modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-31 06:28:41
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 539
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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|---|---|---|---|---|---|---|---|---|---|
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)
|
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
...
```
|
ishansharma1320/wav2vec2-large-xls-r-300m-finetuned-hindi-common-voice-9-0
|
ishansharma1320
| 2022-06-07T20:08:08Z | 103 | 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
| 2022-06-07T09:32:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-finetuned-hindi-common-voice-9-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-finetuned-hindi-common-voice-9-0
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: 0.7392
- Wer: 1.0141
## 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: 4.42184e-05
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 9.2217 | 3.03 | 400 | 4.0314 | 1.0 |
| 3.2902 | 6.06 | 800 | 2.1356 | 1.0001 |
| 0.9858 | 9.09 | 1200 | 0.8566 | 1.0037 |
| 0.5131 | 12.12 | 1600 | 0.7481 | 1.0074 |
| 0.3781 | 15.15 | 2000 | 0.7437 | 1.008 |
| 0.2998 | 18.18 | 2400 | 0.7310 | 1.0162 |
| 0.2553 | 21.21 | 2800 | 0.7384 | 1.0159 |
| 0.2216 | 24.24 | 3200 | 0.7537 | 1.0100 |
| 0.2048 | 27.27 | 3600 | 0.7392 | 1.0141 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 2.2.2
- Tokenizers 0.10.3
|
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"])
```
|
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
|
risethi/distilbert-base-uncased-finetuned-squad
|
risethi
| 2022-06-07T19:32:28Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-07T17:18:28Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: risethi/distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# risethi/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9709
- Validation Loss: 1.1167
- 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': 11064, '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 |
|:----------:|:---------------:|:-----:|
| 1.5123 | 1.1586 | 0 |
| 0.9709 | 1.1167 | 1 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Tokenizers 0.12.1
|
theachyuttiwari/lfqa
|
theachyuttiwari
| 2022-06-07T19:15:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-06-07T09:26:20Z |
---
title: Wikipedia Assistant
emoji: 🌖
colorFrom: green
colorTo: yellow
sdk: streamlit
app_file: app.py
pinned: false
---
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`colorTo`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`sdk`: `streamlit`
Can be either `gradio` or `streamlit`
`sdk_version` : `1.2.0`
Only applicable for `streamlit` SDK.
See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
`app_file`: _string_
Path to your main application file (which contains either `gradio` or `streamlit` Python code).
Path is relative to the root of the repository.
`pinned`: _boolean_
Whether the Space stays on top of your list.
|
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
|
0xrushi/neural-machine-translation-model_1
|
0xrushi
| 2022-06-07T19:02:17Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2022-06-07T19:02:00Z |
---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
akreal/tiny-random-mbart
|
akreal
| 2022-06-07T18:16:58Z | 12,843 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"mbart",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-mbart
Changes: use old format for `pytorch_model.bin`.
|
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
|
memyprokotow/rut5-REBEL-base
|
memyprokotow
| 2022-06-07T17:37:00Z | 30 | 3 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"seq2seq",
"relation-extraction",
"ru",
"dataset:memyprokotow/rebel-dataset-rus",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-01T15:52:53Z |
---
language:
- ru
tags:
- seq2seq
- relation-extraction
- t5
license: apache-2.0
datasets:
- memyprokotow/rebel-dataset-rus
widget:
- text: "За последние 9 месяцев инвесторы в азиатские долларовые долговые обязательства потеряли 155 миллиардов долларов, пострадав от слабости Китая в дополнение к глобальной распродаже фиксированного дохода, наблюдаемой во всем мире по мере роста процентных ставок."
---
# REBEL-ru
Based on russian part of wikipedia (scrapped with CROCODILE).
Model trained for 3 epochs on russian ruT5-base
# How to use
Same code as REBEL-large (https://huggingface.co/Babelscape/rebel-large)
```
text = '''За последние 9 месяцев инвесторы в азиатские долларовые долговые обязательства потеряли 155 миллиардов долларов, пострадав от слабости Китая в дополнение к глобальной распродаже фиксированного дохода, наблюдаемой во всем мире по мере роста процентных ставок. '''
model_path = r"memyprokotow/rut5-REBEL-base"
triplet_extractor = pipeline('text2text-generation', model=model_path,
tokenizer=model_path,
#device=0
)
# We need to use the tokenizer manually since we need special tokens.
extracted_text = triplet_extractor.tokenizer.batch_decode([triplet_extractor(text, return_tensors=True, return_text=False, max_length=500)[0]["generated_token_ids"]])
print(extracted_text[0])
# Function to parse the generated text and extract the triplets
def extract_triplets(text):
triplets = []
relation, subject, relation, object_ = '', '', '', ''
text = text.strip()
current = 'x'
for token in text.replace("<s>", "").replace("<pad>", "").replace("</s>", "").split():
if token == "<triplet>":
current = 't'
if relation != '':
triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
relation = ''
subject = ''
elif token == "<subj>":
current = 's'
if relation != '':
triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
object_ = ''
elif token == "<obj>":
current = 'o'
relation = ''
else:
if current == 't':
subject += ' ' + token
elif current == 's':
object_ += ' ' + token
elif current == 'o':
relation += ' ' + token
if subject != '' and relation != '' and object_ != '':
triplets.append({'head': subject.strip(), 'type': relation.strip(),'tail': object_.strip()})
return triplets
extracted_triplets = extract_triplets(extracted_text[0])
print(extracted_triplets)
```
|
KB/bert-base-swedish-cased-ner
|
KB
| 2022-06-07T16:34:49Z | 14,081 | 7 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"sv",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-07T16:31:50Z |
---
language: sv
---
# Swedish BERT Models
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on approximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and internet forums) aiming to provide a representative BERT model for Swedish text. A more complete description will be published later on.
The following three models are currently available:
- **bert-base-swedish-cased** (*v1*) - A BERT trained with the same hyperparameters as first published by Google.
- **bert-base-swedish-cased-ner** (*experimental*) - a BERT fine-tuned for NER using SUC 3.0.
- **albert-base-swedish-cased-alpha** (*alpha*) - A first attempt at an ALBERT for Swedish.
All models are cased and trained with whole word masking.
## Files
| **name** | **files** |
|---------------------------------|-----------|
| bert-base-swedish-cased | [config](https://s3.amazonaws.com/models.huggingface.co/bert/KB/bert-base-swedish-cased/config.json), [vocab](https://s3.amazonaws.com/models.huggingface.co/bert/KB/bert-base-swedish-cased/vocab.txt), [pytorch_model.bin](https://s3.amazonaws.com/models.huggingface.co/bert/KB/bert-base-swedish-cased/pytorch_model.bin) |
| bert-base-swedish-cased-ner | [config](https://s3.amazonaws.com/models.huggingface.co/bert/KB/bert-base-swedish-cased-ner/config.json), [vocab](https://s3.amazonaws.com/models.huggingface.co/bert/KB/bert-base-swedish-cased-ner/vocab.txt) [pytorch_model.bin](https://s3.amazonaws.com/models.huggingface.co/bert/KB/bert-base-swedish-cased-ner/pytorch_model.bin) |
| albert-base-swedish-cased-alpha | [config](https://s3.amazonaws.com/models.huggingface.co/bert/KB/albert-base-swedish-cased-alpha/config.json), [sentencepiece model](https://s3.amazonaws.com/models.huggingface.co/bert/KB/albert-base-swedish-cased-alpha/spiece.model), [pytorch_model.bin](https://s3.amazonaws.com/models.huggingface.co/bert/KB/albert-base-swedish-cased-alpha/pytorch_model.bin) |
TensorFlow model weights will be released soon.
## Usage requirements / installation instructions
The examples below require Huggingface Transformers 2.4.1 and Pytorch 1.3.1 or greater. For Transformers<2.4.0 the tokenizer must be instantiated manually and the `do_lower_case` flag parameter set to `False` and `keep_accents` to `True` (for ALBERT).
To create an environment where the examples can be run, run the following in an terminal on your OS of choice.
```
# git clone https://github.com/Kungbib/swedish-bert-models
# cd swedish-bert-models
# python3 -m venv venv
# source venv/bin/activate
# pip install --upgrade pip
# pip install -r requirements.txt
```
### BERT Base Swedish
A standard BERT base for Swedish trained on a variety of sources. Vocabulary size is ~50k. Using Huggingface Transformers the model can be loaded in Python as follows:
```python
from transformers import AutoModel,AutoTokenizer
tok = AutoTokenizer.from_pretrained('KB/bert-base-swedish-cased')
model = AutoModel.from_pretrained('KB/bert-base-swedish-cased')
```
### BERT base fine-tuned for Swedish NER
This model is fine-tuned on the SUC 3.0 dataset. Using the Huggingface pipeline the model can be easily instantiated. For Transformer<2.4.1 it seems the tokenizer must be loaded separately to disable lower-casing of input strings:
```python
from transformers import pipeline
nlp = pipeline('ner', model='KB/bert-base-swedish-cased-ner', tokenizer='KB/bert-base-swedish-cased-ner')
nlp('Idag släpper KB tre språkmodeller.')
```
Running the Python code above should produce in something like the result below. Entity types used are `TME` for time, `PRS` for personal names, `LOC` for locations, `EVN` for events and `ORG` for organisations. These labels are subject to change.
```python
[ { 'word': 'Idag', 'score': 0.9998126029968262, 'entity': 'TME' },
{ 'word': 'KB', 'score': 0.9814832210540771, 'entity': 'ORG' } ]
```
The BERT tokenizer often splits words into multiple tokens, with the subparts starting with `##`, for example the string `Engelbert kör Volvo till Herrängens fotbollsklubb` gets tokenized as `Engel ##bert kör Volvo till Herr ##ängens fotbolls ##klubb`. To glue parts back together one can use something like this:
```python
text = 'Engelbert tar Volvon till Tele2 Arena för att titta på Djurgården IF ' +\
'som spelar fotboll i VM klockan två på kvällen.'
l = []
for token in nlp(text):
if token['word'].startswith('##'):
l[-1]['word'] += token['word'][2:]
else:
l += [ token ]
print(l)
```
Which should result in the following (though less cleanly formatted):
```python
[ { 'word': 'Engelbert', 'score': 0.99..., 'entity': 'PRS'},
{ 'word': 'Volvon', 'score': 0.99..., 'entity': 'OBJ'},
{ 'word': 'Tele2', 'score': 0.99..., 'entity': 'LOC'},
{ 'word': 'Arena', 'score': 0.99..., 'entity': 'LOC'},
{ 'word': 'Djurgården', 'score': 0.99..., 'entity': 'ORG'},
{ 'word': 'IF', 'score': 0.99..., 'entity': 'ORG'},
{ 'word': 'VM', 'score': 0.99..., 'entity': 'EVN'},
{ 'word': 'klockan', 'score': 0.99..., 'entity': 'TME'},
{ 'word': 'två', 'score': 0.99..., 'entity': 'TME'},
{ 'word': 'på', 'score': 0.99..., 'entity': 'TME'},
{ 'word': 'kvällen', 'score': 0.54..., 'entity': 'TME'} ]
```
### ALBERT base
The easiest way to do this is, again, using Huggingface Transformers:
```python
from transformers import AutoModel,AutoTokenizer
tok = AutoTokenizer.from_pretrained('KB/albert-base-swedish-cased-alpha'),
model = AutoModel.from_pretrained('KB/albert-base-swedish-cased-alpha')
```
## Acknowledgements ❤️
- Resources from Stockholms University, Umeå University and Swedish Language Bank at Gothenburg University were used when fine-tuning BERT for NER.
- Model pretraining was made partly in-house at the KBLab and partly (for material without active copyright) with the support of Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).
- Models are hosted on S3 by Huggingface 🤗
|
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)
|
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
...
```
|
huggingtweets/aoc-itsjefftiedrich-shaun_vids
|
huggingtweets
| 2022-06-07T12:01:33Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-07T11:43:07Z |
---
language: en
thumbnail: http://www.huggingtweets.com/aoc-itsjefftiedrich-shaun_vids/1654603284413/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1507627313604743171/T8ksXYZu_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1009932396333031424/8FzKlCfB_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/923274881197895680/AbHcStkl_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Shaun & Jeff Tiedrich & Alexandria Ocasio-Cortez</div>
<div style="text-align: center; font-size: 14px;">@aoc-itsjefftiedrich-shaun_vids</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Shaun & Jeff Tiedrich & Alexandria Ocasio-Cortez.
| Data | Shaun | Jeff Tiedrich | Alexandria Ocasio-Cortez |
| --- | --- | --- | --- |
| Tweets downloaded | 3224 | 3249 | 3246 |
| Retweets | 1023 | 11 | 1236 |
| Short tweets | 212 | 713 | 126 |
| Tweets kept | 1989 | 2525 | 1884 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2znx4crj/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @aoc-itsjefftiedrich-shaun_vids's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1q1etxhd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1q1etxhd/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/aoc-itsjefftiedrich-shaun_vids')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
RogerKam/roberta_fine_tuned_sentiment_financial_news
|
RogerKam
| 2022-06-07T11:25:35Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-07T11:08:02Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_fine_tuned_sentiment_financial_news
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_fine_tuned_sentiment_financial_news
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6362
- Accuracy: 0.8826
- F1 Score: 0.8865
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.10.0+cu111
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Sussybaka/gpt2wilkinscoffee
|
Sussybaka
| 2022-06-07T11:01:22Z | 0 | 0 | null |
[
"exbert",
"en",
"dataset:openwebtext",
"arxiv:1910.01108",
"arxiv:2201.08542",
"arxiv:2203.12574",
"arxiv:1910.09700",
"arxiv:1503.02531",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2022-06-07T10:58:10Z |
---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- openwebtext
model-index:
- name: distilgpt2
results:
- task:
type: text-generation
name: Text Generation
dataset:
type: wikitext
name: WikiText-103
metrics:
- type: perplexity
name: Perplexity
value: 21.1
co2_eq_emissions: 149200 g
---
# DistilGPT2
DistilGPT2 (short for Distilled-GPT2) is an English-language model pre-trained with the supervision of the smallest version of Generative Pre-trained Transformer 2 (GPT-2). Like GPT-2, DistilGPT2 can be used to generate text. Users of this model card should also consider information about the design, training, and limitations of [GPT-2](https://huggingface.co/gpt2), And this is a Wilkins-ified Version.
## Model Details
- **Developed by:** Hugging Face
- **Model type:** Transformer-based Language Model
- **Language:** English
- **License:** Apache 2.0
- **Model Description:** DistilGPT2 is an English-language model pre-trained with the supervision of the 124 million parameter version of GPT-2. DistilGPT2, which has 82 million parameters, was developed using [knowledge distillation](#knowledge-distillation) and was designed to be a faster, lighter version of GPT-2.
- **Resources for more information:** See [this repository](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) for more about Distil\* (a class of compressed models including Distilled-GPT2), [Sanh et al. (2019)](https://arxiv.org/abs/1910.01108) for more information about knowledge distillation and the training procedure, and this page for more about [GPT-2](https://openai.com/blog/better-language-models/).
## Uses, Limitations and Risks
#### Limitations and Risks
<details>
<summary>Click to expand</summary>
**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
As the developers of GPT-2 (OpenAI) note in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md), “language models like GPT-2 reflect the biases inherent to the systems they were trained on.” Significant research has explored bias and fairness issues with models for language generation including GPT-2 (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
DistilGPT2 also suffers from persistent bias issues, as highlighted in the demonstrative examples below. Note that these examples are not a comprehensive stress-testing of the model. Readers considering using the model should consider more rigorous evaluations of the model depending on their use case and context.
The impact of model compression techniques – such as knowledge distillation – on bias and fairness issues associated with language models is an active area of research. For example:
- [Silva, Tambwekar and Gombolay (2021)](https://aclanthology.org/2021.naacl-main.189.pdf) find that distilled versions of BERT and RoBERTa consistently exhibit statistically significant bias (with regard to gender and race) with effect sizes larger than the teacher models.
- [Xu and Hu (2022)](https://arxiv.org/pdf/2201.08542.pdf) find that distilled versions of GPT-2 showed consistent reductions in toxicity and bias compared to the teacher model (see the paper for more detail on metrics used to define/measure toxicity and bias).
- [Gupta et al. (2022)](https://arxiv.org/pdf/2203.12574.pdf) find that DistilGPT2 exhibits greater gender disparities than GPT-2 and propose a technique for mitigating gender bias in distilled language models like DistilGPT2.
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='distilgpt2')
>>> set_seed(48)
>>> generator("The White man worked as a", max_length=20, num_return_sequences=3)
[{'generated_text': "The White man worked as a salesman at a McDonald's restaurant called Kia at the time of the"},
{'generated_text': 'The White man worked as a contractor in the Army in the late 1990s. He became a "'},
{'generated_text': 'The White man worked as a police spokesman to the US Navy in the 1930s.'}]
>>> set_seed(48)
>>> generator("The Black man worked as a", max_length=20, num_return_sequences=3)
[{'generated_text': 'The Black man worked as a shop assistant for an hour at Wal-Mart at Wal-Mart in'},
{'generated_text': 'The Black man worked as a waiter in the hotel when he was assaulted when he got out of a'},
{'generated_text': 'The Black man worked as a police spokesman four months ago...'}]
```
</details>
#### Potential Uses
Since DistilGPT2 is a distilled version of GPT-2, it is intended to be used for similar use cases with the increased functionality of being smaller and easier to run than the base model.
The developers of GPT-2 state in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md) that they envisioned GPT-2 would be used by researchers to better understand large-scale generative language models, with possible secondary use cases including:
> - *Writing assistance: Grammar assistance, autocompletion (for normal prose or code)*
> - *Creative writing and art: exploring the generation of creative, fictional texts; aiding creation of poetry and other literary art.*
> - *Entertainment: Creation of games, chat bots, and amusing generations.*
Using DistilGPT2, the Hugging Face team built the [Write With Transformers](https://transformer.huggingface.co/doc/distil-gpt2) web app, which allows users to play with the model to generate text directly from their browser.
#### Out-of-scope Uses
OpenAI states in the GPT-2 [model card](https://github.com/openai/gpt-2/blob/master/model_card.md):
> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true.
>
> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans unless the deployers first carry out a study of biases relevant to the intended use-case.
### How to Get Started with the Model
<details>
<summary>Click to expand</summary>
*Be sure to read the sections on in-scope and out-of-scope uses and limitations of the model for further information on how to use the model.*
Using DistilGPT2 is similar to using GPT-2. DistilGPT2 can be used directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='distilgpt2')
>>> set_seed(42)
>>> generator("Hello, I’m a language model", max_length=20, num_return_sequences=5)
Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.
[{'generated_text': "Hello, I'm a language model, I'm a language model. In my previous post I've"},
{'generated_text': "Hello, I'm a language model, and I'd love to hear what you think about it."},
{'generated_text': "Hello, I'm a language model, but I don't get much of a connection anymore, so"},
{'generated_text': "Hello, I'm a language model, a functional language... It's not an example, and that"},
{'generated_text': "Hello, I'm a language model, not an object model.\n\nIn a nutshell, I"}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
model = GPT2Model.from_pretrained('distilgpt2')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
And in TensorFlow:
```python
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
model = TFGPT2Model.from_pretrained('distilgpt2')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
</details>
## Training Data
DistilGPT2 was trained using [OpenWebTextCorpus](https://skylion007.github.io/OpenWebTextCorpus/), an open-source reproduction of OpenAI’s WebText dataset, which was used to train GPT-2. See the [OpenWebTextCorpus Dataset Card](https://huggingface.co/datasets/openwebtext) for additional information about OpenWebTextCorpus and [Radford et al. (2019)](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf) for additional information about WebText.
## Training Procedure
The texts were tokenized using the same tokenizer as GPT-2, a byte-level version of Byte Pair Encoding (BPE). DistilGPT2 was trained using knowledge distillation, following a procedure similar to the training procedure for DistilBERT, described in more detail in [Sanh et al. (2019)](https://arxiv.org/abs/1910.01108).
## Evaluation Results
The creators of DistilGPT2 [report](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) that, on the [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) benchmark, GPT-2 reaches a perplexity on the test set of 16.3 compared to 21.1 for DistilGPT2 (after fine-tuning on the train set).
## Environmental Impact
*Carbon emissions were estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.*
- **Hardware Type:** 8 16GB V100
- **Hours used:** 168 (1 week)
- **Cloud Provider:** Azure
- **Compute Region:** unavailable, assumed East US for calculations
- **Carbon Emitted** *(Power consumption x Time x Carbon produced based on location of power grid)*: 149.2 kg eq. CO2
## Citation
```bibtex
@inproceedings{sanh2019distilbert,
title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
author={Sanh, Victor and Debut, Lysandre and Chaumond, Julien and Wolf, Thomas},
booktitle={NeurIPS EMC^2 Workshop},
year={2019}
}
```
## Glossary
- <a name="knowledge-distillation">**Knowledge Distillation**</a>: As described in [Sanh et al. (2019)](https://arxiv.org/pdf/1910.01108.pdf), “knowledge distillation is a compression technique in which a compact model – the student – is trained to reproduce the behavior of a larger model – the teacher – or an ensemble of models.” Also see [Bucila et al. (2006)](https://www.cs.cornell.edu/~caruana/compression.kdd06.pdf) and [Hinton et al. (2015)](https://arxiv.org/abs/1503.02531).
<a href="https://huggingface.co/exbert/?model=distilgpt2">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
This is the Wilkins Coffee Version.
|
DenisKochetov/q-Taxi-v3_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"])
```
|
DenisKochetov/q-Taxi-v3_2
|
DenisKochetov
| 2022-06-07T10:47:06Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-07T10:45:23Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3_2
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="DenisKochetov/q-Taxi-v3_2", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
DenisKochetov/q-Taxi-v3
|
DenisKochetov
| 2022-06-07T10:43:08Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-07T10:40:09Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="DenisKochetov/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
prajdabre/morisien_english
|
prajdabre
| 2022-06-07T09:55:36Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-06T11:35:06Z |
---
license: mit
widget:
- text: Kan bann mor pou releve, bann dimoun pa pou marie. </s> <2cr>
---
|
prashanth/IndicBART-ibart-hi-to-en
|
prashanth
| 2022-06-07T09:33:58Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"dataset:hindi_english_machine_translation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-07T09:30:43Z |
---
tags:
- generated_from_trainer
datasets:
- hindi_english_machine_translation
model-index:
- name: IndicBART-ibart-hi-to-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# IndicBART-ibart-hi-to-en
This model is a fine-tuned version of [ai4bharat/IndicBART](https://huggingface.co/ai4bharat/IndicBART) on the hindi_english_machine_translation dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 157 | 4.4208 | 1.0626 | 20.0 |
### Framework versions
- Transformers 4.19.1
- Pytorch 1.11.0+cu102
- Datasets 1.18.0
- Tokenizers 0.12.1
|
sanamoin/wav2vec2-base-timit-demo-google-colab
|
sanamoin
| 2022-06-07T09:13:33Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-02T21:42:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-google-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
spy24/autotrain-expand-parrot-956131825
|
spy24
| 2022-06-07T09:11:04Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"unk",
"dataset:spy24/autotrain-data-expand-parrot",
"co2_eq_emissions",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-07T07:59:01Z |
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- spy24/autotrain-data-expand-parrot
co2_eq_emissions: 0.647019768976749
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 956131825
- CO2 Emissions (in grams): 0.647019768976749
## Validation Metrics
- Loss: 2.330639123916626
- Rouge1: 53.3589
- Rouge2: 40.4273
- RougeL: 48.4928
- RougeLsum: 49.4952
- Gen Len: 18.8741
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/spy24/autotrain-expand-parrot-956131825
```
|
suonbo/bert-finetuned-ner
|
suonbo
| 2022-06-07T07:24:31Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-07T06:43:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9335982778605729
- name: Recall
type: recall
value: 0.9488387748232918
- name: F1
type: f1
value: 0.9411568316501127
- name: Accuracy
type: accuracy
value: 0.9854447518690763
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0637
- Precision: 0.9336
- Recall: 0.9488
- F1: 0.9412
- Accuracy: 0.9854
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0897 | 1.0 | 1756 | 0.0648 | 0.9152 | 0.9408 | 0.9278 | 0.9837 |
| 0.0384 | 2.0 | 3512 | 0.0601 | 0.9277 | 0.9507 | 0.9391 | 0.9859 |
| 0.0201 | 3.0 | 5268 | 0.0637 | 0.9336 | 0.9488 | 0.9412 | 0.9854 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ziq/depression_suggestion
|
ziq
| 2022-06-07T07:18:44Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-07T06:49:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: depression_suggestion
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. -->
# depression_suggestion
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3740
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 70
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 3 | 60.7965 |
| No log | 2.0 | 6 | 60.5778 |
| No log | 3.0 | 9 | 60.1954 |
| No log | 4.0 | 12 | 59.6487 |
| No log | 5.0 | 15 | 58.9372 |
| No log | 6.0 | 18 | 58.0582 |
| No log | 7.0 | 21 | 57.0106 |
| No log | 8.0 | 24 | 55.7910 |
| No log | 9.0 | 27 | 54.3934 |
| No log | 10.0 | 30 | 52.8099 |
| No log | 11.0 | 33 | 51.0219 |
| No log | 12.0 | 36 | 49.0127 |
| No log | 13.0 | 39 | 46.7522 |
| No log | 14.0 | 42 | 44.2033 |
| No log | 15.0 | 45 | 41.3146 |
| No log | 16.0 | 48 | 37.9982 |
| No log | 17.0 | 51 | 34.2236 |
| No log | 18.0 | 54 | 29.8068 |
| No log | 19.0 | 57 | 24.9750 |
| No log | 20.0 | 60 | 20.0707 |
| No log | 21.0 | 63 | 15.5166 |
| No log | 22.0 | 66 | 12.0328 |
| No log | 23.0 | 69 | 9.1012 |
| No log | 24.0 | 72 | 7.2116 |
| No log | 25.0 | 75 | 6.3149 |
| No log | 26.0 | 78 | 5.8127 |
| No log | 27.0 | 81 | 5.4548 |
| No log | 28.0 | 84 | 5.1684 |
| No log | 29.0 | 87 | 4.8927 |
| No log | 30.0 | 90 | 4.6128 |
| No log | 31.0 | 93 | 4.3782 |
| No log | 32.0 | 96 | 4.1996 |
| No log | 33.0 | 99 | 4.0981 |
| No log | 34.0 | 102 | 4.0022 |
| No log | 35.0 | 105 | 3.9224 |
| No log | 36.0 | 108 | 3.8381 |
| No log | 37.0 | 111 | 3.7660 |
| No log | 38.0 | 114 | 3.6887 |
| No log | 39.0 | 117 | 3.6483 |
| No log | 40.0 | 120 | 3.6020 |
| No log | 41.0 | 123 | 3.5590 |
| No log | 42.0 | 126 | 3.5199 |
| No log | 43.0 | 129 | 3.4646 |
| No log | 44.0 | 132 | 3.4098 |
| No log | 45.0 | 135 | 3.3684 |
| No log | 46.0 | 138 | 3.3290 |
| No log | 47.0 | 141 | 3.3113 |
| No log | 48.0 | 144 | 3.3033 |
| No log | 49.0 | 147 | 3.2928 |
| No log | 50.0 | 150 | 3.2776 |
| No log | 51.0 | 153 | 3.2587 |
| No log | 52.0 | 156 | 3.2487 |
| No log | 53.0 | 159 | 3.2390 |
| No log | 54.0 | 162 | 3.2318 |
| No log | 55.0 | 165 | 3.2311 |
| No log | 56.0 | 168 | 3.2377 |
| No log | 57.0 | 171 | 3.2554 |
| No log | 58.0 | 174 | 3.2720 |
| No log | 59.0 | 177 | 3.2781 |
| No log | 60.0 | 180 | 3.2882 |
| No log | 61.0 | 183 | 3.3089 |
| No log | 62.0 | 186 | 3.3352 |
| No log | 63.0 | 189 | 3.3519 |
| No log | 64.0 | 192 | 3.3233 |
| No log | 65.0 | 195 | 3.3028 |
| No log | 66.0 | 198 | 3.3153 |
| No log | 67.0 | 201 | 3.3422 |
| No log | 68.0 | 204 | 3.3753 |
| No log | 69.0 | 207 | 3.4003 |
| No log | 70.0 | 210 | 3.3740 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
bondi/bert-clean-semaphore-prediction-w2
|
bondi
| 2022-06-07T06:55:00Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-07T05:55:06Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-clean-semaphore-prediction-w2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-clean-semaphore-prediction-w2
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0685
- Accuracy: 0.9716
- F1: 0.9715
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
botika/distilbert-base-uncased-finetuned-squad
|
botika
| 2022-06-07T06:36:08Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-06T09:27:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1500
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3149 | 1.0 | 2767 | 1.2079 |
| 1.053 | 2.0 | 5534 | 1.1408 |
| 0.8809 | 3.0 | 8301 | 1.1500 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
KB/ALL-MODELS-MOVED-TO-KBLAB
|
KB
| 2022-06-07T06:34:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-06-07T06:33:24Z |
All models are moved / redirected to [KBLab](https://huggingface.co/KBLab)
|
QuickSilver007/q-Taxi-v3
|
QuickSilver007
| 2022-06-07T05:50:14Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-07T05:50:07Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.50 +/- 2.76
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="QuickSilver007/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"])
```
|
QuickSilver007/q-FrozenLake-v1-4x4-noSlippery
|
QuickSilver007
| 2022-06-07T05:44:52Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-07T05:44:44Z |
---
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="QuickSilver007/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"])
```
|
cutten/wav2vec2-base-timit-demo-google-colab
|
cutten
| 2022-06-07T03:35:57Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-04T13:17:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-base-timit-demo-google-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6342
- Wer: 0.5808
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 9.1358 | 1.19 | 500 | 3.2710 | 1.0 |
| 3.0499 | 2.38 | 1000 | 1.8976 | 1.0 |
| 1.279 | 3.56 | 1500 | 0.7502 | 0.8228 |
| 0.7953 | 4.75 | 2000 | 0.5914 | 0.7343 |
| 0.6451 | 5.94 | 2500 | 0.6152 | 0.7280 |
| 0.5351 | 7.13 | 3000 | 0.5948 | 0.7041 |
| 0.4633 | 8.31 | 3500 | 0.5585 | 0.6712 |
| 0.4272 | 9.5 | 4000 | 0.5372 | 0.6457 |
| 0.3803 | 10.69 | 4500 | 0.5404 | 0.6402 |
| 0.3462 | 11.88 | 5000 | 0.5862 | 0.6484 |
| 0.3302 | 13.06 | 5500 | 0.5991 | 0.6426 |
| 0.3096 | 14.25 | 6000 | 0.5687 | 0.6287 |
| 0.2839 | 15.44 | 6500 | 0.5798 | 0.6384 |
| 0.2701 | 16.63 | 7000 | 0.5775 | 0.6047 |
| 0.2507 | 17.81 | 7500 | 0.5638 | 0.6065 |
| 0.2376 | 19.0 | 8000 | 0.5937 | 0.6094 |
| 0.2264 | 20.19 | 8500 | 0.5944 | 0.6065 |
| 0.2146 | 21.38 | 9000 | 0.6050 | 0.6122 |
| 0.1947 | 22.57 | 9500 | 0.6283 | 0.5992 |
| 0.1982 | 23.75 | 10000 | 0.6126 | 0.6018 |
| 0.1924 | 24.94 | 10500 | 0.6075 | 0.5962 |
| 0.1855 | 26.13 | 11000 | 0.6344 | 0.5938 |
| 0.1839 | 27.32 | 11500 | 0.6118 | 0.5880 |
| 0.1741 | 28.5 | 12000 | 0.6381 | 0.5878 |
| 0.1726 | 29.69 | 12500 | 0.6342 | 0.5808 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
spencerkmarley/distilbert
|
spencerkmarley
| 2022-06-07T03:02:57Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-07T02:28:18Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: spencerkmarley/distilbert
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# spencerkmarley/distilbert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.2904
- Validation Loss: 2.8356
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -949, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.2904 | 2.8356 | 0 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
nestoralvaro/mt5-base-finetuned-xsum-mlsum___summary_text_google_mt5_base
|
nestoralvaro
| 2022-06-07T02:18:15Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:mlsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-06T22:08:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mlsum
metrics:
- rouge
model-index:
- name: mt5-base-finetuned-xsum-mlsum___summary_text_google_mt5_base
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: mlsum
type: mlsum
args: es
metrics:
- name: Rouge1
type: rouge
value: 8.9973
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-finetuned-xsum-mlsum___summary_text_google_mt5_base
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the mlsum dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 8.9973
- Rouge2: 0.9036
- Rougel: 7.6699
- Rougelsum: 7.716
- Gen Len: 10.2326
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 66592 | nan | 8.9973 | 0.9036 | 7.6699 | 7.716 | 10.2326 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/byelihoff
|
huggingtweets
| 2022-06-07T01:08:05Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-06T13:43:11Z |
---
language: en
thumbnail: http://www.huggingtweets.com/byelihoff/1654564001530/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1481727546186211329/U8AeI0cS_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Eli Hoff</div>
<div style="text-align: center; font-size: 14px;">@byelihoff</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Eli Hoff.
| Data | Eli Hoff |
| --- | --- |
| Tweets downloaded | 3248 |
| Retweets | 821 |
| Short tweets | 187 |
| Tweets kept | 2240 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3t22q7l3/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @byelihoff's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3qqqbwen) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3qqqbwen/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/byelihoff')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/sophiadonis10
|
huggingtweets
| 2022-06-07T01:01:18Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-07T00:57:21Z |
---
language: en
thumbnail: http://www.huggingtweets.com/sophiadonis10/1654563613795/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/1475251222802309123/0V1B7h3p_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">Sophia Donis</div>
<div style="text-align: center; font-size: 14px;">@sophiadonis10</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 Sophia Donis.
| Data | Sophia Donis |
| --- | --- |
| Tweets downloaded | 320 |
| Retweets | 113 |
| Short tweets | 5 |
| Tweets kept | 202 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/4gt337he/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 @sophiadonis10's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2u0jynrk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2u0jynrk/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/sophiadonis10')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/heylookaturtle
|
huggingtweets
| 2022-06-07T00:50:23Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-07T00:48:04Z |
---
language: en
thumbnail: http://www.huggingtweets.com/heylookaturtle/1654563018664/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1052029344254701568/2yAQKb6K_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Adam Porter</div>
<div style="text-align: center; font-size: 14px;">@heylookaturtle</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Adam Porter.
| Data | Adam Porter |
| --- | --- |
| Tweets downloaded | 3232 |
| Retweets | 1006 |
| Short tweets | 436 |
| Tweets kept | 1790 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2xiwa2l6/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @heylookaturtle's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/hov36pjn) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/hov36pjn/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/heylookaturtle')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/hopedavistweets
|
huggingtweets
| 2022-06-07T00:48:38Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-07T00:46:24Z |
---
language: en
thumbnail: http://www.huggingtweets.com/hopedavistweets/1654562883505/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1420954294082326529/ZkxWu0ln_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Hope Davis 🪩</div>
<div style="text-align: center; font-size: 14px;">@hopedavistweets</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Hope Davis 🪩.
| Data | Hope Davis 🪩 |
| --- | --- |
| Tweets downloaded | 2707 |
| Retweets | 1812 |
| Short tweets | 100 |
| Tweets kept | 795 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2pkx13m4/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @hopedavistweets's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/objxokv4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/objxokv4/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/hopedavistweets')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
jppaolim/v57_Large_3E
|
jppaolim
| 2022-06-06T23:35:49Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-06T22:55:15Z |
# My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur and his friends go to the beach one day. They go swimming. Then they play volleyball. Arthur is so tired he falls asleep on the beach. Arthur wakes up later and they never go back.
Arthur goes to the beach. Arthur was out surfing. He was having a blast. He got a bit too excited. He got in too much trouble. Arthur left the beach and went home.
Arthur goes to the beach. Arthur is bored at home. He decides to go to the beach. Arthur likes the beach. He enjoys the beach for an hour. Arthur returns home exhausted but happy.
Arthur goes to the beach. Arthur is bored of his suburban life. He decides to take a big trip to the beach. Arthur packs up all his things. He boards the ferry. Arthur takes a nice relaxing stroll on the beach.
Arthur goes to the beach. Arthur was bored. He decided to go to the beach. He got in his car and drove to the beach. At the beach he enjoyed the waves and the sand. Arthur decided to come back the next day.
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1.05}
Arthur goes to the beach. Arthur and his friend wanted to go to the beach. They loaded up the car with beach towels, sunscreen and snacks. Arthur packed a cooler full of drinks and food. They drove to the beach. There was a long line, but they finally got to the beach.
Arthur goes to the beach. Arthur was a sleepy boy. He wanted to play a game but he wasn't very good at it. His mother told him to practice on the weekends. Every weekend he practiced his volleyball game. After a month Arthur became very good at the game.
Arthur goes to the beach. Arthur has been working all day long at his job. He needs a break from work and decides to go to the beach. At the beach he spends a week playing in the sand. He returns home to his family. Arthur is glad that he had a break from work.
Arthur goes to the beach. Arthur is going on a trip to the beach with his friends. He asks for an hour of sleep so he can get ready for the trip. When Arthur wakes up it's dark outside. He rushes to get ready and heads to the beach. Arthur arrives at the beach, exhausted but happy.
Arthur goes to the beach. Arthur is a lonely man. He has been living in the city for Years. One day an older woman passes by. She tells Arthur she misses him. She invites him to go to the beach to make her feel better.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.8, 'repetition_penalty': 1.1}
Arthur goes to the beach. Arthur is feeling very bored on a Saturday afternoon. He decides to go to the beach. He gets in his car and drives to the beach. At the beach, he spends hours playing with his friends. Finally, after a long day of fun, Arthur returns home.
Arthur goes to the beach. Arthur is feeling very bored on a weekend day. He decides that he would like to play in the sand. Arthur spends all morning walking around the beach. At noon he goes into the water and swims for two hours. Now that he has played in the sand, Arthur feels very happy.
Arthur goes to the beach. Arthur loves the ocean. He always wants to get a job in it. One day he gets an amazing job offer. The company hires him for his skills. Now Arthur lives on the beach and loves it.
Arthur goes to the beach. Arthur wanted to go to the beach one sunny day. He packed up his towel and sunscreen before going in the water. Arthur went to the beach and laid out on the sand. He began swimming and having fun for a few hours. When it was time for dinner, Arthur went home with a sunburn.
Arthur goes to the beach. Arthur loves to surf. He asks his friends if they want to go out to the beach. They agree to go. Arthur and his friends go out to the beach. Arthur has a great time surfing at the beach.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.6, 'repetition_penalty': 1.15}
Arthur goes to the beach. Arthur is having a good day at work. He is working on his computer. He gets home and realizes that he forgot to take his sunscreen. He heads to the store and buys some. Now Arthur can't wait for the beach!
Arthur goes to the beach. Arthur is feeling very bored on a Friday evening. He decides he would like to go to the beach. At the beach, Arthur sees many beautiful beaches. However, he cannot find any nice ones that are open all day. Finally, at night, Arthur heads home.
Arthur goes to the beach. Arthur is sitting at home. He decides he wants to go to the beach. He gets in his car and drives to the beach. He spends a day playing in the sand. Finally, he heads back home.
Arthur goes to the beach. Arthur is very sad that his friend won't go to the beach with him. He asks his mom if she can take him but her answer is no. Finally he gets a surprise from his mom. She tells Arthur that he has to go to the beach with him. Arthur spends the whole day at the beach with his friends.
Arthur goes to the beach. Arthur was very happy when he got off work early to go to the beach. He packed his towel and sunscreen, but forgot his umbrella! As he sat on the sand, it began to rain hard. Arthur ran down the beach as fast as he could, but didn't bring his umbrella. When he finally arrived at the beach, he found that it had rained!
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.4, 'repetition_penalty': 1.2}
Arthur goes to the beach. Arthur is going to the beach with his friends. He has never been to the beach before. They all get ready for the trip. When they arrive, Arthur and his friends begin to play in the sand. The beach was a wonderful experience for Arthur.
Arthur goes to the beach. Arthur is feeling very bored one day at home. He decides he would like to go to the beach. At the beach he spends all day playing in the water. When it gets dark Arthur heads back home. Arthur is happy that he went to the beach today.
Arthur goes to the beach. Arthur is sitting at home one day. He decides he would like to go to the beach. He calls his friends and invites them over for a fun day of swimming. They all show up and spend time in the water. It was a great trip to the beach!
Arthur goes to the beach. Arthur is bored at home. He decides he should go to the beach. At the beach, Arthur sees a beautiful sunset. The sunset turns into a full moon. Now Arthur loves the beach even more than at home.
Arthur goes to the beach. Arthur is sitting at home bored out of his mind. He decides he needs something fun to do. He calls up some friends and asks if they want to go to the beach. They all agree that it would be a good idea. The three boys spend the day playing in the ocean.
|
jijo/opus-mt-en-ml-finetuned-en-to-ml
|
jijo
| 2022-06-06T21:58:48Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-30T17:09:58Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: jijo/opus-mt-en-ml-finetuned-en-to-ml
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. -->
# jijo/opus-mt-en-ml-finetuned-en-to-ml
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ml](https://huggingface.co/Helsinki-NLP/opus-mt-en-ml) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.5102
- Validation Loss: 2.2501
- Train Bleu: 3.8750
- Train Gen Len: 20.6042
- 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': 0.0002, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Bleu | Train Gen Len | Epoch |
|:----------:|:---------------:|:----------:|:-------------:|:-----:|
| 2.5102 | 2.2501 | 3.8750 | 20.6042 | 0 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
jplago/bert-finetuned-ner
|
jplago
| 2022-06-06T20:19:07Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-06T19:58:03Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: jplago/bert-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# jplago/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0270
- Validation Loss: 0.0550
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1628 | 0.0660 | 0 |
| 0.0470 | 0.0569 | 1 |
| 0.0270 | 0.0550 | 2 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ett1112/amazon_sentiment_sample_of_1900_with_summary
|
ett1112
| 2022-06-06T19:06:55Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-06T18:56:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: amazon_sentiment_sample_of_1900_with_summary
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. -->
# amazon_sentiment_sample_of_1900_with_summary
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: 0.1062
- Accuracy: 0.9581
- F1: 0.9579
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
miyagawaorj/distilbert-base-uncased-distilled-clinc
|
miyagawaorj
| 2022-06-06T18:42:51Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-06T18:06:58Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9506451612903226
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2466
- Accuracy: 0.9506
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.9383 | 1.0 | 954 | 1.4511 | 0.8397 |
| 0.8485 | 2.0 | 1908 | 0.4733 | 0.9255 |
| 0.2822 | 3.0 | 2862 | 0.3070 | 0.9429 |
| 0.1515 | 4.0 | 3816 | 0.2664 | 0.9490 |
| 0.106 | 5.0 | 4770 | 0.2641 | 0.95 |
| 0.0874 | 6.0 | 5724 | 0.2536 | 0.9510 |
| 0.0764 | 7.0 | 6678 | 0.2475 | 0.9506 |
| 0.0718 | 8.0 | 7632 | 0.2450 | 0.9513 |
| 0.068 | 9.0 | 8586 | 0.2473 | 0.9497 |
| 0.0664 | 10.0 | 9540 | 0.2466 | 0.9506 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.12.1
|
ett1112/amazon_sentiment_sample_of_1900
|
ett1112
| 2022-06-06T18:29:16Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-06T18:19:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: amazon_sentiment_sample_of_1900
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. -->
# amazon_sentiment_sample_of_1900
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: 0.2185
- Accuracy: 0.9162
- F1: 0.9192
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/nonewthing
|
huggingtweets
| 2022-06-06T17:50:00Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-06T17:49:54Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1532336212412977152/TWPqTO8d_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">AI</div>
<div style="text-align: center; font-size: 14px;">@nonewthing</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from AI.
| Data | AI |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 100 |
| Short tweets | 234 |
| Tweets kept | 2913 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/bf84hrrd/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nonewthing's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/169zdg1z) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/169zdg1z/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/nonewthing')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
ubiqtuitin/PPO_CarRacing-v0
|
ubiqtuitin
| 2022-06-06T17:11:06Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-06T17:09:22Z |
---
library_name: stable-baselines3
tags:
- CarRacing-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -82.71 +/- 1.70
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CarRacing-v0
type: CarRacing-v0
---
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
yanekyuk/berturk-uncased-keyword-discriminator
|
yanekyuk
| 2022-06-06T17:09:35Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"tr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-06T15:01:04Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
language:
- tr
widget:
- text: "İngiltere'de düzenlenen Avrupa Tekvando ve Para Tekvando Şampiyonası’nda millî tekvandocular 5 altın, 2 gümüş ve 4 bronz olmak üzere 11, millî para tekvandocular ise 4 altın, 3 gümüş ve 1 bronz olmak üzere 8 madalya kazanarak takım halinde Avrupa şampiyonu oldu."
- text: "Füme somon dedik ama aslında lox salamuralanmış somon anlamına geliyor, füme etme opsiyonel. Lox bagel, 1930'larda Eggs Benedict furyasında New Yorklu Yahudi cemaati tarafından koşer bir alternatif olarak çıkan bir lezzet. Günümüzde benim hangover yüreğim dâhil dünyanın birçok yerinde enfes bir kahvaltı sandviçi."
- text: "Türkiye'de son aylarda sıklıkla tartışılan konut satışı karşılığında yabancılara vatandaşlık verilmesi konusunu beyin göçü kapsamında ele almak mümkün. Daha önce 250 bin dolar olan vatandaşlık bedeli yükselen tepkiler üzerine 400 bin dolara çıkarılmıştı. Türkiye'den göç eden iyi eğitimli kişilerin , gittikleri ülkelerde 250 bin dolar tutarında yabancı yatırıma denk olduğu göz önüne alındığında nitelikli insan gücünün yabancılara konut karşılığında satılan vatandaşlık bedelin eş olduğunu görüyoruz. Yurt dışına giden her bir vatandaşın yüksek teknolojili katma değer üreten sektörlere yapacağı katkılar göz önünde bulundurulduğunda bu açığın inşaat sektörüyle kapatıldığını da görüyoruz. Beyin göçü konusunda sadece ekonomik perspektiften bakıldığında bile kısa vadeli döviz kaynağı yaratmak için kullanılan vatandaşlık satışı yerine beyin göçünü önleyecek önlemler alınmasının ülkemize çok daha faydalı olacağı sonucunu çıkarıyoruz."
- text: "Türkiye’de resmî verilere göre, 15 ve daha yukarı yaştaki kişilerde mevsim etkisinden arındırılmış işsiz sayısı, bu yılın ilk çeyreğinde bir önceki çeyreğe göre 50 bin kişi artarak 3 milyon 845 bin kişi oldu. Mevsim etkisinden arındırılmış işsizlik oranı ise 0,1 puanlık artışla %11,4 seviyesinde gerçekleşti. İşsizlik oranı, ilk çeyrekte geçen yılın aynı çeyreğine göre 1,7 puan azaldı."
- text: "Boeing’in insansız uzay aracı Starliner, birtakım sorunlara rağmen Uluslararası Uzay İstasyonuna (ISS) ulaşarak ilk kez başarılı bir şekilde kenetlendi. Aracın ISS’te beş gün kalmasını takiben sorunsuz bir şekilde New Mexico’ya inmesi halinde Boeing, sonbaharda astronotları yörüngeye göndermek için Starliner’ı kullanabilir.\n\nNeden önemli? NASA’nın personal aracı üretmeyi durdurmasından kaynaklı olarak görevli astronotlar ve kozmonotlar, ISS’te Rusya’nın ürettiği uzay araçları ile taşınıyordu. Starliner’ın kendini kanıtlaması ise bu konuda Rusya’ya olan bağımlılığın potansiyel olarak ortadan kalkabileceği anlamına geliyor."
model-index:
- name: berturk-uncased-keyword-discriminator
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# berturk-uncased-keyword-discriminator
This model is a fine-tuned version of [dbmdz/bert-base-turkish-uncased](https://huggingface.co/dbmdz/bert-base-turkish-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3989
- Precision: 0.6234
- Recall: 0.6508
- Accuracy: 0.9145
- F1: 0.6368
- Ent/precision: 0.6435
- Ent/accuracy: 0.7120
- Ent/f1: 0.6761
- Con/precision: 0.5834
- Con/accuracy: 0.5475
- Con/f1: 0.5649
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------------:|:------------:|:------:|:-------------:|:------------:|:------:|
| 0.2005 | 1.0 | 1875 | 0.2104 | 0.5981 | 0.5978 | 0.9148 | 0.5979 | 0.6280 | 0.6665 | 0.6467 | 0.5383 | 0.4820 | 0.5086 |
| 0.1468 | 2.0 | 3750 | 0.2094 | 0.5996 | 0.6568 | 0.9164 | 0.6269 | 0.6285 | 0.7049 | 0.6645 | 0.5477 | 0.5757 | 0.5614 |
| 0.1124 | 3.0 | 5625 | 0.2372 | 0.6106 | 0.6679 | 0.9154 | 0.6380 | 0.6285 | 0.7270 | 0.6741 | 0.5753 | 0.5684 | 0.5718 |
| 0.0861 | 4.0 | 7500 | 0.2736 | 0.6133 | 0.6707 | 0.9145 | 0.6407 | 0.6281 | 0.7359 | 0.6777 | 0.5830 | 0.5606 | 0.5716 |
| 0.0644 | 5.0 | 9375 | 0.3081 | 0.6115 | 0.6683 | 0.9145 | 0.6386 | 0.6291 | 0.7293 | 0.6755 | 0.5764 | 0.5657 | 0.5710 |
| 0.0498 | 6.0 | 11250 | 0.3449 | 0.6245 | 0.6466 | 0.9149 | 0.6353 | 0.6380 | 0.7097 | 0.6720 | 0.5965 | 0.5401 | 0.5669 |
| 0.0401 | 7.0 | 13125 | 0.3838 | 0.6223 | 0.6545 | 0.9140 | 0.6380 | 0.6449 | 0.7100 | 0.6759 | 0.5790 | 0.5610 | 0.5699 |
| 0.0329 | 8.0 | 15000 | 0.3989 | 0.6234 | 0.6508 | 0.9145 | 0.6368 | 0.6435 | 0.7120 | 0.6761 | 0.5834 | 0.5475 | 0.5649 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
vjeansel/q-Taxi-v3
|
vjeansel
| 2022-06-06T17:02:53Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-06T17:02:48Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="vjeansel/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
KaliYuga/pixelartdiffusion4k
|
KaliYuga
| 2022-06-06T16:55:39Z | 0 | 15 | null |
[
"license:cc-by-3.0",
"region:us"
] | null | 2022-06-06T15:59:40Z |
---
license: cc-by-3.0
---
Unconditional 256x256 Diffusion model trained on ~4100 hand-picked pixel art pieces.\
*Outputs* made with this model may be used however you wish without attribution--although attribution is always nice!
However, if you use this model in your own tool/app/notebook/commercial product/whatever, you MUST credit KaliYuga-ai
and link to my twitter (https://twitter.com/KaliYuga_ai) and Patreon (https://www.patreon.com/kaliyuga_ai) in a non-hidden place. \
Also, if you make bank using this model, feel free to tip me over on Patreon so I can afford to buy my cat the nice cat food :)\
Above all, ENJOY!}
|
dipesh/Intent-Classification-Bert-Base-Cased
|
dipesh
| 2022-06-06T16:43:41Z | 4 | 1 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-27T13:27:06Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: Intent-Classification-Bert-Base-Cased
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Intent-Classification-Bert-Base-Cased
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.9.1
- Datasets 2.2.2
- Tokenizers 0.10.3
|
ubiqtuitin/PPO_CartPole-v1
|
ubiqtuitin
| 2022-06-06T15:48:54Z | 8 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-06T14:58:31Z |
---
library_name: stable-baselines3
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **PPO** Agent playing **CartPole-v1**
This is a trained model of a **PPO** agent playing **CartPole-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
...
```
|
victorlifan/autotrain-song_title_generate-939531516
|
victorlifan
| 2022-06-06T15:36:11Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"unk",
"dataset:victorlifan/autotrain-data-song_title_generate",
"co2_eq_emissions",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-05T21:52:45Z |
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- victorlifan/autotrain-data-song_title_generate
co2_eq_emissions: 11.013963276910237
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 939531516
- CO2 Emissions (in grams): 11.013963276910237
## Validation Metrics
- Loss: 1.1184396743774414
- Rouge1: 54.9539
- Rouge2: 40.7878
- RougeL: 54.8616
- RougeLsum: 54.8682
- Gen Len: 5.1429
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/victorlifan/autotrain-song_title_generate-939531516
```
|
ksabeh/roberta-base-attribute-correction
|
ksabeh
| 2022-06-06T15:33:08Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"roberta",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-06T06:49:17Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: ksabeh/roberta-base-attribute-correction-qa-attribute-correction-qa
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-attribute-correction-qa-attribute-correction-qa
This model is a fine-tuned version of [ksabeh/roberta-base-attribute-correction-qa](https://huggingface.co/ksabeh/roberta-base-attribute-correction-qa) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1296
- Validation Loss: 0.1091
- 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': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 36783, '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 |
|:----------:|:---------------:|:-----:|
| 0.1296 | 0.1091 | 0 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.6.3
- Datasets 2.1.0
- Tokenizers 0.12.1
|
huggingtweets/aksumfootball-geirjordet-slawekmorawski
|
huggingtweets
| 2022-06-06T15:21:53Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-06T15:10:07Z |
---
language: en
thumbnail: http://www.huggingtweets.com/aksumfootball-geirjordet-slawekmorawski/1654528907750/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/1318130998757019649/R8dWYi_b_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/1255843414135975937/9e-_Lg2V_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/1060604477466652675/syszhdwg_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">Geir Jordet & Karl Marius Aksum & Sławek Morawski</div>
<div style="text-align: center; font-size: 14px;">@aksumfootball-geirjordet-slawekmorawski</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 Geir Jordet & Karl Marius Aksum & Sławek Morawski.
| Data | Geir Jordet | Karl Marius Aksum | Sławek Morawski |
| --- | --- | --- | --- |
| Tweets downloaded | 507 | 2778 | 468 |
| Retweets | 47 | 855 | 122 |
| Short tweets | 22 | 137 | 10 |
| Tweets kept | 438 | 1786 | 336 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3s7mtfgq/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 @aksumfootball-geirjordet-slawekmorawski's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/5jtmflz8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/5jtmflz8/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/aksumfootball-geirjordet-slawekmorawski')
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)
|
ubiqtuitin/deeprltutorial1
|
ubiqtuitin
| 2022-06-06T14:30:31Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-06T14:30:07Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -205.33 +/- 79.29
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
...
```
|
q2-jlbar/swin-tiny-patch4-window7-224-finetuned-eurosat
|
q2-jlbar
| 2022-06-06T14:24:15Z | 80 | 0 |
transformers
|
[
"transformers",
"pytorch",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-01T21:36:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9618518518518518
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1199
- Accuracy: 0.9619
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3627 | 0.99 | 47 | 0.1988 | 0.9389 |
| 0.2202 | 1.99 | 94 | 0.1280 | 0.9604 |
| 0.1948 | 2.99 | 141 | 0.1199 | 0.9619 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
galbraun/distilbert-base-uncased-finetuned-cola
|
galbraun
| 2022-06-06T14:20:34Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-06T12:30:33Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5517964161621091
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5277
- Matthews Correlation: 0.5518
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5221 | 1.0 | 535 | 0.5370 | 0.4246 |
| 0.3496 | 2.0 | 1070 | 0.5143 | 0.4892 |
| 0.2378 | 3.0 | 1605 | 0.5277 | 0.5518 |
| 0.1761 | 4.0 | 2140 | 0.7462 | 0.5303 |
| 0.1251 | 5.0 | 2675 | 0.7959 | 0.5414 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
limsc/reqroberta-tapt-epoch50
|
limsc
| 2022-06-06T14:19:05Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-05T23:40:45Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: reqroberta-tapt-epoch50
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# reqroberta-tapt-epoch50
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 37100, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ianspektor/q-FrozenLake-v1-8x8-noSlippery
|
ianspektor
| 2022-06-06T13:58:17Z | 0 | 0 | null |
[
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-06T13:58:11Z |
---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="ianspektor/q-FrozenLake-v1-8x8-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
0xrushi/q-Taxi-v3
|
0xrushi
| 2022-06-06T13:49:28Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-06T13:48:55Z |
---
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="rushic24/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/bigmanbakar
|
huggingtweets
| 2022-06-06T13:49:15Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-06T13:48:15Z |
---
language: en
thumbnail: http://www.huggingtweets.com/bigmanbakar/1654523350313/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/1459686915498819587/cYF4VOWO_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">AbuBakar Siddiq</div>
<div style="text-align: center; font-size: 14px;">@bigmanbakar</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 AbuBakar Siddiq.
| Data | AbuBakar Siddiq |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 452 |
| Short tweets | 769 |
| Tweets kept | 2023 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ggb85vg/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 @bigmanbakar's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1qafbtox) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1qafbtox/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/bigmanbakar')
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)
|
mpsb00/ECHR_test_2
|
mpsb00
| 2022-06-06T11:17:21Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:lex_glue",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-06T10:11:46Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- lex_glue
model-index:
- name: ECHR_test_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ECHR_test_2
This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the lex_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2487
- Macro-f1: 0.4052
- Micro-f1: 0.5660
## 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: 0.001
- 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Macro-f1 | Micro-f1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.2056 | 0.44 | 500 | 0.2846 | 0.3335 | 0.4763 |
| 0.1698 | 0.89 | 1000 | 0.2487 | 0.4052 | 0.5660 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingartists/rammstein
|
huggingartists
| 2022-06-06T11:14:46Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/rammstein",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/rammstein
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/29cedf8dd30a7458f4fca47d1c0f0eab.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Rammstein</div>
<a href="https://genius.com/artists/rammstein">
<div style="text-align: center; font-size: 14px;">@rammstein</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Rammstein.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/rammstein).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/rammstein")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/qt3qa1x1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Rammstein's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2yyigjzv) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2yyigjzv/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/rammstein')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/rammstein")
model = AutoModelWithLMHead.from_pretrained("huggingartists/rammstein")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
lorenzkuhn/distilbert-base-uncased-finetuned-squad
|
lorenzkuhn
| 2022-06-06T10:52:07Z | 34 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-01T13:15:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3206
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2156 | 1.0 | 8235 | 1.1791 |
| 0.9413 | 2.0 | 16470 | 1.2182 |
| 0.7514 | 3.0 | 24705 | 1.3206 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
facebook/data2vec-audio-large-960h
|
facebook
| 2022-06-06T10:36:59Z | 805 | 7 |
transformers
|
[
"transformers",
"pytorch",
"data2vec-audio",
"automatic-speech-recognition",
"speech",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-02T16:01:11Z |
---
language: en
datasets:
- librispeech_asr
tags:
- speech
- hf-asr-leaderboard
license: apache-2.0
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
model-index:
- name: data2vec-audio-large-960h
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (clean)
type: librispeech_asr
config: clean
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 1.89
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (other)
type: librispeech_asr
config: other
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 4.07
---
# Data2Vec-Audio-Large-960h
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The large model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
[Paper](https://arxiv.org/abs/2202.03555)
Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli
**Abstract**
While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.
The original model can be found under https://github.com/pytorch/fairseq/tree/main/examples/data2vec .
# Pre-Training method
For more information, please take a look at the [official paper](https://arxiv.org/abs/2202.03555).
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, Data2VecAudioForCTC
from datasets import load_dataset
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-large-960h")
model = Data2VecAudioForCTC.from_pretrained("facebook/data2vec-audio-large-960h")
# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
# tokenize
input_values = processor(ds[0]["audio"]["array"],, return_tensors="pt", padding="longest").input_values # Batch size 1
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
## Evaluation
This code snippet shows how to evaluate **facebook/data2vec-audio-large-960h** on LibriSpeech's "clean" and "other" test data.
```python
from transformers import Wav2Vec2Processor, Data2VecAudioForCTC
from datasets import load_dataset
import torch
from jiwer import wer
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-large-960h").to("cuda")
model = Data2VecAudioForCTC.from_pretrained("facebook/data2vec-audio-large-960h")
librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
def map_to_pred(batch):
input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values
with torch.no_grad():
logits = model(input_values.to("cuda")).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
batch["transcription"] = transcription
return batch
result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["audio"])
print("WER:", wer(result["text"], result["transcription"]))
```
*Result (WER)*:
| "clean" | "other" |
|---|---|
| 1.89 | 4.07 |
|
huggingartists/elton-john
|
huggingartists
| 2022-06-06T10:32:19Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/elton-john",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/elton-john
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/ec76d346c4c8b057169194c1781021fd.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elton John</div>
<a href="https://genius.com/artists/elton-john">
<div style="text-align: center; font-size: 14px;">@elton-john</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Elton John.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/elton-john).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/elton-john")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/188xpm2n/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Elton John's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1rgstntu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1rgstntu/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/elton-john')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/elton-john")
model = AutoModelWithLMHead.from_pretrained("huggingartists/elton-john")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
imamnurby/rob2rand_merged_w_prefix_c_fc_field
|
imamnurby
| 2022-06-06T09:40:39Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-06T09:38:04Z |
---
tags:
- generated_from_trainer
model-index:
- name: rob2rand_merged_w_prefix_c_fc_field
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rob2rand_merged_w_prefix_c_fc_field
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3
### Framework versions
- Transformers 4.18.0
- Pytorch 1.7.1
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Copninixh/distilbert-base-uncased-finetuned-imdb
|
Copninixh
| 2022-06-06T09:36:09Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-06T09:28:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4897 |
| 2.5796 | 2.0 | 314 | 2.4230 |
| 2.5269 | 3.0 | 471 | 2.4354 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.11.0+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Nawaphong-zax/wangchanberta-base-att-spm-uncased-finetuned-cosme
|
Nawaphong-zax
| 2022-06-06T08:52:29Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"camembert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-06T07:12:12Z |
---
tags:
- generated_from_trainer
model-index:
- name: wangchanberta-base-att-spm-uncased-finetuned-cosme
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wangchanberta-base-att-spm-uncased-finetuned-cosme
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9973
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1386 | 1.0 | 391 | 1.9939 |
| 2.1301 | 2.0 | 782 | 1.9974 |
| 2.1296 | 3.0 | 1173 | 2.0013 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.11.0+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anlausch/aq_bert_gaq_mt
|
anlausch
| 2022-06-06T08:09:38Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-06T07:41:55Z |
---
license: mit
---
Multi-task learning model (flat architecture) trained on GAQCorpus for 4 epochs with a learning rate of 2e-5 (optimised via grid search) in a similar way as in Lauscher et al. 2020 (see below). The original model was Tensorflow-based. This model corresponds to a reimplementation with Transformers & PyTorch.
```
@inproceedings{lauscher-etal-2020-rhetoric,
title = "Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing",
author = "Lauscher, Anne and
Ng, Lily and
Napoles, Courtney and
Tetreault, Joel",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.402",
doi = "10.18653/v1/2020.coling-main.402",
pages = "4563--4574",
abstract = "Though preceding work in computational argument quality (AQ) mostly focuses on assessing overall AQ, researchers agree that writers would benefit from feedback targeting individual dimensions of argumentation theory. However, a large-scale theory-based corpus and corresponding computational models are missing. We fill this gap by conducting an extensive analysis covering three diverse domains of online argumentative writing and presenting GAQCorpus: the first large-scale English multi-domain (community Q{\&}A forums, debate forums, review forums) corpus annotated with theory-based AQ scores. We then propose the first computational approaches to theory-based assessment, which can serve as strong baselines for future work. We demonstrate the feasibility of large-scale AQ annotation, show that exploiting relations between dimensions yields performance improvements, and explore the synergies between theory-based prediction and practical AQ assessment.",
}
```
|
HWJin/SMU-NLP-assignment2-finetuned-best
|
HWJin
| 2022-06-06T08:05:31Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-06T07:55:04Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: HWJin/SMU-NLP-assignment2-finetuned-best
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# HWJin/SMU-NLP-assignment2-finetuned-best
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9936
- Validation Loss: 0.9867
- Epoch: 13
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 990, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 10, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.6490 | 1.2199 | 0 |
| 1.2679 | 1.1622 | 1 |
| 1.1796 | 1.0931 | 2 |
| 1.1200 | 1.0274 | 3 |
| 1.0841 | 1.0739 | 4 |
| 1.0567 | 1.0317 | 5 |
| 1.0164 | 0.9895 | 6 |
| 0.9819 | 1.0365 | 7 |
| 0.9960 | 0.9857 | 8 |
| 1.0143 | 0.9954 | 9 |
| 1.0156 | 1.0173 | 10 |
| 0.9915 | 1.0391 | 11 |
| 1.0246 | 1.0288 | 12 |
| 0.9936 | 0.9867 | 13 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
stephenleejm/T5_yoda_translator
|
stephenleejm
| 2022-06-06T07:01:44Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-24T01:41:29Z |
# Introduction
This model translate between Yoda-ish to English and vice versa. It makes use of the [T5-base](https://huggingface.co/t5-base) model and finetuning.
Basically it trains for 2 tasks using the same dataset. In Yoda-ish to English, trains
# Dataset
For this first version of the model I used a small sample of 20 Yoda quotes for training. I am in the midst of collecting more samples for training.
# Usage
**Input**
For Yoda-ish to English, you can use the prefix "y_to_e: text" to pass in as the input.
For English to Yodaish you can use the prefix "e_to_y: text"
**Output**
The translated sentence.
E.g
e_to_y: I am sick of you => Sick of you, I am
# Spaces
To try this model you can access it [here](https://huggingface.co/spaces/stephenleejm/yoda_translator)
|
Evelyn18/legalectra-base-spanish-finetuned-squad
|
Evelyn18
| 2022-06-06T06:22:06Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:squad_es",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-06T04:29:40Z |
---
tags:
- generated_from_trainer
datasets:
- squad_es
model-index:
- name: legalectra-base-spanish-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# legalectra-base-spanish-finetuned-squad
This model is a fine-tuned version of [mrm8488/legalectra-base-spanish](https://huggingface.co/mrm8488/legalectra-base-spanish) on the squad_es dataset.
It achieves the following results on the evaluation set:
- Loss: 5.9506
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 3 | 5.9506 |
| No log | 2.0 | 6 | 5.9506 |
| No log | 3.0 | 9 | 5.9506 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
mindwrapped/dqn-MountainCar-v0
|
mindwrapped
| 2022-06-06T06:07:52Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-06T06:07:19Z |
---
library_name: stable-baselines3
tags:
- MountainCar-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: -104.89 +/- 20.36
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MountainCar-v0
type: MountainCar-v0
---
# **DQN** Agent playing **MountainCar-v0**
This is a trained model of a **DQN** agent playing **MountainCar-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Chetan1997/layoutlmv2-finetuned-funsd-test
|
Chetan1997
| 2022-06-06T03:20:00Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-06T02:23:11Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2-finetuned-funsd-test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv2-finetuned-funsd-test
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.8.0+cu101
- Datasets 2.2.2
- Tokenizers 0.12.1
|
erfangc/mt5-small-sandbox1
|
erfangc
| 2022-06-06T03:10:37Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-06-06T02:57:26Z |
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-sandbox1
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-small-sandbox1
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 14.5875
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.10.3
|
bondi/bert-semaphore-prediction-w4
|
bondi
| 2022-06-06T02:35:24Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-06T02:34:23Z |
---
tags:
- generated_from_trainer
model-index:
- name: bert-semaphore-prediction-w4
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-semaphore-prediction-w4
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
bondi/bert-semaphore-prediction-w2
|
bondi
| 2022-06-06T02:34:15Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-06T02:33:16Z |
---
tags:
- generated_from_trainer
model-index:
- name: bert-semaphore-prediction-w2
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-semaphore-prediction-w2
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
bondi/bert-semaphore-prediction-w0
|
bondi
| 2022-06-06T02:33:08Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-06T02:32:08Z |
---
tags:
- generated_from_trainer
model-index:
- name: bert-semaphore-prediction-w0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-semaphore-prediction-w0
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
0xrushi/q-FrozenLake-v1-4x4-noSlippery
|
0xrushi
| 2022-06-06T02:14:51Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-06T02:13:07Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="rushic24/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
jppaolim/v55_Large_2E
|
jppaolim
| 2022-06-06T01:24:38Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-06T00:33:50Z |
# My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur is bored and wanted to go the beach. His friends suggest he drive to the beach. Arthur gets a ride and they take off. Arthur takes a nap and has a good time. He has so much fun at the beach he doesn't want to leave.
Arthur goes to the beach. Arthur is feeling very hungry. He decides to go to the beach. Arthur gets some food. Arthur puts his food in his cooler. Arthur goes home and doesn't feel hungry any more.
Arthur goes to the beach. Arthur always wanted to go to the beach. He saved up money so he could take his dream trip. Finally he went to the beach and it was so beautiful. He loved his trip to the beach and decided he would go again. Arthur packed his bags and went to the beach.
Arthur goes to the beach. Arthur went to the beach last weekend. He swam on the sand and looked at the ocean. He saw several people walking around on the beach. Arthur stopped to talk to them. Arthur went home and told his mother about his trip.
Arthur goes to the beach. Arthur is so excited for the weekend. He knows he needs a new bathing suit. He finds the perfect one at the beach. He spends the day relaxing and exploring the shore. Arthur cannot wait for the next trip to the beach.
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1.05}
Arthur goes to the beach. Arthur is playing with his friends in the sand at the beach. His friend Tom comes by and invites him to join them. Arthur loves the beach. Arthur spends the afternoon playing in the sand. Arthur and Tom have a great day at the beach.
Arthur goes to the beach. Arthur was going to the beach. He packed his towel and his sunscreen. He drove his car to the beach. Arthur swam in the ocean. Arthur had fun at the beach.
Arthur goes to the beach. Arthur is bored one day and decides he wants to go to the beach. He packs up his surfboard, towel, and sunscreen. Arthur goes to the ocean and spends the day there. He goes home and tells his mom about his day. Arthur is happy that he took a trip to the beach.
Arthur goes to the beach. Arthur loved the beach. He got his towel and sandals. He went out into the ocean. Arthur was shocked by the cold ocean. He decided he needed to go back home.
Arthur goes to the beach. Arthur really wants to go to the beach. His friend tells him it is too hot out. Arthur convinces his friend to come with him. They drive to the beach. Arthur spends the day playing in the ocean.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.8, 'repetition_penalty': 1.1}
Arthur goes to the beach. Arthur is going to the beach. He has packed his beach towel and sunscreen. Once he gets to the beach he finds a spot to sit down. He relaxes for a while and then swims in the water. Arthur loves the beach!
Arthur goes to the beach. Arthur is very bored. He decides to head to the beach. At the beach he relaxes on the sand. Then he gets out of his car and checks out. Arthur has spent the day at the beach.
Arthur goes to the beach. Arthur had always wanted to visit the ocean. He has saved his money for many Years. Finally he saves up enough money. Arthur takes a trip to the beach. He spends the whole day in the ocean.
Arthur goes to the beach. Arthur was so excited that he had packed his swimming trunks. He was going to the beach and he couldn't wait to swim! When he got to the beach, he saw it was closed for cleaning. He asked his mom if she would take him to the beach anyway. She said yes, but Arthur could have a picnic instead.
Arthur goes to the beach. Arthur is going to the beach with his friends today. He needs a bathing suit but doesn't have one. He decides to go without a bathing suit. When he gets there, he sees that they have a long line. Arthur finally finds a nice one and swims in the water.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.6, 'repetition_penalty': 1.15}
Arthur goes to the beach. Arthur is going on vacation with his family. He asks if they want to go to the beach. They agree and he drives them there. When they get to the beach, Arthur falls in love with a beautiful girl. Arthur and his family spend the rest of their trip together.
Arthur goes to the beach. Arthur is very bored on a hot day. He decides he needs something to do. He heads down to the local beach. He spends all day playing in the sand and sun. Arthur is happy that he no longer feels bored.
Arthur goes to the beach. Arthur was bored one day. He decided to go to the beach. Arthur packed a towel and sunscreen. Then, he went out into the ocean. Arthur had fun at the beach.
Arthur goes to the beach. Arthur was bored at home one day. He decided he would go to the beach. Arthur packed up his car and drove to the beach. Arthur laid on the sand enjoying the sun. Afterwards, Arthur went back home.
Arthur goes to the beach. Arthur was bored one afternoon so he decided to go to the beach. He packed his cooler and drove to the beach. Arthur found a spot on the sand that looked nice. He laid out his towel and sunblock and went for a swim. Arthur had such a great time at the beach!
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.4, 'repetition_penalty': 1.2}
Arthur goes to the beach. Arthur was bored one day and wanted something to do. He decided to go to the beach. At the beach he played in the sand. Then he went swimming in the ocean. Finally, he came back home exhausted but happy.
Arthur goes to the beach. Arthur is bored one day and wants something to do. He decides he would like to go to the beach. Arthur packs up his car and drives to the beach. Once there, he spends a few hours playing in the sand. Afterwards, Arthur has a good time at the beach.
Arthur goes to the beach. Arthur is bored one day and decides to go to the beach. He packs up his towel, swims in the ocean, and gets out of his car. When he arrives at the beach it's very sunny and nice. Arthur spends all day playing in the water. Afterwards, he comes home and rests for a bit.
Arthur goes to the beach. Arthur is bored one day. He decides he needs something to do. He calls his friend Steve and asks if they want to go to the beach. Steve tells Arthur that it's not a good idea to go to the beach. Now Arthur knows that he should have asked Steve for advice.
Arthur goes to the beach. Arthur is bored at home one day. He decides he needs something to do. He heads out to the local beach and plays in the sand. At the beach, Arthur sees many beautiful people. Arthur feels happy that he no longer feels bored.
|
TinySuitStarfish/q-Taxi-v3
|
TinySuitStarfish
| 2022-06-06T00:23:40Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-06T00:23:34Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.48 +/- 2.65
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="TinySuitStarfish/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
kabelomalapane/En-Af
|
kabelomalapane
| 2022-06-05T23:47:35Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-06-05T20:04:44Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: En-Af
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# En-Af
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-af](https://huggingface.co/Helsinki-NLP/opus-mt-en-af) on the None dataset.
It achieves the following results on the evaluation set:
Before training:
- 'eval_bleu': 35.055184951449
- 'eval_loss': 2.225693941116333
After training:
- Loss: 2.0057
- Bleu: 44.2309
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
limsc/reqroberta-tapt-epoch43
|
limsc
| 2022-06-05T23:29:26Z | 6 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-05T23:29:13Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: reqroberta-tapt-epoch43
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# reqroberta-tapt-epoch43
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 37100, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
limsc/reqroberta-tapt-epoch33
|
limsc
| 2022-06-05T23:18:27Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-05T23:18:14Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: reqroberta-tapt-epoch33
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# reqroberta-tapt-epoch33
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 37100, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Xibanya/City
|
Xibanya
| 2022-06-05T22:06:56Z | 0 | 2 | null |
[
"PyTorch",
"Transformers",
"text-to-image",
"ru",
"en",
"license:cc-by-nc-4.0",
"region:us"
] |
text-to-image
| 2022-06-05T07:02:45Z |
---
license: cc-by-nc-4.0
pipeline_tag: text-to-image
tags:
- PyTorch
- Transformers
language:
- ru
- en
---
# ruDALL-E Cities
### Generate illustrations of cities from image prompts and/or text
Finetuned from [Malevich XL](https://huggingface.co/sberbank-ai/rudalle-Malevich) on thousands of anime screenshots of cities. For more information on training, see https://github.com/Xibanya/ru-dalle
<img src="https://huggingface.co/Xibanya/City/resolve/main/Pics/city-examples-sm.png" width="1024" height="1024">
|
poltoran/RL-course-1-unit-ppo-LunarLander-v2-v1
|
poltoran
| 2022-06-05T21:33:23Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-05T21:16:23Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 284.67 +/- 17.68
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Jherb/finetuning-sentiment-model-3000-samples
|
Jherb
| 2022-06-05T21:21:18Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-05T21:00:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666666666667
- name: F1
type: f1
value: 0.8666666666666667
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3063
- Accuracy: 0.8667
- F1: 0.8667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
jppaolim/v54_Large_AdaMW
|
jppaolim
| 2022-06-05T19:38:49Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-05T18:46:40Z |
# My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur was at the beach. His parents got him a towel for the trip. He lay down and got out of the sand. Arthur put on his towel and went to the ocean. He felt very refreshed as he surfed and swam for a bit.
Arthur goes to the beach. Arthur has always been scared to go to the beach. But his friends convinced him to go. Arthur decided to try it. He found the water to be really cold. He turned around and went back to the car.
Arthur goes to the beach. Arthur was very lonely. He decided to go to the beach. He packed his bathing suit and towel. He got ready to go to the beach. Arthur arrived at the beach and relaxed on his chair.
Arthur goes to the beach. Arthur loved to surf and was always looking for new places to surf. He decided to head to the beach with his friends. Arthur drove for hours to find the spot and found it. Arthur and his friends went in and made it their new place. Arthur and his friends spent all day playing in the sun.
Arthur goes to the beach. Arthur really wanted to go to the beach. Arthur was afraid of the cold water. Arthur called a friend for a swim meetup. Arthur met up with his friend. Arthur had a fun time at the beach at the end of the day.
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1.05}
Arthur goes to the beach. Arthur loves to swim. He decides to go swimming at the beach. Arthur gets a towel and a water bottle. He swam all afternoon. At the end of the day, he was soaked!
Arthur goes to the beach. Arthur always wanted to go to the beach. One day his friends told him he had to go. Arthur called the beach and made plans. The next morning he drove to the beach. Arthur had a great time at the beach that day!
Arthur goes to the beach. Arthur was always bored with life. He had no idea where to go on vacation. Arthur decided to go to the beach. He packed up his bag and drove to the beach. Arthur found it so much fun that he left the city.
Arthur goes to the beach. Arthur went to the beach with his friends. They decided to go swimming. Arthur thought it would be fun to jump in the water. He splashed around until the sun was shining in the sky. After the sun came up, Arthur swam out into the ocean.
Arthur goes to the beach. Arthur was feeling lonely one day. He decided to go to the beach. He packed his bag and drove to the beach. He walked to the beach and looked for many people. The people were nice and he met a new friend.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.8, 'repetition_penalty': 1.1}
Arthur goes to the beach. Arthur is going to the beach. His family tells him not to go because they have been looking forward to it. He decides to go anyway. Arthur finds the beach very relaxing. He is glad he went to the beach.
Arthur goes to the beach. Arthur had never been to the beach before. He decided to go one day. Arthur packed a bag of snacks for the trip. He made his way to the beach. When he got there, he found out it was very sunny.
Arthur goes to the beach. Arthur was having a great time at the beach with his family. He was playing in the water when he saw an angry turtle. The turtle had attacked the boat that Arthur was on. Arthur ran away as fast as he could, hoping no one would see him. But then, a huge wave crashed against the shore!
Arthur goes to the beach. Arthur is bored and decides he wants to go to the beach. He arrives at the beach and sets up his tent. He then sets up a chair and a picnic table for himself. Finally, he lays down and gets ready to go. Arthur has a great time at the beach at the end of the day!
Arthur goes to the beach. Arthur always wanted to go to the beach. His friends told him he was too old to go. Finally his parents took him out of school and took him. He drove to the beach and got his sandals and towels ready. When Arthur went to the beach, he realized it was not as bad as he thought.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.6, 'repetition_penalty': 1.15}
Arthur goes to the beach. Arthur was going to go to the beach with his friends. He packed up his things and drove to the beach. When he got there, it was very crowded. Arthur had to wait a long time to get his sandals. Finally, he finally arrived at the beach and played in the water.
Arthur goes to the beach. Arthur was very excited about going on a trip to the beach. He packed up his car and drove to the beach. When he arrived, he saw that it was very crowded. Arthur realized that he had forgotten his sunscreen! Arthur decided not to go to the beach.
Arthur goes to the beach. Arthur was out on a date with his girlfriend. They went to the beach and had fun swimming in the water. Afterwards, they walked around the beach for awhile. After walking, they saw a beautiful sunset. Finally, they left the beach and went home.
Arthur goes to the beach. Arthur was excited for his trip to the beach. He packed up his car and drove out to the beach. Once he got there, Arthur realized it was really hot outside. The air conditioning in his car was broken. Arthur decided to leave without going to the beach.
Arthur goes to the beach. Arthur wanted to go to the beach. He got his friends together and they all went to the beach. They played in the sand for a while then swam in the water. Finally, Arthur was tired but still had fun. Arthur decided he would go back next summer.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.4, 'repetition_penalty': 1.2}
Arthur goes to the beach. Arthur is feeling very bored one day. He decides he needs something to do. He heads out to the beach and finds a spot. He plays in the sand for hours. Finally, he is happy that he no longer feels bored.
Arthur goes to the beach. Arthur was going to go to the beach with his friends. He had never been before but he decided to try it. They all packed up their things and headed out. When they got there, Arthur realized that he forgot his sunscreen! Luckily, his friend brought him a bottle of water so he could use it.
Arthur goes to the beach. Arthur had always wanted to go to the beach. He saved up his money for a week and finally went on vacation. On the day of his trip, he was so excited that he forgot all about work! He spent hours at the beach and even more when he got home. Afterwards, he decided he would never forget to pay attention to work again.
Arthur goes to the beach. Arthur is feeling very tired one day. He decides he needs something to do. He calls his friend and asks him if he wants to go to the beach. His friend says yes. They spend the afternoon playing in the sand.
Arthur goes to the beach. Arthur had always wanted to go to the beach. He saved up for a few months so he could take his trip. Finally, Arthur went to the beach and spent all day playing in the water. Afterwards, he was very tired but happy that he finally got to the beach. The next morning, he decided it would be best to go back home.
|
AlphaZetta/finetuning-sentiment-model-3000-samples
|
AlphaZetta
| 2022-06-05T19:32:45Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-04T18:00:44Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4338
- Accuracy: 0.85
- F1: 0.9189
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
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