# my-deberta
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
anas-awadalla/bert-large-uncased-lora-squad
|
anas-awadalla
| 2022-06-08T04:45:36Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"region:us"
] | null | 2022-06-08T02:07:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-large-uncased-lora-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. -->
# bert-large-uncased-lora-squad
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the squad 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: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15.0
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
huggingtweets/vufewequ
|
huggingtweets
| 2022-06-08T03:59:36Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-08T03:59:29Z |
---
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/1350929535454359558/lWAfxbn4_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">Vu Fewequ</div>
<div style="text-align: center; font-size: 14px;">@vufewequ</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 Vu Fewequ.
| Data | Vu Fewequ |
| --- | --- |
| Tweets downloaded | 175 |
| Retweets | 60 |
| Short tweets | 5 |
| Tweets kept | 110 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3d6nz5jt/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 @vufewequ's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1psyqthq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1psyqthq/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/vufewequ')
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)
|
cammy/wa2vec2-5epochs
|
cammy
| 2022-06-08T03:41:41Z | 33 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-08T02:23:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wa2vec2-5epochs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wa2vec2-5epochs
This model is a fine-tuned version of [lighteternal/wav2vec2-large-xlsr-53-greek](https://huggingface.co/lighteternal/wav2vec2-large-xlsr-53-greek) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3049
- Accuracy: 0.9282
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 454 | 0.7179 | 0.7599 |
| 0.6962 | 2.0 | 908 | 0.3806 | 0.8911 |
| 0.3776 | 3.0 | 1362 | 0.3299 | 0.9109 |
| 0.2071 | 4.0 | 1816 | 0.3021 | 0.9257 |
| 0.1262 | 5.0 | 2270 | 0.3049 | 0.9282 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.3.dev0
- Tokenizers 0.12.1
|
huggingtweets/benny_thejet_11
|
huggingtweets
| 2022-06-08T02:50:27Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-08T02:42:32Z |
---
language: en
thumbnail: http://www.huggingtweets.com/benny_thejet_11/1654656621512/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/1328273166599217152/TUO71Spk_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">Benny “The Jet”</div>
<div style="text-align: center; font-size: 14px;">@benny_thejet_11</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 Benny “The Jet”.
| Data | Benny “The Jet” |
| --- | --- |
| Tweets downloaded | 338 |
| Retweets | 24 |
| Short tweets | 53 |
| Tweets kept | 261 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2dvxsn3h/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 @benny_thejet_11's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3b7y2vf9) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3b7y2vf9/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/benny_thejet_11')
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/_pancagkes
|
huggingtweets
| 2022-06-08T02:40:03Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-08T02:31:47Z |
---
language: en
thumbnail: http://www.huggingtweets.com/_pancagkes/1654655985301/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/1525194520970899457/uqCAbAl__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">carlala</div>
<div style="text-align: center; font-size: 14px;">@_pancagkes</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 carlala.
| Data | carlala |
| --- | --- |
| Tweets downloaded | 3096 |
| Retweets | 2299 |
| Short tweets | 253 |
| Tweets kept | 544 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/w3ejvw24/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 @_pancagkes's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1e8xcsmm) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1e8xcsmm/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/_pancagkes')
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)
|
qbhy/model-example
|
qbhy
| 2022-06-08T02:25:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-06-08T02:23:37Z |
# 这是一个测试模型
language:
- "List of ISO 639-1 code for your language"
- zh
thumbnail: "url to a thumbnail used in social sharing"
tags:
- example
- qbhy
license: "any valid license identifier"
datasets:
- qbhy/dataset-example
metrics:
- metric1
|
anas-awadalla/bert-base-uncased-lora-squad
|
anas-awadalla
| 2022-06-08T02:05:03Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"region:us"
] | null | 2022-06-08T01:07:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-lora-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. -->
# bert-base-uncased-lora-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad 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: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15.0
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
jppaolim/v61_Large_2E
|
jppaolim
| 2022-06-08T01:06:26Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-08T00:20:05Z |
# My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur is in his beach house. He decides to lay out. Arthur wants to lay out on the beach. He puts on his favorite sandals. Arthur lays on the beach.
Arthur goes to the beach. Arthur is walking on a beach. He notices a family enjoying the beach. He offers to swim with them. The family swims with him. Arthur and the family enjoy the beach.
Arthur goes to the beach. Arthur always had a lot of fun at the beach. One day his friends invite him to go swimming. Arthur accepts their invitation and agrees to go swimming. On the way to the beach Arthur gets into an argument with a boy. He leaves the beach disappointed but happy.
Arthur goes to the beach. Arthur has never been to the beach. His friends tell him about it and he decides to go. He parks his car, packs up his bags and walks to the beach. Arthur looks at the beach and begins to take pictures. He returns home and is very happy.
Arthur goes to the beach. Arthur is so tired of not seeing the sun. He finally decides to go the beach. He walks down the beach. He sees a large sandcastle and waves crashing. He is finally able to see the sun.
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1.05}
Arthur goes to the beach. Arthur never liked the sand at the beach. He was sure it would make him ill. One day his friends convinced him to go to the beach. Once there, Arthur saw many beautiful shells on the beach. Arthur decided that he enjoyed going to the beach!
Arthur goes to the beach. Arthur loves going to the beach with his grandfather. Arthur's grandfather always brings his fishing pole. Today is Arthur's first time seeing his grandfather's fishing pole. He can't believe how much he loves his grandfather's fishing pole. Arthur can't wait for his grandfather's fishing pole next weekend.
Arthur goes to the beach. Arthur loves going to the beach. This weekend he goes for the first time. He decides he wants to go swimming. He finds a beautiful spot for his swimming excursion. He is very glad he went.
Arthur goes to the beach. It was a hot summer day. Arthur had forgotten his sunscreen and he was sweating profusely. He decided to take a dip in the ocean instead of staying inside. He laid on the sand and relaxed until it cooled off. Arthur was glad that he didn't go inside all day!
Arthur goes to the beach. Arthur was bored on a week long vacation. So he decided to head to the beach. He walked along the shore and jumped in the water. He jumped off and ran towards his friends. Arthur had so much fun on the beach that day.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.8, 'repetition_penalty': 1.1}
Arthur goes to the beach. One day Arthur was out on his boat in the ocean. He noticed a big wave coming at him from the north. He decided to swim to shore and waited for it to pass. When it did he jumped into the water. The waves were so large that Arthur drowned and never returned home.
Arthur goes to the beach. Arthur loves going to the beach. He usually stays at his house. One day, he decides he wants to go to the beach. He buys a new life preserver and sets off for the beach. Finally he finds the perfect spot on the sand and has fun.
Arthur goes to the beach. Arthur was a very athletic boy. He loved going to the beach and swimming. One day, he decided to take a swim in the ocean. He swam for hours and did not feel tired at all. Later that day, Arthur swam back to shore with his friends!
Arthur goes to the beach. Arthur wanted to go to the beach. He had never been before. He asked his friends if they would go with him. They all agreed and they went together. At the end of the day, Arthur felt much better about the trip.
Arthur goes to the beach. Arthur is feeling lonely at home. He decides he needs a way to make new friends. He decides to go to the beach. At the beach he meets some cool people. Arthur has made new friends at the beach.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.6, 'repetition_penalty': 1.15}
Arthur goes to the beach. One day Arthur went to the beach with his friends. He played in the sand for a while. Then he sat and watched the waves roll in. When it was time to go home, Arthur's friends all left him. Arthur decided that he would never go back to the beach.
Arthur goes to the beach. Arthur had always wanted to go to the beach. He finally saved up enough money for a trip to the beach. On his first day at the beach he got lost. The next day he found the beach and was very happy. He is now planning on going back every weekend.
Arthur goes to the beach. One day, Arthur decides he wants to go to the beach. He drives to the beach and takes a taxi to get there. When he gets there, he parks his car. Then, he walks around for a while. Finally, he enjoys the sunset at the beach.
Arthur goes to the beach. Arthur was on vacation in Florida. He decided to go to the beach. He saw a girl that he liked and went up to her. She said yes and they spent the day together. They ended up dating for three years!
Arthur goes to the beach. Arthur was going on a vacation. He needed a place to stay. The beach was his first choice. He found one nearby. It was perfect for him.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.4, 'repetition_penalty': 1.2}
Arthur goes to the beach. Arthur is a very adventurous boy who loves going to the ocean. He decides he wants to go swimming at the local pool. At the local pool, Arthur swims for hours in the water. Finally, it's time to get out of the pool and go home. Now Arthur has a great day at the beach!
Arthur goes to the beach. One day Arthur was on vacation in Florida. He decided he wanted to go to the beach. At first it seemed like a long trip but then he got there. There were so many beautiful beaches! Finally, after an hour of walking, he arrived at the beach.
Arthur goes to the beach. One day Arthur decided he wanted to go to the beach. He packed his surfboard and some sunscreen. Then he went out on the water. When he got there, it was very sunny. Arthur had a great time at the beach!
Arthur goes to the beach. Arthur is on vacation in Florida. He decides he wants to go to the beach. At the beach, Arthur sees a beautiful sunset. He enjoys his day at the beach. Arthur returns home happy that he went to the beach.
Arthur goes to the beach. Arthur is a very adventurous person. He decides that he wants to go to the beach. He packs his bag and leaves for the beach. At the beach, Arthur sees many beautiful beaches. Finally, Arthur returns home happy with his trip.
|
anas-awadalla/roberta-large-prefix-tuning-squad
|
anas-awadalla
| 2022-06-08T00:45:43Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"region:us"
] | null | 2022-06-07T23:00:31Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-prefix-tuning-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. -->
# roberta-large-prefix-tuning-squad
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the squad 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: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- 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 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
skiltz/dall-e
|
skiltz
| 2022-06-07T23:42:38Z | 0 | 0 | null |
[
"arxiv:2102.12092",
"region:us"
] | null | 2022-06-10T05:32:10Z |
# Model Card: DALL·E Mini
This model is a reproduction of OpenAI’s DALL·E. Please see [this link](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy) for project-specific details. Below, we include the original DALL·E model card available on [the OpenAI github](https://github.com/openai/DALL-E/edit/master/model_card.md).
## Model Details
The dVAE was developed by researchers at OpenAI to reduce the memory footprint of the transformer trained on the
text-to-image generation task. The details involved in training the dVAE are described in [the paper][dalle_paper]. This
model card describes the first version of the model, released in February 2021. The model consists of a convolutional
encoder and decoder whose architectures are described [here](dall_e/encoder.py) and [here](dall_e/decoder.py), respectively.
For questions or comments about the models or the code release, please file a Github issue.
## Model Use
### Intended Use
The model is intended for others to use for training their own generative models.
### Out-of-Scope Use Cases
This model is inappropriate for high-fidelity image processing applications. We also do not recommend its use as a
general-purpose image compressor.
## Training Data
The model was trained on publicly available text-image pairs collected from the internet. This data consists partly of
[Conceptual Captions][cc] and a filtered subset of [YFCC100M][yfcc100m]. We used a subset of the filters described in
[Sharma et al.][cc_paper] to construct this dataset; further details are described in [our paper][dalle_paper]. We will
not be releasing the dataset.
## Performance and Limitations
The heavy compression from the encoding process results in a noticeable loss of detail in the reconstructed images. This
renders it inappropriate for applications that require fine-grained details of the image to be preserved.
[dalle_paper]: https://arxiv.org/abs/2102.12092
[cc]: https://ai.google.com/research/ConceptualCaptions
[cc_paper]: https://www.aclweb.org/anthology/P18-1238/
[yfcc100m]: http://projects.dfki.uni-kl.de/yfcc100m/
|
huggingtweets/dwr-elonmusk-maccaw
|
huggingtweets
| 2022-06-07T23:37:18Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-07T23:37:10Z |
---
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/1529956155937759233/Nyn1HZWF_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/1418421541054918657/ng4Kyv5G_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/1518670972559130624/-G9gNsOp_400x400.png')">
</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">Elon Musk & Alex MacCaw & Dan Romero</div>
<div style="text-align: center; font-size: 14px;">@dwr-elonmusk-maccaw</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 Elon Musk & Alex MacCaw & Dan Romero.
| Data | Elon Musk | Alex MacCaw | Dan Romero |
| --- | --- | --- | --- |
| Tweets downloaded | 3200 | 3244 | 3126 |
| Retweets | 146 | 255 | 2 |
| Short tweets | 956 | 258 | 333 |
| Tweets kept | 2098 | 2731 | 2791 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ritkn2s/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 @dwr-elonmusk-maccaw's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1o2qtjkw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1o2qtjkw/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/dwr-elonmusk-maccaw')
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)
|
anas-awadalla/spanbert-base-cased-compacter-squad
|
anas-awadalla
| 2022-06-07T23:29:52Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"region:us"
] | null | 2022-06-07T22:59:06Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-compacter-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. -->
# spanbert-base-cased-compacter-squad
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad 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: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15.0
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anas-awadalla/roberta-large-compacter-squad
|
anas-awadalla
| 2022-06-07T22:57:36Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"region:us"
] | null | 2022-06-07T21:35:02Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-large-compacter-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. -->
# roberta-large-compacter-squad
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the squad 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: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15.0
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Anery/bert-finetuned-ner
|
Anery
| 2022-06-07T22:48:14Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-07T20:44:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0244
- Precision: 0.7368
- Recall: 0.4
- F1: 0.5185
- Accuracy: 0.9919
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 14 | 0.0598 | 0.0 | 0.0 | 0.0 | 0.9870 |
| No log | 2.0 | 28 | 0.0357 | 0.0 | 0.0 | 0.0 | 0.9894 |
| No log | 3.0 | 42 | 0.0256 | 0.75 | 0.2571 | 0.3830 | 0.9910 |
| No log | 4.0 | 56 | 0.0244 | 0.7368 | 0.4 | 0.5185 | 0.9919 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Cristian-dcg/beto-sentiment-analysis-finetuned-onpremise
|
Cristian-dcg
| 2022-06-07T22:36:41Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-30T21:10:37Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: beto-sentiment-analysis-finetuned-onpremise
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. -->
# beto-sentiment-analysis-finetuned-onpremise
This model is a fine-tuned version of [finiteautomata/beto-sentiment-analysis](https://huggingface.co/finiteautomata/beto-sentiment-analysis) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7939
- Accuracy: 0.8301
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4573 | 1.0 | 1250 | 0.4375 | 0.8191 |
| 0.2191 | 2.0 | 2500 | 0.5367 | 0.8288 |
| 0.1164 | 3.0 | 3750 | 0.7939 | 0.8301 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 1.18.4
- Tokenizers 0.12.1
|
ferjeffQ/roberta-base-bne-finetuned-amazon_reviews_multi
|
ferjeffQ
| 2022-06-07T21:47:07Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-07T21:31:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model-index:
- name: roberta-base-bne-finetuned-amazon_reviews_multi
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
args: es
metrics:
- name: Accuracy
type: accuracy
value: 0.9325
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2207
- Accuracy: 0.9325
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1937 | 1.0 | 1250 | 0.1811 | 0.9327 |
| 0.1005 | 2.0 | 2500 | 0.2207 | 0.9325 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
nateraw/my-cool-model-with-eval-results
|
nateraw
| 2022-06-07T21:42:12Z | 0 | 0 |
timm
|
[
"timm",
"image-classification",
"resnet",
"en",
"dataset:beans",
"license:mit",
"model-index",
"region:us"
] |
image-classification
| 2022-05-17T20:20:51Z |
---
language: en
license: mit
library_name: timm
tags:
- image-classification
- resnet
datasets: beans
metrics:
- accuracy
- f1
model-index:
- name: my-cool-model-with-eval-results
results:
- task:
type: image-classification
dataset:
type: beans
name: Beans
metrics:
- type: accuracy
value: 0.85
- type: f1
value: 0.75
---
# my-cool-model-with-eval-results
## Model description
This isn't really a model, it's just a test repo to see if the [modelcards](https://github.com/nateraw/modelcards) package works!
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data.
## Training procedure
Preprocessing, hardware used, hyperparameters...
## Eval results
Provide some evaluation results.
### BibTeX entry and citation info
```bibtex
@inproceedings{...,
year={2020}
}
```
|
nateraw/my-cool-model-with-card
|
nateraw
| 2022-06-07T21:41:53Z | 0 | 0 |
timm
|
[
"timm",
"image-classification",
"resnet",
"en",
"dataset:beans",
"license:mit",
"region:us"
] |
image-classification
| 2022-05-13T02:13:22Z |
---
language: en
license: mit
library_name: timm
tags:
- image-classification
- resnet
datasets: beans
metrics:
- acc
- f1
---
# my-cool-model-with-card
## Model description
This isn't really a model, it's just a test repo to see if the [modelcards](https://github.com/nateraw/modelcards) package works!
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data.
## Training procedure
Preprocessing, hardware used, hyperparameters...
## Eval results
Provide some evaluation results.
### BibTeX entry and citation info
```bibtex
@inproceedings{...,
year={2020}
}
```
|
huggingtweets/afraidofwasps-dril-senn_spud
|
huggingtweets
| 2022-06-07T21:10:15Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-28T00:36:09Z |
---
language: en
thumbnail: http://www.huggingtweets.com/afraidofwasps-dril-senn_spud/1654636210975/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/1510917391533830145/XW-zSFDJ_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/1387151448203358209/HKNuKY7L_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/1182478458552832000/xqEwluRJ_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">wint & Will Sennett & Boots, 'with the fur'</div>
<div style="text-align: center; font-size: 14px;">@afraidofwasps-dril-senn_spud</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 wint & Will Sennett & Boots, 'with the fur'.
| Data | wint | Will Sennett | Boots, 'with the fur' |
| --- | --- | --- | --- |
| Tweets downloaded | 3230 | 3228 | 3217 |
| Retweets | 487 | 312 | 504 |
| Short tweets | 297 | 622 | 434 |
| Tweets kept | 2446 | 2294 | 2279 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/156iladp/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 @afraidofwasps-dril-senn_spud's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/6g2dktc9) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/6g2dktc9/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/afraidofwasps-dril-senn_spud')
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)
|
ksabeh/bert-base-uncased-attribute-correction
|
ksabeh
| 2022-06-07T21:01:05Z | 10 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-07T12:44:48Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ksabeh/bert-base-uncased-attribute-correction
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. -->
# ksabeh/bert-base-uncased-attribute-correction
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0541
- Validation Loss: 0.0579
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36848, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1513 | 0.0671 | 0 |
| 0.0541 | 0.0579 | 1 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
renjithks/layoutlmv1-cord-ner
|
renjithks
| 2022-06-07T20:59:30Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlm",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-07T20:44:15Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv1-cord-ner
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. -->
# layoutlmv1-cord-ner
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1438
- Precision: 0.9336
- Recall: 0.9453
- F1: 0.9394
- Accuracy: 0.9767
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 113 | 0.1251 | 0.9054 | 0.9184 | 0.9119 | 0.9651 |
| No log | 2.0 | 226 | 0.1343 | 0.9002 | 0.9261 | 0.9130 | 0.9635 |
| No log | 3.0 | 339 | 0.1264 | 0.9189 | 0.9357 | 0.9272 | 0.9647 |
| No log | 4.0 | 452 | 0.1235 | 0.9122 | 0.9376 | 0.9248 | 0.9681 |
| 0.1371 | 5.0 | 565 | 0.1353 | 0.9378 | 0.9405 | 0.9391 | 0.9717 |
| 0.1371 | 6.0 | 678 | 0.1431 | 0.9233 | 0.9357 | 0.9295 | 0.9709 |
| 0.1371 | 7.0 | 791 | 0.1473 | 0.9289 | 0.9405 | 0.9347 | 0.9759 |
| 0.1371 | 8.0 | 904 | 0.1407 | 0.9473 | 0.9491 | 0.9482 | 0.9784 |
| 0.0106 | 9.0 | 1017 | 0.1440 | 0.9301 | 0.9453 | 0.9376 | 0.9769 |
| 0.0106 | 10.0 | 1130 | 0.1438 | 0.9336 | 0.9453 | 0.9394 | 0.9767 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
anas-awadalla/bert-large-uncased-compacter-squad
|
anas-awadalla
| 2022-06-07T20:53:57Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"region:us"
] | null | 2022-06-07T19:12:54Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-large-uncased-compacter-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. -->
# bert-large-uncased-compacter-squad
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the squad 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: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15.0
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
huggingtweets/0pn-lil_icebunny
|
huggingtweets
| 2022-06-07T20:49:32Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-07T20:48:55Z |
---
language: en
thumbnail: http://www.huggingtweets.com/0pn-lil_icebunny/1654634967211/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/1331413261070307329/N7du8baD_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/1194734625547010048/NB1V0fMb_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">oneohtrix point never & JAMES FERRARO</div>
<div style="text-align: center; font-size: 14px;">@0pn-lil_icebunny</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 oneohtrix point never & JAMES FERRARO.
| Data | oneohtrix point never | JAMES FERRARO |
| --- | --- | --- |
| Tweets downloaded | 1862 | 3184 |
| Retweets | 361 | 167 |
| Short tweets | 417 | 926 |
| Tweets kept | 1084 | 2091 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/btu8y5w7/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 @0pn-lil_icebunny's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2fg2ki8d) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2fg2ki8d/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/0pn-lil_icebunny')
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/jpegmafia
|
huggingtweets
| 2022-06-07T20:33:58Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-07T20:33:15Z |
---
language: en
thumbnail: http://www.huggingtweets.com/jpegmafia/1654634032817/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/1510648677995581453/13zowZ1f_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">JPEGMAFIA</div>
<div style="text-align: center; font-size: 14px;">@jpegmafia</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 JPEGMAFIA.
| Data | JPEGMAFIA |
| --- | --- |
| Tweets downloaded | 3114 |
| Retweets | 1181 |
| Short tweets | 495 |
| Tweets kept | 1438 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ub5q17i2/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 @jpegmafia's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ihd6e39h) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ihd6e39h/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/jpegmafia')
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)
|
Galeros/dqn-mountaincar-v0-opt
|
Galeros
| 2022-06-07T20:19:00Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-07T20:18:53Z |
---
library_name: stable-baselines3
tags:
- MountainCar-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: -120.60 +/- 28.30
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MountainCar-v0
type: MountainCar-v0
---
# **DQN** Agent playing **MountainCar-v0**
This is a trained model of a **DQN** agent playing **MountainCar-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Galeros/dqn-mountaincar-v0
|
Galeros
| 2022-06-07T20:14:17Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-07T19:11:49Z |
---
library_name: stable-baselines3
tags:
- MountainCar-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: -101.40 +/- 9.64
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MountainCar-v0
type: MountainCar-v0
---
# **DQN** Agent playing **MountainCar-v0**
This is a trained model of a **DQN** agent playing **MountainCar-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
mariastull/q-Taxi-v3-2
|
mariastull
| 2022-06-07T19:37:43Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-07T19:37:35Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-2
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="mariastull/q-Taxi-v3-2", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
mariastull/q-Taxi-v3
|
mariastull
| 2022-06-07T19:35:05Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-07T19:34:38Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="mariastull/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"])
```
|
pylemountain/distilbert-base-uncased-finetuned-imdb
|
pylemountain
| 2022-06-07T19:33:15Z | 9 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-07T18:59:54Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: pylemountain/distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# pylemountain/distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.8553
- Validation Loss: 2.5640
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.8553 | 2.5640 | 0 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
mariastull/q-FrozenLake-v1-4x4-noSlippery
|
mariastull
| 2022-06-07T19:18:07Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-07T18:44:52Z |
---
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="mariastull/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"])
```
|
anas-awadalla/bert-base-uncased-compacter-squad
|
anas-awadalla
| 2022-06-07T19:09:25Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"region:us"
] | null | 2022-06-07T18:39:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-compacter-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. -->
# bert-base-uncased-compacter-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad 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: 64
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 512
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15.0
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
huggingtweets/jeanswayy
|
huggingtweets
| 2022-06-07T18:40:15Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-07T18:21:26Z |
---
language: en
thumbnail: http://www.huggingtweets.com/jeanswayy/1654627123103/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/1448289036171309068/LiGzmPgt_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">j e a n 🤷🏻♀️</div>
<div style="text-align: center; font-size: 14px;">@jeanswayy</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 j e a n 🤷🏻♀️.
| Data | j e a n 🤷🏻♀️ |
| --- | --- |
| Tweets downloaded | 2697 |
| Retweets | 1017 |
| Short tweets | 240 |
| Tweets kept | 1440 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/16duoq0d/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 @jeanswayy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ds4fwqc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ds4fwqc/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/jeanswayy')
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)
|
anas-awadalla/spanbert-base-cased-prefix-tuning-squad
|
anas-awadalla
| 2022-06-07T18:36:13Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"region:us"
] | null | 2022-06-07T17:49:46Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-prefix-tuning-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. -->
# spanbert-base-cased-prefix-tuning-squad
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad 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: 64
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 256
- total_eval_batch_size: 32
- 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 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
huggingtweets/irodori7
|
huggingtweets
| 2022-06-07T18:27:35Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-07T18:27:27Z |
---
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/948537441429803009/NgUotYet_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">たつき／irodori</div>
<div style="text-align: center; font-size: 14px;">@irodori7</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 たつき／irodori.
| Data | たつき／irodori |
| --- | --- |
| Tweets downloaded | 1494 |
| Retweets | 224 |
| Short tweets | 1087 |
| Tweets kept | 183 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2641xmb8/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 @irodori7's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3pehfpkr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3pehfpkr/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/irodori7')
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)
|
Theivaprakasham/layoutlmv3-finetuned-sroie
|
Theivaprakasham
| 2022-06-07T18:08:04Z | 69 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:sroie",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-07T10:26:57Z |
---
tags:
- generated_from_trainer
datasets:
- sroie
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-sroie
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: sroie
type: sroie
args: sroie
metrics:
- name: Precision
type: precision
value: 0.9370529327610873
- name: Recall
type: recall
value: 0.9438040345821326
- name: F1
type: f1
value: 0.9404163675520459
- name: Accuracy
type: accuracy
value: 0.9945347083116948
---
<!-- 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. -->
# layoutlmv3-finetuned-sroie
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0426
- Precision: 0.9371
- Recall: 0.9438
- F1: 0.9404
- Accuracy: 0.9945
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.32 | 100 | 0.1127 | 0.6466 | 0.6102 | 0.6279 | 0.9729 |
| No log | 0.64 | 200 | 0.0663 | 0.8215 | 0.7428 | 0.7802 | 0.9821 |
| No log | 0.96 | 300 | 0.0563 | 0.8051 | 0.8718 | 0.8371 | 0.9855 |
| No log | 1.28 | 400 | 0.0470 | 0.8766 | 0.8595 | 0.8680 | 0.9895 |
| 0.1328 | 1.6 | 500 | 0.0419 | 0.8613 | 0.9128 | 0.8863 | 0.9906 |
| 0.1328 | 1.92 | 600 | 0.0338 | 0.8888 | 0.9099 | 0.8993 | 0.9926 |
| 0.1328 | 2.24 | 700 | 0.0320 | 0.8690 | 0.9467 | 0.9062 | 0.9929 |
| 0.1328 | 2.56 | 800 | 0.0348 | 0.8960 | 0.9438 | 0.9193 | 0.9931 |
| 0.1328 | 2.88 | 900 | 0.0300 | 0.9169 | 0.9460 | 0.9312 | 0.9942 |
| 0.029 | 3.19 | 1000 | 0.0281 | 0.9080 | 0.9452 | 0.9262 | 0.9942 |
| 0.029 | 3.51 | 1100 | 0.0259 | 0.9174 | 0.9438 | 0.9304 | 0.9945 |
| 0.029 | 3.83 | 1200 | 0.0309 | 0.9207 | 0.9532 | 0.9366 | 0.9944 |
| 0.029 | 4.15 | 1300 | 0.0366 | 0.9195 | 0.9388 | 0.9291 | 0.9940 |
| 0.029 | 4.47 | 1400 | 0.0302 | 0.9343 | 0.9424 | 0.9383 | 0.9949 |
| 0.0174 | 4.79 | 1500 | 0.0349 | 0.9142 | 0.9517 | 0.9326 | 0.9939 |
| 0.0174 | 5.11 | 1600 | 0.0327 | 0.9322 | 0.9510 | 0.9415 | 0.9950 |
| 0.0174 | 5.43 | 1700 | 0.0317 | 0.9215 | 0.9561 | 0.9385 | 0.9938 |
| 0.0174 | 5.75 | 1800 | 0.0385 | 0.9282 | 0.9316 | 0.9299 | 0.9940 |
| 0.0174 | 6.07 | 1900 | 0.0342 | 0.9235 | 0.9481 | 0.9357 | 0.9944 |
| 0.0117 | 6.39 | 2000 | 0.0344 | 0.9287 | 0.9474 | 0.9379 | 0.9944 |
| 0.0117 | 6.71 | 2100 | 0.0388 | 0.9232 | 0.9445 | 0.9338 | 0.9941 |
| 0.0117 | 7.03 | 2200 | 0.0325 | 0.9269 | 0.9496 | 0.9381 | 0.9949 |
| 0.0117 | 7.35 | 2300 | 0.0343 | 0.9225 | 0.9438 | 0.9330 | 0.9941 |
| 0.0117 | 7.67 | 2400 | 0.0372 | 0.9216 | 0.9481 | 0.9347 | 0.9944 |
| 0.0081 | 7.99 | 2500 | 0.0385 | 0.9192 | 0.9589 | 0.9386 | 0.9944 |
| 0.0081 | 8.31 | 2600 | 0.0376 | 0.9293 | 0.9467 | 0.9379 | 0.9944 |
| 0.0081 | 8.63 | 2700 | 0.0425 | 0.9261 | 0.9474 | 0.9366 | 0.9941 |
| 0.0081 | 8.95 | 2800 | 0.0407 | 0.9266 | 0.9452 | 0.9358 | 0.9941 |
| 0.0081 | 9.27 | 2900 | 0.0403 | 0.9280 | 0.9467 | 0.9372 | 0.9941 |
| 0.0055 | 9.58 | 3000 | 0.0364 | 0.9287 | 0.9474 | 0.9379 | 0.9948 |
| 0.0055 | 9.9 | 3100 | 0.0427 | 0.9122 | 0.9510 | 0.9312 | 0.9941 |
| 0.0055 | 10.22 | 3200 | 0.0394 | 0.9223 | 0.9488 | 0.9354 | 0.9943 |
| 0.0055 | 10.54 | 3300 | 0.0393 | 0.9247 | 0.9561 | 0.9401 | 0.9945 |
| 0.0055 | 10.86 | 3400 | 0.0413 | 0.9334 | 0.9496 | 0.9414 | 0.9945 |
| 0.0049 | 11.18 | 3500 | 0.0400 | 0.9290 | 0.9517 | 0.9402 | 0.9945 |
| 0.0049 | 11.5 | 3600 | 0.0412 | 0.9317 | 0.9539 | 0.9427 | 0.9945 |
| 0.0049 | 11.82 | 3700 | 0.0419 | 0.9314 | 0.9481 | 0.9397 | 0.9947 |
| 0.0049 | 12.14 | 3800 | 0.0452 | 0.9243 | 0.9503 | 0.9371 | 0.9941 |
| 0.0049 | 12.46 | 3900 | 0.0412 | 0.9334 | 0.9496 | 0.9414 | 0.9947 |
| 0.0039 | 12.78 | 4000 | 0.0438 | 0.9294 | 0.9481 | 0.9387 | 0.9941 |
| 0.0039 | 13.1 | 4100 | 0.0416 | 0.9326 | 0.9467 | 0.9396 | 0.9944 |
| 0.0039 | 13.42 | 4200 | 0.0418 | 0.9327 | 0.9488 | 0.9407 | 0.9948 |
| 0.0039 | 13.74 | 4300 | 0.0423 | 0.9345 | 0.9460 | 0.9402 | 0.9946 |
| 0.0039 | 14.06 | 4400 | 0.0419 | 0.9286 | 0.9467 | 0.9376 | 0.9947 |
| 0.0022 | 14.38 | 4500 | 0.0426 | 0.9371 | 0.9438 | 0.9404 | 0.9945 |
| 0.0022 | 14.7 | 4600 | 0.0424 | 0.9371 | 0.9445 | 0.9408 | 0.9947 |
| 0.0022 | 15.02 | 4700 | 0.0427 | 0.9372 | 0.9467 | 0.9419 | 0.9947 |
| 0.0022 | 15.34 | 4800 | 0.0431 | 0.9339 | 0.9460 | 0.9399 | 0.9945 |
| 0.0022 | 15.65 | 4900 | 0.0431 | 0.9346 | 0.9467 | 0.9406 | 0.9946 |
| 0.0015 | 15.97 | 5000 | 0.0434 | 0.9324 | 0.9445 | 0.9384 | 0.9945 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
anas-awadalla/bert-base-uncased-prefix-tuning-squad
|
anas-awadalla
| 2022-06-07T17:44:02Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"region:us"
] | null | 2022-06-07T16:54:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-prefix-tuning
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-prefix-tuning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad 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: 64
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 256
- total_eval_batch_size: 32
- 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 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
inokufu/bert-base-uncased-xnli-sts-finetuned-education
|
inokufu
| 2022-06-07T16:39:43Z | 9 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"Education",
"en",
"xnli",
"stsb_multi_mt",
"dataset:xnli",
"dataset:stsb_multi_mt",
"arxiv:1810.04805",
"arxiv:1809.05053",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-06-07T15:36:19Z |
---
pipeline_tag: sentence-similarity
language: en
tags:
- sentence-similarity
- transformers
- Education
- en
- bert
- sentence-transformers
- feature-extraction
- xnli
- stsb_multi_mt
datasets:
- xnli
- stsb_multi_mt
---
# inokufu/bertheo-en
A [sentence-transformers](https://www.SBERT.net) model fine-tuned on course sentences. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Details
This model is based on the English bert-base-uncased pre-trained model [1, 2].
It was first fine-tuned on our learning object (LO) sentences dataset. This dataset consists of a sample of 500k sentences of course descriptions. We used standard parameter settings for fine-tuning as mentioned in the original BERT paper [2]. This allows the model to improve its performance on the target task (Masked Language Model) for domain-specific sentences.
It was then fine-tuned on a natural language inference task (XNLI) [3]. This task consists in training the model to recognize relations between sentences (contradiction, neutral, implication).
It was then fine-tuned on a text semantic similarity task (on STS data) [4]. This task consists in training the model to estimate the similarity between two sentences.
This fine-tuning process allows our model to have a semantic representation of words that is much better than the one proposed by the base model.
## 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 = ["Learn to code in python", "Become an expert in accounting"]
model = SentenceTransformer('inokufu/bert-base-uncased-xnli-sts-finetuned-education')
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 = ["Learn to code in python", "Become an expert in accounting"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('inokufu/bert-base-uncased-xnli-sts-finetuned-education')
model = AutoModel.from_pretrained('inokufu/bert-base-uncased-xnli-sts-finetuned-education')
# 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
STS (en) score: 84.61%
## Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(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})
)
```
## References
[1] https://huggingface.co/bert-base-uncased <br>
[2] https://arxiv.org/abs/1810.04805 <br>
[3] https://arxiv.org/abs/1809.05053 <br>
[4] https://huggingface.co/datasets/stsb_multi_mt <br>
|
elena-soare/bat-table-aug
|
elena-soare
| 2022-06-07T16:15:48Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-21T21:23:22Z |
# Text2SQL Task T5-Base + Fine-tuning on Spider + Table Augumentation
This is our T5 model fine-tuned on Spider using a schema serialization, which includes a table description for injecting domain knowledge into T5
## Running the model
Inspired by the work done by [Picard](https://github.com/ElementAI/picard/) by adding a table description to the question and serialized schema:
```python
[question] | [db_id] | [table] : [column] ( [content] , [content] ) , [column] ( ... ) , [...] | [table] : ... | ... description * [table] : <meaning of table>; [table] : <meaning of table> ; ....
```
|
huggingtweets/mizefian
|
huggingtweets
| 2022-06-07T16:10:44Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-07T16:10:37Z |
---
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/1488896240083517453/Bu0lDApj_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">Mizefian 🇺🇦</div>
<div style="text-align: center; font-size: 14px;">@mizefian</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 Mizefian 🇺🇦.
| Data | Mizefian 🇺🇦 |
| --- | --- |
| Tweets downloaded | 1265 |
| Retweets | 188 |
| Short tweets | 355 |
| Tweets kept | 722 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/x49ahgym/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 @mizefian's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/xdjgjn3p) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/xdjgjn3p/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/mizefian')
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)
|
mmillet/rubert-tiny2_best_finetuned_emotion_experiment_augmented_anger_fear
|
mmillet
| 2022-06-07T15:52:18Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-07T15:44:34Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: rubert-tiny2_best_finetuned_emotion_experiment_augmented_anger_fear
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rubert-tiny2_best_finetuned_emotion_experiment_augmented_anger_fear
This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3902
- Accuracy: 0.8727
- F1: 0.8720
- Precision: 0.8718
- Recall: 0.8727
## 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=0.0001
- lr_scheduler_type: linear
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.3497 | 1.0 | 69 | 1.2944 | 0.5376 | 0.4665 | 0.6374 | 0.5376 |
| 1.2023 | 2.0 | 138 | 1.0370 | 0.7056 | 0.6745 | 0.7458 | 0.7056 |
| 0.9289 | 3.0 | 207 | 0.7437 | 0.8121 | 0.8082 | 0.8117 | 0.8121 |
| 0.6932 | 4.0 | 276 | 0.5717 | 0.8445 | 0.8428 | 0.8434 | 0.8445 |
| 0.5613 | 5.0 | 345 | 0.4888 | 0.8580 | 0.8572 | 0.8573 | 0.8580 |
| 0.469 | 6.0 | 414 | 0.4401 | 0.8633 | 0.8625 | 0.8623 | 0.8633 |
| 0.4176 | 7.0 | 483 | 0.4156 | 0.8653 | 0.8646 | 0.8644 | 0.8653 |
| 0.3724 | 8.0 | 552 | 0.4001 | 0.8706 | 0.8700 | 0.8699 | 0.8706 |
| 0.3427 | 9.0 | 621 | 0.3972 | 0.8706 | 0.8698 | 0.8701 | 0.8706 |
| 0.3243 | 10.0 | 690 | 0.3898 | 0.8737 | 0.8729 | 0.8736 | 0.8737 |
| 0.3039 | 11.0 | 759 | 0.3887 | 0.8716 | 0.8710 | 0.8717 | 0.8716 |
| 0.2803 | 12.0 | 828 | 0.3841 | 0.8716 | 0.8709 | 0.8709 | 0.8716 |
| 0.264 | 13.0 | 897 | 0.3872 | 0.8758 | 0.8753 | 0.8758 | 0.8758 |
| 0.2607 | 14.0 | 966 | 0.3837 | 0.8747 | 0.8743 | 0.8741 | 0.8747 |
| 0.2437 | 15.0 | 1035 | 0.3893 | 0.8716 | 0.8710 | 0.8712 | 0.8716 |
| 0.2358 | 16.0 | 1104 | 0.3867 | 0.8695 | 0.8691 | 0.8690 | 0.8695 |
| 0.2278 | 17.0 | 1173 | 0.3886 | 0.8737 | 0.8732 | 0.8732 | 0.8737 |
| 0.2143 | 18.0 | 1242 | 0.3902 | 0.8727 | 0.8720 | 0.8718 | 0.8727 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
PontifexMaximus/mt5-small-parsinlu-opus-translation_fa_en-finetuned-fa-to-en
|
PontifexMaximus
| 2022-06-07T15:17:41Z | 24 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_infopankki",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-03T10:59:17Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- opus_infopankki
metrics:
- bleu
model-index:
- name: mt5-small-parsinlu-opus-translation_fa_en-finetuned-fa-to-en
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus_infopankki
type: opus_infopankki
args: en-fa
metrics:
- name: Bleu
type: bleu
value: 15.1329
---
<!-- 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-small-parsinlu-opus-translation_fa_en-finetuned-fa-to-en
This model is a fine-tuned version of [persiannlp/mt5-small-parsinlu-opus-translation_fa_en](https://huggingface.co/persiannlp/mt5-small-parsinlu-opus-translation_fa_en) on the opus_infopankki dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9193
- Bleu: 15.1329
- Gen Len: 13.4603
## 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-06
- 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 3.1182 | 1.0 | 1807 | 2.5985 | 10.6445 | 13.7938 |
| 2.8377 | 2.0 | 3614 | 2.3799 | 11.852 | 13.6168 |
| 2.6644 | 3.0 | 5421 | 2.2426 | 12.877 | 13.5768 |
| 2.5286 | 4.0 | 7228 | 2.1521 | 13.5342 | 13.5567 |
| 2.4523 | 5.0 | 9035 | 2.0801 | 14.0355 | 13.5387 |
| 2.4026 | 6.0 | 10842 | 2.0197 | 14.4284 | 13.4956 |
| 2.317 | 7.0 | 12649 | 1.9691 | 14.7776 | 13.4325 |
| 2.3174 | 8.0 | 14456 | 1.9373 | 15.189 | 13.4261 |
| 2.3374 | 9.0 | 16263 | 1.9393 | 15.1149 | 13.4087 |
| 2.3131 | 10.0 | 18070 | 1.9304 | 15.0654 | 13.4234 |
| 2.295 | 11.0 | 19877 | 1.9239 | 15.102 | 13.4443 |
| 2.3017 | 12.0 | 21684 | 1.9203 | 15.1676 | 13.4575 |
| 2.3153 | 13.0 | 23491 | 1.9193 | 15.1329 | 13.4603 |
| 2.2939 | 14.0 | 25298 | 1.9193 | 15.1329 | 13.4603 |
| 2.3241 | 15.0 | 27105 | 1.9193 | 15.1329 | 13.4603 |
| 2.3376 | 16.0 | 28912 | 1.9193 | 15.1329 | 13.4603 |
| 2.2859 | 17.0 | 30719 | 1.9193 | 15.1329 | 13.4603 |
| 2.3016 | 18.0 | 32526 | 1.9193 | 15.1329 | 13.4603 |
| 2.3101 | 19.0 | 34333 | 1.9193 | 15.1329 | 13.4603 |
| 2.3088 | 20.0 | 36140 | 1.9193 | 15.1329 | 13.4603 |
| 2.2833 | 21.0 | 37947 | 1.9193 | 15.1329 | 13.4603 |
| 2.2986 | 22.0 | 39754 | 1.9193 | 15.1329 | 13.4603 |
| 2.3254 | 23.0 | 41561 | 1.9193 | 15.1329 | 13.4603 |
| 2.3165 | 24.0 | 43368 | 1.9193 | 15.1329 | 13.4603 |
| 2.289 | 25.0 | 45175 | 1.9193 | 15.1329 | 13.4603 |
| 2.3212 | 26.0 | 46982 | 1.9193 | 15.1329 | 13.4603 |
| 2.2902 | 27.0 | 48789 | 1.9193 | 15.1329 | 13.4603 |
| 2.3026 | 28.0 | 50596 | 1.9193 | 15.1329 | 13.4603 |
| 2.2949 | 29.0 | 52403 | 1.9193 | 15.1329 | 13.4603 |
| 2.3152 | 30.0 | 54210 | 1.9193 | 15.1329 | 13.4603 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.7.1+cu110
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingnft/alpacadabraz
|
huggingnft
| 2022-06-07T14:20:28Z | 3 | 1 |
transformers
|
[
"transformers",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"unconditional-image-generation",
"dataset:huggingnft/alpacadabraz",
"license:mit",
"endpoints_compatible",
"region:us"
] |
unconditional-image-generation
| 2022-04-14T22:08:45Z |
---
tags:
- huggingnft
- nft
- huggan
- gan
- image
- images
- unconditional-image-generation
datasets:
- huggingnft/alpacadabraz
license: mit
---
# Hugging NFT: alpacadabraz
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/alpacadabraz).
Dataset is available [here](https://huggingface.co/datasets/huggingnft/alpacadabraz).
Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft).
Project repository: [link](https://github.com/AlekseyKorshuk/huggingnft).
[](https://github.com/AlekseyKorshuk/huggingnft)
## Intended uses & limitations
#### How to use
Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft).
#### Limitations and bias
Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft).
## Training data
Dataset is available [here](https://huggingface.co/datasets/huggingnft/alpacadabraz).
## Training procedure
Training script is available [here](https://github.com/AlekseyKorshuk/huggingnft).
## Generated Images
Check results with Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft).
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingnft)
### BibTeX entry and citation info
```bibtex
@InProceedings{huggingnft,
author={Aleksey Korshuk}
year=2022
}
```
|
nestoralvaro/mt5-small-finetuned-google_small_for_summarization_TF
|
nestoralvaro
| 2022-06-07T14:19:38Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-06T23:07:13Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: nestoralvaro/mt5-small-finetuned-google_small_for_summarization_TF
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. -->
# nestoralvaro/mt5-small-finetuned-google_small_for_summarization_TF
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.3123
- Validation Loss: 2.1399
- Epoch: 7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 266360, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.2631 | 2.3702 | 0 |
| 2.6166 | 2.2422 | 1 |
| 2.4974 | 2.2074 | 2 |
| 2.4288 | 2.1843 | 3 |
| 2.3837 | 2.1613 | 4 |
| 2.3503 | 2.1521 | 5 |
| 2.3263 | 2.1407 | 6 |
| 2.3123 | 2.1399 | 7 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ernestumorga/ppo-Pendulum-v1
|
ernestumorga
| 2022-06-07T14:06:23Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"Pendulum-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-07T14:05:48Z |
---
library_name: stable-baselines3
tags:
- Pendulum-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -227.99 +/- 144.65
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pendulum-v1
type: Pendulum-v1
---
# **PPO** Agent playing **Pendulum-v1**
This is a trained model of a **PPO** agent playing **Pendulum-v1**
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 ppo --env Pendulum-v1 -orga ernestumorga -f logs/
python enjoy.py --algo ppo --env Pendulum-v1 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo ppo --env Pendulum-v1 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo ppo --env Pendulum-v1 -f logs/ -orga ernestumorga
```
## Hyperparameters
```python
OrderedDict([('clip_range', 0.2),
('ent_coef', 0.0),
('gae_lambda', 0.95),
('gamma', 0.9),
('learning_rate', 0.001),
('n_envs', 4),
('n_epochs', 10),
('n_steps', 1024),
('n_timesteps', 100000.0),
('policy', 'MlpPolicy'),
('sde_sample_freq', 4),
('use_sde', True),
('normalize', False)])
```
|
huggingtweets/arthur_rimbaud
|
huggingtweets
| 2022-06-07T13:46:36Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-07T13:46:29Z |
---
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/3077349437/46e19fdb6614ff10d09d353a07b75d60_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">Arthur Rimbaud</div>
<div style="text-align: center; font-size: 14px;">@arthur_rimbaud</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 Arthur Rimbaud.
| Data | Arthur Rimbaud |
| --- | --- |
| Tweets downloaded | 423 |
| Retweets | 49 |
| Short tweets | 6 |
| Tweets kept | 368 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1oytr5hf/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 @arthur_rimbaud's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1kk1xq6s) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1kk1xq6s/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/arthur_rimbaud')
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)
|
jppaolim/v59_Large_2E
|
jppaolim
| 2022-06-07T13:01:39Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-07T12:11:41Z |
# My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur is in love with his girlfriend. They go to the beach together. Arthur falls off the beach. Arthur needs medical attention. Arthur gets a broken leg from the fall.
Arthur goes to the beach. Arthur is feeling cold. He looks at the weather report. He knows he needs to get out of the house. He decides to walk to the local beach. Arthur is happy he got out of the house.
Arthur goes to the beach. Arthur always hated going to the beach. His parents always made him go, even if it was just to swim. His father finally convinced him to go to the beach with him. Arthur was not happy, but he had to go anyway. At the beach, Arthur met lots of people he was interested in.
Arthur goes to the beach. Arthur has never been to the beach. His friends tell him that it is very hot. He decides to go to the beach. He enjoys his day at the beach. Now Arthur loves the beach.
Arthur goes to the beach. Arthur is so bored one day. He decides to go to the beach. He sees a nice, sunny beach. Arthur enjoys his day at the beach. Arthur is happy that he found a good day to be bored.
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1.05}
Arthur goes to the beach. Arthur is out on a day of vacation. He decides to take his girlfriend out to the beach. The two surf. They surf all day long. After the sun comes up they relax on a beach blanket.
Arthur goes to the beach. Arthur was feeling very bored one day. He decided he wanted to swim in the ocean. He went to the beach to feel like he was in the ocean. When he got to the beach he was surprised how warm it was. Arthur immediately went back home and went to bed.
Arthur goes to the beach. Arthur has never been to the beach before. He is excited but also nervous about swimming. He boards his car and goes to the ocean. At first he does not like it. However, after a while, he loves the beach.
Arthur goes to the beach. Arthur was planning on going to the beach with friends. Arthur decided that he would go to the beach. When Arthur arrived, there were too many cars for him. Arthur could not see where his friends were. Arthur realized he forgot his sunscreen.
Arthur goes to the beach. Arthur is on vacation. He heads out to the ocean. Arthur spends most of the time swimming. Arthur falls asleep on the beach. He gets up the next day and heads home.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.8, 'repetition_penalty': 1.1}
Arthur goes to the beach. Arthur is going on a trip. He decides to take his girlfriend Mary with him. They decide to go to the beach. When Arthur gets there he realizes that it's too hot. His girlfriend has no choice but to stay home.
Arthur goes to the beach. Arthur is on vacation in the beach. He enjoys taking his swim. However, a storm comes and knocks Arthur's umbrella off of him. Arthur rushes to get it back. He can't swim after that.
Arthur goes to the beach. Arthur had always wanted to go to the beach. He saved up all his money for a trip to the beach. Arthur finally decided to go on vacation. While at the beach he fell in love with the water. When he got home, he was happy he went.
Arthur goes to the beach. Arthur was bored one day so he decided to go to the beach. He got a towel and swimsuit to wear and went out on the water. When Arthur arrived at the beach it was very hot. However, when he stepped into the ocean, it was a beautiful sunny day. Arthur was glad that he chose to spend his day at the beach.
Arthur goes to the beach. Arthur is on a long plane trip. He has been waiting for a very long time to finally go to the beach. Finally the plane lands and Arthur boards the plane. On board he sees beautiful ocean and decides to stay there. After landing he spends the rest of the day relaxing by the water.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.6, 'repetition_penalty': 1.15}
Arthur goes to the beach. Arthur is on a vacation with his family. His family decides to go to the beach. They spend a lot of time at the beach. Arthur has a great day at the beach. He will never forget that trip!
Arthur goes to the beach. Arthur is bored on a rainy day at work. He decides he needs some fun time. He heads out to the ocean. At first Arthur does not like it. However, after a while he finds that the water is very relaxing.
Arthur goes to the beach. Arthur is bored on a Friday night. He decides he would like to go to the beach. He calls his friend and asks him if he wants to come with him. His friend agrees to take Arthur to the beach. They have a great time at the beach.
Arthur goes to the beach. Arthur loved the ocean. One day, he decided to go for a walk on the beach. He walked down the beach and saw many beautiful flowers. Then, he noticed a seagull flying overhead. Arthur went back home and told his mother about the bird.
Arthur goes to the beach. Arthur loved going to the beach. He had a lot of fun at the beach. One day, Arthur went to the beach and got sand in his eyes. Arthur realized that he was not wearing sunscreen. Arthur went home with red spots on his face from the sand.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.4, 'repetition_penalty': 1.2}
Arthur goes to the beach. Arthur was a very happy boy who loved going to the beach. One day, Arthur's mom told him she had an idea for him. She said that he could take his favorite toy and play in the ocean! He went to the beach with his favorite toy and played all day long. Now, Arthur loves the beach just as much as ever.
Arthur goes to the beach. Arthur was a very lazy boy who never did anything. One day his mom took him to the beach. He played in the water and sunbathed for hours. When it was time to go home, he went with his mother. His mom brought him back home and Arthur slept all day!
Arthur goes to the beach. Arthur is bored one day and decides he needs a vacation. He calls his friends up to go with him to the beach. They all agree that it would be fun to spend time together. When they get there, Arthur spends most of his time swimming. He had a great trip at the beach!
Arthur goes to the beach. Arthur is bored one day and decides to go to the beach. He gets his towel, sunscreen and some sunblock. When he arrives at the beach, it's very hot outside. Finally Arthur finds a spot on the sand that isn't so hot. Now Arthur can enjoy the rest of his day!
Arthur goes to the beach. Arthur is bored at home. He decides he needs a change of scenery. He calls his friend and asks if they can go to the beach. His friends agree to go with him. They spend the day playing in the ocean together.
|
nestoralvaro/mt5-base-finetuned-xsum-data_prep_2021_12_26___t55_403.csv___topic_text_google_mt5_base
|
nestoralvaro
| 2022-06-07T12:57:21Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-07T10:31:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-finetuned-xsum-data_prep_2021_12_26___t55_403.csv___topic_text_google_mt5_base
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-data_prep_2021_12_26___t55_403.csv___topic_text_google_mt5_base
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: 0.9647
- Rouge2: 0.1331
- Rougel: 0.9633
- Rougelsum: 0.9627
- Gen Len: 6.4489
## 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 | 36479 | nan | 0.9647 | 0.1331 | 0.9633 | 0.9627 | 6.4489 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
clement-w/PPO-FrozenLakeV1-rlclass
|
clement-w
| 2022-06-07T12:54:22Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"FrozenLake-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-07T12:45:23Z |
---
library_name: stable-baselines3
tags:
- FrozenLake-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 0.80 +/- 0.40
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1
type: FrozenLake-v1
---
# **PPO** Agent playing **FrozenLake-v1**
This is a trained model of a **PPO** agent playing **FrozenLake-v1**
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
...
```
|
RogerKam/roberta_fine_tuned_sentiment_financial_news
|
RogerKam
| 2022-06-07T11:25:35Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-07T11:08:02Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_fine_tuned_sentiment_financial_news
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_fine_tuned_sentiment_financial_news
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6362
- Accuracy: 0.8826
- F1 Score: 0.8865
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.10.0+cu111
- Datasets 2.2.2
- Tokenizers 0.12.1
|
DenisKochetov/q-Taxi-v3_3
|
DenisKochetov
| 2022-06-07T10:49:30Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-07T10:49:20Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3_3
results:
- metrics:
- type: mean_reward
value: -2.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="DenisKochetov/q-Taxi-v3_3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
ThaisBeham/distilbert-base-uncased-finetuned-fira
|
ThaisBeham
| 2022-06-07T10:44:12Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-07T10:04:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-fira
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-fira
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7687
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 200 | 2.9963 |
| No log | 2.0 | 400 | 2.7457 |
| 3.0576 | 3.0 | 600 | 2.7687 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
DenisKochetov/q-FrozenLake-v1-4x4-noSlippery
|
DenisKochetov
| 2022-06-07T10:38:07Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-07T10:37:56Z |
---
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="DenisKochetov/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"])
```
|
nestoralvaro/mt5-base-finetuned-xsum-mlsum___topic_text_google_mt5_base
|
nestoralvaro
| 2022-06-07T09:56:14Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:mlsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-07T05:56:41Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mlsum
metrics:
- rouge
model-index:
- name: mt5-base-finetuned-xsum-mlsum___topic_text_google_mt5_base
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: mlsum
type: mlsum
args: es
metrics:
- name: Rouge1
type: rouge
value: 0.1582
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-finetuned-xsum-mlsum___topic_text_google_mt5_base
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the mlsum dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.1582
- Rouge2: 0.0133
- Rougel: 0.1585
- Rougelsum: 0.1586
- Gen Len: 10.2326
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 66592 | nan | 0.1582 | 0.0133 | 0.1585 | 0.1586 | 10.2326 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
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