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Batsy24/DialoGPT-medium-Twilight_BellaBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
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} | 8 | 2022-08-27T13:27:06Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.fr
split: train
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8367792906370819
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2772
- F1: 0.8368
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.581 | 1.0 | 191 | 0.3798 | 0.7573 |
| 0.2625 | 2.0 | 382 | 0.2806 | 0.8260 |
| 0.1748 | 3.0 | 573 | 0.2772 | 0.8368 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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} | 0 | 2022-08-27T13:39:29Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.it
split: train
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8094848732624693
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2619
- F1: 0.8095
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7908 | 1.0 | 70 | 0.3093 | 0.7437 |
| 0.2824 | 2.0 | 140 | 0.2580 | 0.8015 |
| 0.1834 | 3.0 | 210 | 0.2619 | 0.8095 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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} | 0 | 2022-08-27T13:50:42Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.en
split: train
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.6833890746934226
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4085
- F1: 0.6834
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1943 | 1.0 | 50 | 0.6081 | 0.5020 |
| 0.5325 | 2.0 | 100 | 0.4455 | 0.6193 |
| 0.3915 | 3.0 | 150 | 0.4085 | 0.6834 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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} | 0 | 2022-08-27T13:54:55Z | ---
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/1326222819248791552/u6HtLEIV_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/1478805340212838413/YAJM_fei_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/1516077327981109259/Z4JJ2Pey_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">Sesame Street & Nick Jr. & Nickelodeon</div>
<div style="text-align: center; font-size: 14px;">@nickelodeon-nickjr-sesamestreet</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 Sesame Street & Nick Jr. & Nickelodeon.
| Data | Sesame Street | Nick Jr. | Nickelodeon |
| --- | --- | --- | --- |
| Tweets downloaded | 3250 | 3250 | 3250 |
| Retweets | 746 | 51 | 54 |
| Short tweets | 41 | 754 | 658 |
| Tweets kept | 2463 | 2445 | 2538 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2en4utsq/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 @nickelodeon-nickjr-sesamestreet's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/6x3fqezt) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/6x3fqezt/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/nickelodeon-nickjr-sesamestreet')
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)
|
BatuhanYilmaz/distilbert-base-uncased-finetuned-squad-d5716d28 | [
"pytorch",
"distilbert",
"fill-mask",
"en",
"dataset:squad",
"arxiv:1910.01108",
"transformers",
"question-answering",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | {
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"DistilBertForMaskedLM"
],
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} | 18 | 2022-08-27T14:03:47Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 83.20 +/- 44.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
BatuhanYilmaz/dummy-model | [
"tf",
"camembert",
"fill-mask",
"transformers",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"CamembertForMaskedLM"
],
"model_type": "camembert",
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} | 6 | 2022-08-27T14:08:26Z | ---
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/1326222819248791552/u6HtLEIV_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/1478805340212838413/YAJM_fei_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/1508543786737090570/k9hp_5-2_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">Sesame Street & Nick Jr. & Paramount+</div>
<div style="text-align: center; font-size: 14px;">@nickjr-paramountplus-sesamestreet</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 Sesame Street & Nick Jr. & Paramount+.
| Data | Sesame Street | Nick Jr. | Paramount+ |
| --- | --- | --- | --- |
| Tweets downloaded | 3250 | 3250 | 3250 |
| Retweets | 746 | 51 | 60 |
| Short tweets | 41 | 754 | 40 |
| Tweets kept | 2463 | 2445 | 3150 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3lbv4k51/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 @nickjr-paramountplus-sesamestreet's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/339dkoxu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/339dkoxu/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/nickjr-paramountplus-sesamestreet')
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)
|
BatuhanYilmaz/mt5-small-finetuned-amazonbooks-en-es | [] | null | {
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} | 0 | null | Access to model ronvolutional/anton-l-wav2vec2-random-tiny-classifier is restricted and you are not in the authorized list. Visit https://huggingface.co/ronvolutional/anton-l-wav2vec2-random-tiny-classifier to ask for access. |
Baybars/wav2vec2-xls-r-300m-cv8-turkish | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"tr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"license:apache-2.0"
] | automatic-speech-recognition | {
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} | 5 | 2022-08-27T14:49:16Z | ---
title: Stable Diffusion
emoji: 🏃
colorFrom: red
colorTo: red
sdk: gradio
sdk_version: 3.1.7
app_file: app.py
pinned: false
license: afl-3.0
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
BearThreat/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
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} | 30 | 2022-08-27T15:15:25Z | ---
widget:
- text: " Исмоили Сомонӣ - намояндаи бузурги форсу-тоҷик"
- text: "Ин фурудгоҳ дар кишвари Индонезия қарор дорад."
- text: " Бобоҷон Ғафуров – солҳои 1946-1956"
- text: " Лоиқ Шералӣ дар васфи Модар шеър"
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tajberto-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
config: tg
split: train+test
args: tg
metrics:
- name: Precision
type: precision
value: 0.576
- name: Recall
type: recall
value: 0.6923076923076923
- name: F1
type: f1
value: 0.62882096069869
- name: Accuracy
type: accuracy
value: 0.8934049079754601
---
<!-- 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. -->
# tajberto-ner
This model is a fine-tuned version of [muhtasham/TajBERTo](https://huggingface.co/muhtasham/TajBERTo) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6129
- Precision: 0.576
- Recall: 0.6923
- F1: 0.6288
- Accuracy: 0.8934
## 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: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 2.0 | 50 | 0.6171 | 0.1667 | 0.2885 | 0.2113 | 0.7646 |
| No log | 4.0 | 100 | 0.4733 | 0.2824 | 0.4615 | 0.3504 | 0.8344 |
| No log | 6.0 | 150 | 0.3857 | 0.3372 | 0.5577 | 0.4203 | 0.8589 |
| No log | 8.0 | 200 | 0.4523 | 0.4519 | 0.5865 | 0.5105 | 0.8765 |
| No log | 10.0 | 250 | 0.3870 | 0.44 | 0.6346 | 0.5197 | 0.8834 |
| No log | 12.0 | 300 | 0.4512 | 0.5267 | 0.6635 | 0.5872 | 0.8865 |
| No log | 14.0 | 350 | 0.4934 | 0.4789 | 0.6538 | 0.5528 | 0.8819 |
| No log | 16.0 | 400 | 0.4924 | 0.4783 | 0.6346 | 0.5455 | 0.8842 |
| No log | 18.0 | 450 | 0.5355 | 0.4595 | 0.6538 | 0.5397 | 0.8788 |
| 0.1682 | 20.0 | 500 | 0.5440 | 0.5547 | 0.6827 | 0.6121 | 0.8942 |
| 0.1682 | 22.0 | 550 | 0.5299 | 0.5794 | 0.7019 | 0.6348 | 0.9003 |
| 0.1682 | 24.0 | 600 | 0.5735 | 0.5691 | 0.6731 | 0.6167 | 0.8926 |
| 0.1682 | 26.0 | 650 | 0.6027 | 0.5833 | 0.6731 | 0.6250 | 0.8796 |
| 0.1682 | 28.0 | 700 | 0.6119 | 0.568 | 0.6827 | 0.6201 | 0.8934 |
| 0.1682 | 30.0 | 750 | 0.6098 | 0.5635 | 0.6827 | 0.6174 | 0.8911 |
| 0.1682 | 32.0 | 800 | 0.6237 | 0.5469 | 0.6731 | 0.6034 | 0.8834 |
| 0.1682 | 34.0 | 850 | 0.6215 | 0.5530 | 0.7019 | 0.6186 | 0.8842 |
| 0.1682 | 36.0 | 900 | 0.6179 | 0.5802 | 0.7308 | 0.6468 | 0.8888 |
| 0.1682 | 38.0 | 950 | 0.6201 | 0.5373 | 0.6923 | 0.6050 | 0.8873 |
| 0.0007 | 40.0 | 1000 | 0.6114 | 0.5952 | 0.7212 | 0.6522 | 0.8911 |
| 0.0007 | 42.0 | 1050 | 0.6073 | 0.5625 | 0.6923 | 0.6207 | 0.8896 |
| 0.0007 | 44.0 | 1100 | 0.6327 | 0.5620 | 0.6538 | 0.6044 | 0.8896 |
| 0.0007 | 46.0 | 1150 | 0.6129 | 0.576 | 0.6923 | 0.6288 | 0.8934 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Beatriz/model_name | [] | null | {
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} | 0 | 2022-08-27T15:27:16Z | ---
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tajroberto-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
config: tg
split: train+test
args: tg
metrics:
- name: Precision
type: precision
value: 0.3155080213903743
- name: Recall
type: recall
value: 0.5673076923076923
- name: F1
type: f1
value: 0.4054982817869416
- name: Accuracy
type: accuracy
value: 0.83597621407334
---
<!-- 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. -->
# tajroberto-ner
This model is a fine-tuned version of [muhtasham/RoBERTa-tg](https://huggingface.co/muhtasham/RoBERTa-tg) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9408
- Precision: 0.3155
- Recall: 0.5673
- F1: 0.4055
- Accuracy: 0.8360
## 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: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 2.0 | 50 | 0.7710 | 0.0532 | 0.1827 | 0.0824 | 0.6933 |
| No log | 4.0 | 100 | 0.5901 | 0.0847 | 0.25 | 0.1265 | 0.7825 |
| No log | 6.0 | 150 | 0.5226 | 0.2087 | 0.4615 | 0.2874 | 0.8186 |
| No log | 8.0 | 200 | 0.5041 | 0.2585 | 0.5096 | 0.3430 | 0.8449 |
| No log | 10.0 | 250 | 0.5592 | 0.2819 | 0.5096 | 0.3630 | 0.8499 |
| No log | 12.0 | 300 | 0.5725 | 0.3032 | 0.5481 | 0.3904 | 0.8558 |
| No log | 14.0 | 350 | 0.6433 | 0.3122 | 0.5673 | 0.4027 | 0.8508 |
| No log | 16.0 | 400 | 0.6744 | 0.3543 | 0.5962 | 0.4444 | 0.8553 |
| No log | 18.0 | 450 | 0.7617 | 0.3353 | 0.5577 | 0.4188 | 0.8335 |
| 0.2508 | 20.0 | 500 | 0.7608 | 0.3262 | 0.5865 | 0.4192 | 0.8419 |
| 0.2508 | 22.0 | 550 | 0.8483 | 0.3224 | 0.5673 | 0.4111 | 0.8494 |
| 0.2508 | 24.0 | 600 | 0.8370 | 0.3275 | 0.5385 | 0.4073 | 0.8439 |
| 0.2508 | 26.0 | 650 | 0.8652 | 0.3410 | 0.5673 | 0.4260 | 0.8394 |
| 0.2508 | 28.0 | 700 | 0.9441 | 0.3409 | 0.5769 | 0.4286 | 0.8216 |
| 0.2508 | 30.0 | 750 | 0.9228 | 0.3333 | 0.5577 | 0.4173 | 0.8439 |
| 0.2508 | 32.0 | 800 | 0.9175 | 0.3430 | 0.5673 | 0.4275 | 0.8355 |
| 0.2508 | 34.0 | 850 | 0.9603 | 0.3073 | 0.5288 | 0.3887 | 0.8340 |
| 0.2508 | 36.0 | 900 | 0.9417 | 0.3240 | 0.5577 | 0.4099 | 0.8370 |
| 0.2508 | 38.0 | 950 | 0.9408 | 0.3155 | 0.5673 | 0.4055 | 0.8360 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Bee-Garbs/DialoGPT-real-cartman-small | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 10 | 2022-08-27T15:51:58Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-small-finetuned-glue-rte
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: rte
split: train
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.631768953068592
---
<!-- 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-small-finetuned-glue-rte
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8715
- Accuracy: 0.6318
## 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
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 2.62 | 50 | 1.8285 | 0.6318 |
| No log | 5.26 | 100 | 2.0806 | 0.6462 |
| No log | 7.87 | 150 | 2.1598 | 0.6282 |
| No log | 10.51 | 200 | 2.2774 | 0.6318 |
| No log | 13.15 | 250 | 2.3676 | 0.6245 |
| No log | 15.77 | 300 | 2.4581 | 0.6462 |
| No log | 18.41 | 350 | 2.6175 | 0.6354 |
| No log | 21.05 | 400 | 2.6697 | 0.6354 |
| No log | 23.67 | 450 | 2.7717 | 0.6354 |
| 0.0101 | 26.31 | 500 | 2.7975 | 0.6462 |
| 0.0101 | 28.92 | 550 | 2.8532 | 0.6390 |
| 0.0101 | 31.56 | 600 | 2.9054 | 0.6209 |
| 0.0101 | 34.21 | 650 | 2.8715 | 0.6318 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-gauss-wo-outliers
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. -->
# recipe-gauss-wo-outliers
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2885
- Rmse: 0.5371
- Mse: 0.2885
- Mae: 0.4213
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|
| 0.2768 | 1.0 | 1245 | 0.2747 | 0.5241 | 0.2747 | 0.4081 |
| 0.2737 | 2.0 | 2490 | 0.2793 | 0.5285 | 0.2793 | 0.4288 |
| 0.2722 | 3.0 | 3735 | 0.2792 | 0.5284 | 0.2792 | 0.4332 |
| 0.2703 | 4.0 | 4980 | 0.2770 | 0.5263 | 0.2770 | 0.4000 |
| 0.2682 | 5.0 | 6225 | 0.2758 | 0.5252 | 0.2758 | 0.4183 |
| 0.2658 | 6.0 | 7470 | 0.2792 | 0.5284 | 0.2792 | 0.4212 |
| 0.2631 | 7.0 | 8715 | 0.2769 | 0.5262 | 0.2769 | 0.4114 |
| 0.2599 | 8.0 | 9960 | 0.2802 | 0.5294 | 0.2802 | 0.4107 |
| 0.2572 | 9.0 | 11205 | 0.2885 | 0.5371 | 0.2885 | 0.4213 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Beri/legal-qa | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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} | 10 | null | # Testing whether a sentence is consistent with the CREAK dataset
This framework is trained on the [CREAK dataset](https://arxiv.org/abs/2109.01653).
# Install
pip install creak-sense
# Example
```python
from creak_sense import CreakSense
sense = CreakSense("fractalego/creak-sense")
claim = "Bananas can be found in a grocery list"
sense.make_sense(claim)
```
with output "True".
# Example with explanation
```python
from creak_sense import CreakSense
sense = CreakSense("fractalego/creak-sense")
claim = "Bananas can be found in a grocery list"
sense.get_reason(claim)
```
with output "Bananas are a staple food".
|
Bharathdamu/wav2vec2-large-xls-r-300m-hindi-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
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}
} | 4 | 2022-08-27T18:24:26Z | ---
tags:
- Pong-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pong-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-PLE-v0
type: Pong-PLE-v0
metrics:
- type: mean_reward
value: -16.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pong-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pong-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
Bharathdamu/wav2vec2-large-xls-r-300m-hindi3-colab | [] | null | {
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} | 0 | 2022-08-27T18:37:20Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9174193548387096
---
<!-- 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-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: 0.7711
- Accuracy: 0.9174
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2892 | 1.0 | 318 | 3.2830 | 0.7426 |
| 2.627 | 2.0 | 636 | 1.8728 | 0.8410 |
| 1.5429 | 3.0 | 954 | 1.1555 | 0.8913 |
| 1.0089 | 4.0 | 1272 | 0.8530 | 0.9126 |
| 0.7939 | 5.0 | 1590 | 0.7711 | 0.9174 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Bhumika/roberta-base-finetuned-sst2 | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
} | 85 | 2022-08-27T19:08:51Z | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/Infill2")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/Infill2")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
```
```
prompt = """few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep]"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
```
prompt = """few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep]"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
Infill / Infilling / Masking / Phrase Masking
```
his contention [blank] by the evidence [sep] was refuted [answer]
***
few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer]
***
when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer]
***
```
```
original: Other film stars to have appeared in Scrubs include Heather Graham, while Friends actor Matthew Perry has guest-starred and directed an episode of the [MASK] star, who recently played the title role in historical blockbuster Alexander, will make a cameo appearance as an unruly Irishman. Its leading star, Zach Braff, has recently [MASK] the big screen in Garden State, which he also directed. Farrell is pencilled in to [MASK] of Crockett in a film version of 1980s police [MASK] Farrell's appearance is said to be a result of his friendship with Zach Braff, who stars in the programme.
infill: Other film stars to have appeared in Scrubs include Heather Graham, while Friends actor Matthew Perry has guest-starred and directed an episode of the show. The film star, who recently played the title role in historical blockbuster Alexander, will make a cameo appearance as an unruly Irishman. Its leading star, Zach Braff, has recently been seen on the big screen in Garden State, which he also directed. Farrell is pencilled in to play the role of Crockett in a film version of 1980s police drama Miami Vice. Farrell's appearance is said to be a result of his friendship with Zach Braff, who stars in the programme.
``` |
Bhuvana/t5-base-spellchecker | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
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"min_length": 30,
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"prefix": "summarize: "
},
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},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 93 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr2e05-wd0.01-bs16
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. -->
# recipe-lr2e05-wd0.01-bs16
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2792
- Rmse: 0.5284
- Mse: 0.2792
- Mae: 0.4332
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2768 | 1.0 | 1245 | 0.2747 | 0.5241 | 0.2747 | 0.4081 |
| 0.2737 | 2.0 | 2490 | 0.2793 | 0.5285 | 0.2793 | 0.4288 |
| 0.2722 | 3.0 | 3735 | 0.2792 | 0.5284 | 0.2792 | 0.4332 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Bia18/Beatriz | [] | null | {
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}
} | 0 | 2022-08-27T19:12:10Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 252.72 +/- 21.52
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
...
```
|
BigSalmon/BertaMyWorda | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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},
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}
}
} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr2e05-wd0.005-bs16
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. -->
# recipe-lr2e05-wd0.005-bs16
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2780
- Rmse: 0.5272
- Mse: 0.2780
- Mae: 0.4314
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.277 | 1.0 | 1245 | 0.2743 | 0.5237 | 0.2743 | 0.4112 |
| 0.2738 | 2.0 | 2490 | 0.2811 | 0.5302 | 0.2811 | 0.4288 |
| 0.2724 | 3.0 | 3735 | 0.2780 | 0.5272 | 0.2780 | 0.4314 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/DaBlank | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 4 | 2022-08-27T19:44:53Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr2e05-wd0.1-bs16
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. -->
# recipe-lr2e05-wd0.1-bs16
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2783
- Rmse: 0.5275
- Mse: 0.2783
- Mae: 0.4319
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2771 | 1.0 | 1245 | 0.2744 | 0.5238 | 0.2744 | 0.4105 |
| 0.2738 | 2.0 | 2490 | 0.2819 | 0.5309 | 0.2819 | 0.4298 |
| 0.2724 | 3.0 | 3735 | 0.2783 | 0.5275 | 0.2783 | 0.4319 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/Flowberta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
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},
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}
} | 13 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr2e05-wd0.02-bs16
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. -->
# recipe-lr2e05-wd0.02-bs16
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2780
- Rmse: 0.5272
- Mse: 0.2780
- Mae: 0.4313
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.277 | 1.0 | 1245 | 0.2743 | 0.5237 | 0.2743 | 0.4111 |
| 0.2738 | 2.0 | 2490 | 0.2814 | 0.5305 | 0.2814 | 0.4294 |
| 0.2725 | 3.0 | 3735 | 0.2780 | 0.5272 | 0.2780 | 0.4313 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/FormalBerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
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}
} | 10 | 2022-08-27T20:04:16Z | ---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-average-prompt-a-loob
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8641666666666666
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6443850267379679
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6468842729970327
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7137298499166204
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.898
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.543859649122807
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5833333333333334
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9153231881874341
- name: F1 (macro)
type: f1_macro
value: 0.910194305368961
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.854225352112676
- name: F1 (macro)
type: f1_macro
value: 0.6939611644499436
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6603466955579632
- name: F1 (macro)
type: f1_macro
value: 0.6449027403702262
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9617444529456771
- name: F1 (macro)
type: f1_macro
value: 0.8891323512830197
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.902851770604826
- name: F1 (macro)
type: f1_macro
value: 0.9021609534307928
---
# relbert/roberta-large-semeval2012-average-prompt-a-loob
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-a-loob/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.6443850267379679
- Accuracy on SAT: 0.6468842729970327
- Accuracy on BATS: 0.7137298499166204
- Accuracy on U2: 0.543859649122807
- Accuracy on U4: 0.5833333333333334
- Accuracy on Google: 0.898
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-a-loob/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9153231881874341
- Micro F1 score on CogALexV: 0.854225352112676
- Micro F1 score on EVALution: 0.6603466955579632
- Micro F1 score on K&H+N: 0.9617444529456771
- Micro F1 score on ROOT09: 0.902851770604826
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-a-loob/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8641666666666666
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-semeval2012-average-prompt-a-loob")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity
- template_mode: manual
- template: Today, I finally discovered the relation between <subj> and <obj> : <subj> is the <mask> of <obj>
- loss_function: info_loob
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 22
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-a-loob/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
BigSalmon/FormalRobertaa | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"RobertaForMaskedLM"
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} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr8e06-wd0.01-bs16
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. -->
# recipe-lr8e06-wd0.01-bs16
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2795
- Rmse: 0.5286
- Mse: 0.2795
- Mae: 0.4342
## 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: 8e-06
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2767 | 1.0 | 1245 | 0.2745 | 0.5239 | 0.2745 | 0.4140 |
| 0.2741 | 2.0 | 2490 | 0.2760 | 0.5254 | 0.2760 | 0.4222 |
| 0.2729 | 3.0 | 3735 | 0.2795 | 0.5286 | 0.2795 | 0.4342 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/FroBurta | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr8e06-wd0.005-bs16
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. -->
# recipe-lr8e06-wd0.005-bs16
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2794
- Rmse: 0.5286
- Mse: 0.2794
- Mae: 0.4342
## 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: 8e-06
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2767 | 1.0 | 1245 | 0.2745 | 0.5239 | 0.2745 | 0.4140 |
| 0.2741 | 2.0 | 2490 | 0.2760 | 0.5253 | 0.2760 | 0.4222 |
| 0.2729 | 3.0 | 3735 | 0.2794 | 0.5286 | 0.2794 | 0.4342 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/GPT2HardArticleEasyArticle | [
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 7 | 2022-08-27T20:39:41Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-small-finetuned-keyword-to-text-generation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-keyword-to-text-generation
This model is a fine-tuned version of [caffsean/t5-small-finetuned-keyword-to-text-generation](https://huggingface.co/caffsean/t5-small-finetuned-keyword-to-text-generation) 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 188 | 3.8742 | 0.5567 | 0.0851 | 0.4968 | 0.4972 | 16.243 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/GPT2HardandEasy | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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} | 9 | 2022-08-27T20:55:54Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
model-index:
- name: distilbert-base-uncased-distilled-clinc
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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
BigSalmon/GPTHeHe | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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}
} | 8 | 2022-08-27T20:55:55Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr8e06-wd0.1-bs16
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. -->
# recipe-lr8e06-wd0.1-bs16
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2795
- Rmse: 0.5287
- Mse: 0.2795
- Mae: 0.4342
## 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: 8e-06
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2767 | 1.0 | 1245 | 0.2745 | 0.5239 | 0.2745 | 0.4140 |
| 0.2741 | 2.0 | 2490 | 0.2760 | 0.5253 | 0.2760 | 0.4222 |
| 0.2729 | 3.0 | 3735 | 0.2795 | 0.5287 | 0.2795 | 0.4342 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/GPTIntro | [] | null | {
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} | 0 | 2022-08-27T21:03:22Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9748148148148148
---
<!-- 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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0741
- Accuracy: 0.9748
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2868 | 1.0 | 190 | 0.1234 | 0.9574 |
| 0.1519 | 2.0 | 380 | 0.0741 | 0.9748 |
| 0.1211 | 3.0 | 570 | 0.0724 | 0.9744 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/GPTNeo350MInformalToFormalLincoln | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
"conversational": {
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}
} | 8 | 2022-08-27T21:06:22Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eurlex
model-index:
- name: bert-small-eurlex
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-small-eurlex
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the eurlex dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4260
## 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: 10
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 80
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9536 | 1.5 | 1000 | 2.0670 |
| 2.0331 | 3.0 | 2000 | 1.7540 |
| 1.8046 | 4.5 | 3000 | 1.5993 |
| 1.678 | 6.0 | 4000 | 1.5039 |
| 1.6074 | 7.5 | 5000 | 1.4544 |
| 1.5664 | 8.99 | 6000 | 1.4260 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/GPTNeo350MInformalToFormalLincoln2 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
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} | 8 | 2022-08-27T21:13:42Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr8e06-wd0.02-bs16
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. -->
# recipe-lr8e06-wd0.02-bs16
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2795
- Rmse: 0.5287
- Mse: 0.2795
- Mae: 0.4342
## 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: 8e-06
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2767 | 1.0 | 1245 | 0.2745 | 0.5239 | 0.2745 | 0.4140 |
| 0.2741 | 2.0 | 2490 | 0.2760 | 0.5254 | 0.2760 | 0.4222 |
| 0.2729 | 3.0 | 3735 | 0.2795 | 0.5287 | 0.2795 | 0.4342 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/GPTNeo350MInformalToFormalLincoln3 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
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} | 10 | 2022-08-27T21:31:43Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr1e05-wd0.01-bs16
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. -->
# recipe-lr1e05-wd0.01-bs16
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2793
- Rmse: 0.5285
- Mse: 0.2793
- Mae: 0.4342
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2767 | 1.0 | 1245 | 0.2744 | 0.5239 | 0.2744 | 0.4124 |
| 0.2739 | 2.0 | 2490 | 0.2757 | 0.5251 | 0.2757 | 0.4212 |
| 0.2727 | 3.0 | 3735 | 0.2793 | 0.5285 | 0.2793 | 0.4342 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/GPTNeo350MInformalToFormalLincoln4 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
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"GPTNeoForCausalLM"
],
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}
} | 11 | null | ---
tags:
- Pong-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PongPolGrad
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-PLE-v0
type: Pong-PLE-v0
metrics:
- type: mean_reward
value: -16.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pong-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pong-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
BigSalmon/GPTNeo350MInformalToFormalLincoln5 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
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}
} | 11 | 2022-08-27T21:49:30Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr1e05-wd0.005-bs16
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. -->
# recipe-lr1e05-wd0.005-bs16
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2793
- Rmse: 0.5285
- Mse: 0.2793
- Mae: 0.4342
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2767 | 1.0 | 1245 | 0.2744 | 0.5239 | 0.2744 | 0.4125 |
| 0.2739 | 2.0 | 2490 | 0.2757 | 0.5250 | 0.2757 | 0.4212 |
| 0.2727 | 3.0 | 3735 | 0.2793 | 0.5285 | 0.2793 | 0.4342 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/InfillFormalLincoln | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
],
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"max_length": 50
},
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}
} | 8 | 2022-08-27T22:07:17Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr1e05-wd0.1-bs16
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. -->
# recipe-lr1e05-wd0.1-bs16
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2794
- Rmse: 0.5286
- Mse: 0.2794
- Mae: 0.4343
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2767 | 1.0 | 1245 | 0.2744 | 0.5239 | 0.2744 | 0.4124 |
| 0.2739 | 2.0 | 2490 | 0.2757 | 0.5250 | 0.2757 | 0.4211 |
| 0.2727 | 3.0 | 3735 | 0.2794 | 0.5286 | 0.2794 | 0.4343 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/InformalToFormalLincoln14 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr1e05-wd0.02-bs16
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. -->
# recipe-lr1e05-wd0.02-bs16
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2793
- Rmse: 0.5285
- Mse: 0.2793
- Mae: 0.4342
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2767 | 1.0 | 1245 | 0.2744 | 0.5239 | 0.2744 | 0.4125 |
| 0.2739 | 2.0 | 2490 | 0.2757 | 0.5250 | 0.2757 | 0.4212 |
| 0.2727 | 3.0 | 3735 | 0.2793 | 0.5285 | 0.2793 | 0.4342 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/InformalToFormalLincoln15 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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} | 11 | 2022-08-27T22:42:52Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr2e05-wd0.01-bs8
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. -->
# recipe-lr2e05-wd0.01-bs8
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2765
- Rmse: 0.5259
- Mse: 0.2765
- Mae: 0.4240
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2771 | 1.0 | 2490 | 0.2743 | 0.5237 | 0.2743 | 0.4175 |
| 0.2739 | 2.0 | 4980 | 0.2801 | 0.5292 | 0.2801 | 0.4307 |
| 0.2723 | 3.0 | 7470 | 0.2765 | 0.5259 | 0.2765 | 0.4240 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/InformalToFormalLincoln17 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
],
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} | 12 | 2022-08-27T23:07:51Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr2e05-wd0.005-bs8
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. -->
# recipe-lr2e05-wd0.005-bs8
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2771
- Rmse: 0.5264
- Mse: 0.2771
- Mae: 0.4266
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.277 | 1.0 | 2490 | 0.2746 | 0.5240 | 0.2746 | 0.4202 |
| 0.274 | 2.0 | 4980 | 0.2827 | 0.5317 | 0.2827 | 0.4360 |
| 0.2723 | 3.0 | 7470 | 0.2771 | 0.5264 | 0.2771 | 0.4266 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/InformalToFormalLincoln19 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
],
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} | 11 | 2022-08-27T23:29:01Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-base-finetuned-keyword-to-text-generation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-finetuned-keyword-to-text-generation
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4643
- Rouge1: 2.1108
- Rouge2: 0.3331
- Rougel: 1.7368
- Rougelsum: 1.7391
- Gen Len: 16.591
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 375 | 3.4862 | 2.0718 | 0.326 | 1.7275 | 1.7308 | 16.7995 |
| 3.5928 | 2.0 | 750 | 3.4761 | 2.0829 | 0.3253 | 1.7192 | 1.7224 | 16.773 |
| 3.5551 | 3.0 | 1125 | 3.4701 | 2.1028 | 0.3272 | 1.7274 | 1.7296 | 16.6505 |
| 3.5225 | 4.0 | 1500 | 3.4671 | 2.11 | 0.3305 | 1.7343 | 1.7362 | 16.699 |
| 3.5225 | 5.0 | 1875 | 3.4653 | 2.1134 | 0.3319 | 1.7418 | 1.7437 | 16.5485 |
| 3.4987 | 6.0 | 2250 | 3.4643 | 2.1108 | 0.3331 | 1.7368 | 1.7391 | 16.591 |
| 3.4939 | 7.0 | 2625 | 3.4643 | 2.1108 | 0.3331 | 1.7368 | 1.7391 | 16.591 |
| 3.498 | 8.0 | 3000 | 3.4643 | 2.1108 | 0.3331 | 1.7368 | 1.7391 | 16.591 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/InformalToFormalLincoln21 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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} | 8 | 2022-08-27T23:32:49Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr2e05-wd0.1-bs8
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. -->
# recipe-lr2e05-wd0.1-bs8
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2768
- Rmse: 0.5262
- Mse: 0.2768
- Mae: 0.4258
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.277 | 1.0 | 2490 | 0.2745 | 0.5239 | 0.2745 | 0.4180 |
| 0.2739 | 2.0 | 4980 | 0.2814 | 0.5304 | 0.2814 | 0.4321 |
| 0.2723 | 3.0 | 7470 | 0.2768 | 0.5262 | 0.2768 | 0.4258 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/InformalToFormalLincoln24 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
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} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilroberta-base-task-specific-distilation-on-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. -->
# distilroberta-base-task-specific-distilation-on-squad
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
BigSalmon/InformalToFormalLincoln25 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
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"GPT2LMHeadModel"
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} | 10 | 2022-08-27T23:56:07Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- f1
model-index:
- name: model_for_inca
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: F1
type: f1
value: 0.9281082156355679
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model_for_inca
This model is a fine-tuned version of [marcus2000/finetuning-sentiment-model-3000-samples](https://huggingface.co/marcus2000/finetuning-sentiment-model-3000-samples) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3349
- F1: 0.9281
## 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 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/InformalToFormalLincolnDistilledGPT2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr2e05-wd0.02-bs8
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. -->
# recipe-lr2e05-wd0.02-bs8
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2767
- Rmse: 0.5260
- Mse: 0.2767
- Mae: 0.4245
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2771 | 1.0 | 2490 | 0.2746 | 0.5240 | 0.2746 | 0.4201 |
| 0.2739 | 2.0 | 4980 | 0.2810 | 0.5301 | 0.2810 | 0.4329 |
| 0.2723 | 3.0 | 7470 | 0.2767 | 0.5260 | 0.2767 | 0.4245 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/Lincoln4 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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} | 11 | 2022-08-28T00:11:22Z | ---
language:
- kab
library_name: nemo
datasets:
- mozilla-foundation/common_voice_10_0
thumbnail: null
tags:
- automatic-speech-recognition
- speech
- audio
- Transducer
- Conformer
- Transformer
- pytorch
- NeMo
- hf-asr-leaderboard
license: cc-by-4.0
model-index:
- name: stt_kab_conformer_transducer_large
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Mozilla Common Voice 10.0
type: mozilla-foundation/common_voice_10_0
config: kab
split: test
args:
language: kab
metrics:
- name: Test WER
type: wer
value: 18.86
---
# NVIDIA Conformer-Transducer Large (Kabyle)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| [](#datasets)
This model transcribes speech into lowercase Latin alphabet including space and apostrophe and is trained on around 131 hours of Kabyle speech data.
It is a non-autoregressive "large" variant of Conformer, with around 120 million parameters.
See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-transducer) for complete architecture details.
## Usage
The model is available for use in the NeMo toolkit [3] and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
To train, fine-tune or play with the model, you need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed the latest PyTorch version.
```
pip install nemo_toolkit['all']
```
### Automatically instantiate the model
```python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_kab_conformer_transducer_large")
```
### Transcribing using Python
Simply do:
```
asr_model.transcribe(['<your_audio>.wav'])
```
### Transcribing many audio files
```shell
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py
pretrained_name="nvidia/stt_kab_conformer_transducer_large"
audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
```
### Input
This model accepts 16 kHz mono-channel Audio (wav files) as input.
### Output
This model provides transcribed speech as a string for a given audio sample.
## Model Architecture
Conformer-Transducer model is an autoregressive variant of the Conformer model [1] for Automatic Speech Recognition which uses Transducer loss/decoding. You may find more info on the detail of this model here: [Conformer-Transducer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html).
## Training
The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_transducer_bpe.yaml).
The vocabulary we use contains 39 characters including space:
```python
['a', 'b', 'c', 'č', 'd', 'ḍ', 'e', 'ɛ', 'f', 'g', 'ǧ', 'ɣ', 'h', 'ḥ', 'i', 'j', 'k', 'l', 'm', 'n', 'q', 'r', 'ř', 'ṛ', 's', 'ṣ', 't', 'ṭ', 'u', 'w', 'x', 'y', 'z', 'ẓ', ' ', 'o', 'p', 'γ', 'v']
```
Rare symbols with diacritics were replaced during preprocessing.
The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).
For vocabulary of size 128 we restrict the maximum subtoken length to 4 symbols to avoid populating vocabulary with specific frequent words from the dataset. This does not affect the model performance and potentially helps to adapt to other domain without retraining tokenizer.
Full config can be found inside the .nemo files.
### Datasets
All the models in this collection are trained on the MCV-10.0 Kabyle dataset, which contains around 131 hours of training data, 14.74 hours of development, and 15.67 hours of testing speech audio.
## Performance
The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.
| Version | Tokenizer | Vocabulary Size | Dev WER| Test WER| Train Dataset |
|---------|-----------------------|-----------------|--------|---------|-----------------|
| 1.12.0 | SentencePiece BPE, maxlen=4 | 128 |18.12 | 18.86 | MCV-10.0 Train set|
## Limitations
Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
## Deployment with NVIDIA Riva
[NVIDIA Riva](https://developer.nvidia.com/riva), is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded.
Additionally, Riva provides:
* World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours
* Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization
* Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support
Although this model isn’t supported yet by Riva, the [list of supported models is here](https://huggingface.co/models?other=Riva).
Check out [Riva live demo](https://developer.nvidia.com/riva#demos).
## References
- [1] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)
- [2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)
- [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) |
BigSalmon/MrLincoln | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr8e06-wd0.01-bs8
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. -->
# recipe-lr8e06-wd0.01-bs8
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2782
- Rmse: 0.5274
- Mse: 0.2782
- Mae: 0.4299
## 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: 8e-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
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2766 | 1.0 | 2490 | 0.2739 | 0.5234 | 0.2739 | 0.4152 |
| 0.2739 | 2.0 | 4980 | 0.2769 | 0.5262 | 0.2769 | 0.4274 |
| 0.2725 | 3.0 | 7470 | 0.2782 | 0.5274 | 0.2782 | 0.4299 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/MrLincoln10 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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} | 5 | 2022-08-28T00:45:38Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8648740833380706
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1365
- F1: 0.8649
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2553 | 1.0 | 525 | 0.1575 | 0.8279 |
| 0.1284 | 2.0 | 1050 | 0.1386 | 0.8463 |
| 0.0813 | 3.0 | 1575 | 0.1365 | 0.8649 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
BigSalmon/MrLincoln11 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
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} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr8e06-wd0.005-bs8
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. -->
# recipe-lr8e06-wd0.005-bs8
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2782
- Rmse: 0.5274
- Mse: 0.2782
- Mae: 0.4298
## 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: 8e-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
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2766 | 1.0 | 2490 | 0.2739 | 0.5234 | 0.2739 | 0.4154 |
| 0.2739 | 2.0 | 4980 | 0.2768 | 0.5261 | 0.2768 | 0.4273 |
| 0.2725 | 3.0 | 7470 | 0.2782 | 0.5274 | 0.2782 | 0.4298 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/MrLincoln13 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
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} | 9 | 2022-08-28T01:13:07Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr8e06-wd0.1-bs8
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. -->
# recipe-lr8e06-wd0.1-bs8
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2778
- Rmse: 0.5270
- Mse: 0.2778
- Mae: 0.4290
## 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: 8e-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
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2766 | 1.0 | 2490 | 0.2741 | 0.5235 | 0.2741 | 0.4176 |
| 0.2739 | 2.0 | 4980 | 0.2773 | 0.5266 | 0.2773 | 0.4286 |
| 0.2726 | 3.0 | 7470 | 0.2778 | 0.5270 | 0.2778 | 0.4290 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- cord-layoutlmv3
model-index:
- name: layoutlmv2-finetuned-cord_100
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-cord_100
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the cord-layoutlmv3 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: 3000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.21.2
- Pytorch 1.10.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/MrLincoln4 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
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} | 10 | 2022-08-28T02:03:21Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr1e05-wd0.01-bs8
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. -->
# recipe-lr1e05-wd0.01-bs8
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2779
- Rmse: 0.5272
- Mse: 0.2779
- Mae: 0.4281
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2766 | 1.0 | 2490 | 0.2740 | 0.5235 | 0.2740 | 0.4175 |
| 0.2738 | 2.0 | 4980 | 0.2784 | 0.5277 | 0.2784 | 0.4296 |
| 0.2724 | 3.0 | 7470 | 0.2779 | 0.5272 | 0.2779 | 0.4281 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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} | 0 | 2022-08-28T02:28:12Z | ---
license: mit
datasets:
- sagawa/pubchem-10m-canonicalized
metrics:
- accuracy
model-index:
- name: PubChem-10m-t5
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: sagawa/pubchem-10m-canonicalized
type: sagawa/pubchem-10m-canonicalized
metrics:
- name: Accuracy
type: accuracy
value: 0.9259435534477234
---
# PubChem-10m-t5
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/microsoft/deberta-base) on the sagawa/pubchem-10m-canonicalized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2121
- Accuracy: 0.9259
## Model description
We trained t5 on SMILES from PubChem using the task of masked-language modeling (MLM). Its tokenizer is also trained on PubChem.
## Intended uses & limitations
This model can be used for the prediction of molecules' properties, reactions, or interactions with proteins by changing the way of finetuning.
## Training and evaluation data
We downloaded [PubChem data](https://drive.google.com/file/d/1ygYs8dy1-vxD1Vx6Ux7ftrXwZctFjpV3/view) and canonicalized them using RDKit. Then, we dropped duplicates. The total number of data is 9999960, and they were randomly split into train:validation=10:1.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-03
- train_batch_size: 30
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0
### Training results
| Training Loss | Step | Accuracy | Validation Loss |
|:-------------:|:------:|:--------:|:---------------:|
| 0.3866 | 25000 | 0.8830 | 0.3631 |
| 0.3352 | 50000 | 0.8996 | 0.3049 |
| 0.2834 | 75000 | 0.9057 | 0.2825 |
| 0.2685 | 100000 | 0.9099 | 0.2675 |
| 0.2591 | 125000 | 0.9124 | 0.2587 |
| 0.2620 | 150000 | 0.9144 | 0.2512 |
| 0.2806 | 175000 | 0.9161 | 0.2454 |
| 0.2468 | 200000 | 0.9179 | 0.2396 |
| 0.2669 | 225000 | 0.9194 | 0.2343 |
| 0.2611 | 250000 | 0.9210 | 0.2283 |
| 0.2346 | 275000 | 0.9226 | 0.2230 |
| 0.1972 | 300000 | 0.9238 | 0.2191 |
| 0.2344 | 325000 | 0.9250 | 0.2152 |
| 0.2164 | 350000 | 0.9259 | 0.2121 | |
BigSalmon/MrLincoln8 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
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} | 12 | 2022-08-28T02:28:30Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr1e05-wd0.005-bs8
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. -->
# recipe-lr1e05-wd0.005-bs8
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2779
- Rmse: 0.5271
- Mse: 0.2779
- Mae: 0.4279
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2766 | 1.0 | 2490 | 0.2740 | 0.5235 | 0.2740 | 0.4173 |
| 0.2738 | 2.0 | 4980 | 0.2785 | 0.5277 | 0.2785 | 0.4295 |
| 0.2724 | 3.0 | 7470 | 0.2779 | 0.5271 | 0.2779 | 0.4279 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/NEO125InformalToFormalLincoln | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | {
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} | 8 | 2022-08-28T02:32:47Z | ---
language: en
thumbnail: http://www.huggingtweets.com/pink_rodent/1661654012124/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/1558011857838931968/JdtfxNhf_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">mouse</div>
<div style="text-align: center; font-size: 14px;">@pink_rodent</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 mouse.
| Data | mouse |
| --- | --- |
| Tweets downloaded | 242 |
| Retweets | 48 |
| Short tweets | 55 |
| Tweets kept | 139 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/182s7hgh/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 @pink_rodent's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/35lwy7go) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/35lwy7go/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/pink_rodent')
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)
|
BigSalmon/Neo | [
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"text-generation",
"transformers"
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} | 13 | 2022-08-28T02:47:12Z | ---
language: en
thumbnail: http://www.huggingtweets.com/cant_piss/1661654878948/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/1554200320468721665/FGL79hJA_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">meltin' eyes</div>
<div style="text-align: center; font-size: 14px;">@cant_piss</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 meltin' eyes.
| Data | meltin' eyes |
| --- | --- |
| Tweets downloaded | 210 |
| Retweets | 9 |
| Short tweets | 28 |
| Tweets kept | 173 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/278494ey/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 @cant_piss's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/pol9eo28) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/pol9eo28/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/cant_piss')
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)
|
BigSalmon/ParaphraseParentheses | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
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}
} | 10 | 2022-08-28T02:53:40Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr1e05-wd0.1-bs8
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. -->
# recipe-lr1e05-wd0.1-bs8
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2779
- Rmse: 0.5271
- Mse: 0.2779
- Mae: 0.4280
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2766 | 1.0 | 2490 | 0.2740 | 0.5235 | 0.2740 | 0.4175 |
| 0.2738 | 2.0 | 4980 | 0.2785 | 0.5277 | 0.2785 | 0.4296 |
| 0.2724 | 3.0 | 7470 | 0.2779 | 0.5271 | 0.2779 | 0.4280 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/PhraseBerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
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}
} | 10 | 2022-08-28T03:18:59Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr1e05-wd0.02-bs8
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. -->
# recipe-lr1e05-wd0.02-bs8
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2781
- Rmse: 0.5273
- Mse: 0.2781
- Mae: 0.4279
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2766 | 1.0 | 2490 | 0.2740 | 0.5234 | 0.2740 | 0.4172 |
| 0.2738 | 2.0 | 4980 | 0.2783 | 0.5276 | 0.2783 | 0.4297 |
| 0.2724 | 3.0 | 7470 | 0.2781 | 0.5273 | 0.2781 | 0.4279 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/Rowerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
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}
} | 4 | 2022-08-28T03:44:47Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr2e05-wd0.01-bs32
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. -->
# recipe-lr2e05-wd0.01-bs32
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2860
- Rmse: 0.5348
- Mse: 0.2860
- Mae: 0.4434
## 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: 256
- eval_batch_size: 256
- 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 | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2774 | 1.0 | 623 | 0.2745 | 0.5239 | 0.2745 | 0.4161 |
| 0.274 | 2.0 | 1246 | 0.2739 | 0.5233 | 0.2739 | 0.4167 |
| 0.2724 | 3.0 | 1869 | 0.2860 | 0.5348 | 0.2860 | 0.4434 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigSalmon/T5Salmon | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
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"max_length": 300,
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"prefix": "translate English to German: "
},
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"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 6 | 2022-08-28T04:14:35Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1-baseline
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 215.80 +/- 39.04
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
BigSalmon/TS3 | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible",
"has_space"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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},
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}
}
} | 7 | 2022-08-28T04:29:21Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr2e05-wd0.02-bs32
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. -->
# recipe-lr2e05-wd0.02-bs32
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2784
- Rmse: 0.5277
- Mse: 0.2784
- Mae: 0.4161
## 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: 256
- eval_batch_size: 256
- 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 | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2774 | 1.0 | 623 | 0.2749 | 0.5243 | 0.2749 | 0.4184 |
| 0.2741 | 2.0 | 1246 | 0.2741 | 0.5235 | 0.2741 | 0.4173 |
| 0.2724 | 3.0 | 1869 | 0.2855 | 0.5343 | 0.2855 | 0.4428 |
| 0.2713 | 4.0 | 2492 | 0.2758 | 0.5252 | 0.2758 | 0.4013 |
| 0.2695 | 5.0 | 3115 | 0.2777 | 0.5270 | 0.2777 | 0.4245 |
| 0.2674 | 6.0 | 3738 | 0.2784 | 0.5277 | 0.2784 | 0.4161 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigTooth/DialoGPT-Megumin | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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},
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}
}
} | 16 | 2022-08-28T05:10:50Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1-exp1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 458.90 +/- 80.57
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
BigTooth/DialoGPT-small-tohru | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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}
} | 10 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr8e06-wd0.005-bs32
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. -->
# recipe-lr8e06-wd0.005-bs32
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2752
- Rmse: 0.5246
- Mse: 0.2752
- Mae: 0.4184
## 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: 8e-06
- train_batch_size: 256
- eval_batch_size: 256
- 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 | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2769 | 1.0 | 623 | 0.2773 | 0.5266 | 0.2773 | 0.4296 |
| 0.2745 | 2.0 | 1246 | 0.2739 | 0.5233 | 0.2739 | 0.4144 |
| 0.2733 | 3.0 | 1869 | 0.2752 | 0.5246 | 0.2752 | 0.4215 |
| 0.2722 | 4.0 | 2492 | 0.2744 | 0.5238 | 0.2744 | 0.4058 |
| 0.2714 | 5.0 | 3115 | 0.2758 | 0.5251 | 0.2758 | 0.4232 |
| 0.2705 | 6.0 | 3738 | 0.2752 | 0.5246 | 0.2752 | 0.4184 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigTooth/Megumin-v0.2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
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}
} | 13 | 2022-08-28T05:35:26Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1-exp2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
BigeS/DialoGPT-small-Rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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"length_penalty": null,
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}
} | 10 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER_ehealth_Spanish_mBERT_fine_tuned
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. -->
# NER_ehealth_Spanish_mBERT_fine_tuned
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6563
- Precision: 0.8094
- Recall: 0.8330
- F1: 0.8210
- Accuracy: 0.9051
## 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: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 100 | 0.5335 | 0.8018 | 0.8307 | 0.8160 | 0.9047 |
| No log | 2.0 | 200 | 0.5034 | 0.8110 | 0.8253 | 0.8181 | 0.9067 |
| No log | 3.0 | 300 | 0.5632 | 0.7932 | 0.8230 | 0.8078 | 0.9038 |
| No log | 4.0 | 400 | 0.5904 | 0.8004 | 0.8299 | 0.8149 | 0.9027 |
| 0.017 | 5.0 | 500 | 0.5958 | 0.7993 | 0.8330 | 0.8158 | 0.9071 |
| 0.017 | 6.0 | 600 | 0.6168 | 0.7980 | 0.8352 | 0.8162 | 0.9022 |
| 0.017 | 7.0 | 700 | 0.6219 | 0.8079 | 0.8314 | 0.8195 | 0.9062 |
| 0.017 | 8.0 | 800 | 0.6441 | 0.8046 | 0.8299 | 0.8171 | 0.9038 |
| 0.017 | 9.0 | 900 | 0.6338 | 0.8086 | 0.8253 | 0.8168 | 0.9051 |
| 0.0066 | 10.0 | 1000 | 0.6482 | 0.8021 | 0.8261 | 0.8139 | 0.9029 |
| 0.0066 | 11.0 | 1100 | 0.6578 | 0.8039 | 0.8291 | 0.8163 | 0.9038 |
| 0.0066 | 12.0 | 1200 | 0.6563 | 0.8094 | 0.8330 | 0.8210 | 0.9051 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BillelBenoudjit/jplu-wikiann | [
"fr",
"dataset:wikiann",
"model-index"
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} | 0 | 2022-08-28T05:45:44Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.863677639046538
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1343
- F1: 0.8637
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2578 | 1.0 | 525 | 0.1562 | 0.8273 |
| 0.1297 | 2.0 | 1050 | 0.1330 | 0.8474 |
| 0.0809 | 3.0 | 1575 | 0.1343 | 0.8637 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr8e06-wd0.1-bs32
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. -->
# recipe-lr8e06-wd0.1-bs32
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2752
- Rmse: 0.5246
- Mse: 0.2752
- Mae: 0.4184
## 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: 8e-06
- train_batch_size: 256
- eval_batch_size: 256
- 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 | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2769 | 1.0 | 623 | 0.2773 | 0.5266 | 0.2773 | 0.4297 |
| 0.2745 | 2.0 | 1246 | 0.2739 | 0.5233 | 0.2739 | 0.4144 |
| 0.2733 | 3.0 | 1869 | 0.2752 | 0.5246 | 0.2752 | 0.4215 |
| 0.2722 | 4.0 | 2492 | 0.2744 | 0.5238 | 0.2744 | 0.4058 |
| 0.2714 | 5.0 | 3115 | 0.2758 | 0.5252 | 0.2758 | 0.4233 |
| 0.2705 | 6.0 | 3738 | 0.2752 | 0.5246 | 0.2752 | 0.4184 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BinksSachary/DialoGPT-small-shaxx | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
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} | 12 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8888888955116272
---
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### fresh leaf of plant
#### plant diseases
 |
BinksSachary/ShaxxBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
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} | 9 | 2022-08-28T07:17:41Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr1e05-wd0.005-bs32
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. -->
# recipe-lr1e05-wd0.005-bs32
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2756
- Rmse: 0.5250
- Mse: 0.2756
- Mae: 0.4181
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 256
- eval_batch_size: 256
- 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 | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2769 | 1.0 | 623 | 0.2768 | 0.5261 | 0.2768 | 0.4281 |
| 0.2743 | 2.0 | 1246 | 0.2739 | 0.5234 | 0.2739 | 0.4153 |
| 0.2732 | 3.0 | 1869 | 0.2760 | 0.5253 | 0.2760 | 0.4229 |
| 0.2719 | 4.0 | 2492 | 0.2749 | 0.5243 | 0.2749 | 0.4041 |
| 0.271 | 5.0 | 3115 | 0.2761 | 0.5255 | 0.2761 | 0.4238 |
| 0.2699 | 6.0 | 3738 | 0.2756 | 0.5250 | 0.2756 | 0.4181 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr1e05-wd0.1-bs32
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. -->
# recipe-lr1e05-wd0.1-bs32
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2756
- Rmse: 0.5250
- Mse: 0.2756
- Mae: 0.4181
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 256
- eval_batch_size: 256
- 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 | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2769 | 1.0 | 623 | 0.2768 | 0.5261 | 0.2768 | 0.4281 |
| 0.2743 | 2.0 | 1246 | 0.2739 | 0.5234 | 0.2739 | 0.4152 |
| 0.2732 | 3.0 | 1869 | 0.2760 | 0.5253 | 0.2760 | 0.4229 |
| 0.2719 | 4.0 | 2492 | 0.2749 | 0.5243 | 0.2749 | 0.4041 |
| 0.271 | 5.0 | 3115 | 0.2761 | 0.5255 | 0.2761 | 0.4238 |
| 0.2699 | 6.0 | 3738 | 0.2756 | 0.5250 | 0.2756 | 0.4181 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Blazeolmo/Scrabunzi | [] | null | {
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} | 0 | null | Access to model AlexZarem/test is restricted and you are not in the authorized list. Visit https://huggingface.co/AlexZarem/test to ask for access. |
Blerrrry/Kkk | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-lr1e05-wd0.02-bs32
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. -->
# recipe-lr1e05-wd0.02-bs32
This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2756
- Rmse: 0.5250
- Mse: 0.2756
- Mae: 0.4181
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 256
- eval_batch_size: 256
- 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 | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2769 | 1.0 | 623 | 0.2768 | 0.5261 | 0.2768 | 0.4281 |
| 0.2743 | 2.0 | 1246 | 0.2739 | 0.5234 | 0.2739 | 0.4152 |
| 0.2732 | 3.0 | 1869 | 0.2760 | 0.5253 | 0.2760 | 0.4229 |
| 0.2719 | 4.0 | 2492 | 0.2749 | 0.5243 | 0.2749 | 0.4041 |
| 0.271 | 5.0 | 3115 | 0.2761 | 0.5255 | 0.2761 | 0.4238 |
| 0.2699 | 6.0 | 3738 | 0.2756 | 0.5250 | 0.2756 | 0.4181 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Boondong/Wandee | [] | null | {
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} | 0 | 2022-08-28T11:25:18Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **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="Shivus/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"])
```
|
Buntan/xlm-roberta-base-finetuned-marc-en | [] | null | {
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} | 0 | 2022-08-28T16:27:26Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-xls-r-300m-arabic_speech_commands_10s_one_speaker_40_classes
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-xls-r-300m-arabic_speech_commands_10s_one_speaker_40_classes
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8103
- Accuracy: 0.7671
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 80
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.6878 | 1.0 | 25 | 3.6888 | 0.03 |
| 3.6867 | 2.0 | 50 | 3.6867 | 0.025 |
| 3.6652 | 3.0 | 75 | 3.6260 | 0.1558 |
| 3.3912 | 4.0 | 100 | 3.3953 | 0.1254 |
| 3.103 | 5.0 | 125 | 3.1497 | 0.125 |
| 2.661 | 6.0 | 150 | 2.7077 | 0.2196 |
| 2.374 | 7.0 | 175 | 2.7057 | 0.2112 |
| 1.9198 | 8.0 | 200 | 2.5269 | 0.2929 |
| 1.7809 | 9.0 | 225 | 2.8878 | 0.2204 |
| 1.4742 | 10.0 | 250 | 2.1809 | 0.3917 |
| 1.3373 | 11.0 | 275 | 1.9832 | 0.4412 |
| 1.0601 | 12.0 | 300 | 2.0539 | 0.4958 |
| 0.9018 | 13.0 | 325 | 2.2291 | 0.46 |
| 0.6925 | 14.0 | 350 | 1.5878 | 0.5946 |
| 0.6181 | 15.0 | 375 | 2.1394 | 0.5283 |
| 0.4066 | 16.0 | 400 | 2.2009 | 0.5363 |
| 0.4346 | 17.0 | 425 | 1.9644 | 0.5625 |
| 0.3882 | 18.0 | 450 | 1.3859 | 0.6658 |
| 0.3382 | 19.0 | 475 | 1.6092 | 0.6771 |
| 0.3172 | 20.0 | 500 | 1.7496 | 0.6571 |
| 0.322 | 21.0 | 525 | 1.6505 | 0.6621 |
| 0.1848 | 22.0 | 550 | 2.1235 | 0.5933 |
| 0.2695 | 23.0 | 575 | 2.1248 | 0.6054 |
| 0.2091 | 24.0 | 600 | 2.0269 | 0.6312 |
| 0.172 | 25.0 | 625 | 1.5532 | 0.7167 |
| 0.2043 | 26.0 | 650 | 1.9791 | 0.6358 |
| 0.1744 | 27.0 | 675 | 1.4877 | 0.7458 |
| 0.1837 | 28.0 | 700 | 1.8348 | 0.6896 |
| 0.2209 | 29.0 | 725 | 2.1801 | 0.6267 |
| 0.144 | 30.0 | 750 | 1.9425 | 0.6692 |
| 0.0513 | 31.0 | 775 | 1.6531 | 0.7096 |
| 0.0494 | 32.0 | 800 | 1.8506 | 0.715 |
| 0.0697 | 33.0 | 825 | 1.9599 | 0.6933 |
| 0.1528 | 34.0 | 850 | 2.0854 | 0.6521 |
| 0.0769 | 35.0 | 875 | 2.6593 | 0.6483 |
| 0.0691 | 36.0 | 900 | 1.9098 | 0.7321 |
| 0.0401 | 37.0 | 925 | 2.0541 | 0.6967 |
| 0.0287 | 38.0 | 950 | 2.3037 | 0.6904 |
| 0.1034 | 39.0 | 975 | 1.6426 | 0.7304 |
| 0.0876 | 40.0 | 1000 | 2.1685 | 0.6775 |
| 0.0557 | 41.0 | 1025 | 2.2643 | 0.6821 |
| 0.0395 | 42.0 | 1050 | 2.0308 | 0.6979 |
| 0.1046 | 43.0 | 1075 | 2.0277 | 0.7021 |
| 0.0768 | 44.0 | 1100 | 1.7130 | 0.7371 |
| 0.048 | 45.0 | 1125 | 1.9549 | 0.7192 |
| 0.0835 | 46.0 | 1150 | 1.9024 | 0.7179 |
| 0.0505 | 47.0 | 1175 | 2.0993 | 0.7125 |
| 0.0515 | 48.0 | 1200 | 1.9806 | 0.7183 |
| 0.0556 | 49.0 | 1225 | 1.8291 | 0.7321 |
| 0.0886 | 50.0 | 1250 | 2.1479 | 0.6992 |
| 0.0769 | 51.0 | 1275 | 2.0540 | 0.7146 |
| 0.0092 | 52.0 | 1300 | 1.8446 | 0.7462 |
| 0.0032 | 53.0 | 1325 | 2.0847 | 0.7125 |
| 0.0593 | 54.0 | 1350 | 1.9553 | 0.7304 |
| 0.0053 | 55.0 | 1375 | 1.8164 | 0.74 |
| 0.0101 | 56.0 | 1400 | 1.7514 | 0.7421 |
| 0.0155 | 57.0 | 1425 | 1.6395 | 0.7604 |
| 0.0035 | 58.0 | 1450 | 1.7393 | 0.7504 |
| 0.0019 | 59.0 | 1475 | 1.8103 | 0.7671 |
| 0.0144 | 60.0 | 1500 | 1.8234 | 0.7588 |
| 0.0028 | 61.0 | 1525 | 1.8479 | 0.7529 |
| 0.0306 | 62.0 | 1550 | 1.7948 | 0.7454 |
| 0.0028 | 63.0 | 1575 | 1.7417 | 0.7562 |
| 0.0095 | 64.0 | 1600 | 1.6973 | 0.7592 |
| 0.0086 | 65.0 | 1625 | 1.9997 | 0.7342 |
| 0.0953 | 66.0 | 1650 | 1.8202 | 0.7538 |
| 0.0018 | 67.0 | 1675 | 1.8316 | 0.7533 |
| 0.0053 | 68.0 | 1700 | 1.8916 | 0.7475 |
| 0.004 | 69.0 | 1725 | 1.8794 | 0.7521 |
| 0.0169 | 70.0 | 1750 | 1.8215 | 0.7533 |
| 0.0013 | 71.0 | 1775 | 1.7565 | 0.7508 |
| 0.0008 | 72.0 | 1800 | 1.8171 | 0.7454 |
| 0.0011 | 73.0 | 1825 | 1.8354 | 0.7479 |
| 0.0025 | 74.0 | 1850 | 1.8283 | 0.7488 |
| 0.0013 | 75.0 | 1875 | 1.8876 | 0.7412 |
| 0.0415 | 76.0 | 1900 | 1.8789 | 0.7454 |
| 0.0341 | 77.0 | 1925 | 1.8665 | 0.7512 |
| 0.0149 | 78.0 | 1950 | 1.8579 | 0.7488 |
| 0.0018 | 79.0 | 1975 | 1.8571 | 0.7488 |
| 0.008 | 80.0 | 2000 | 1.8596 | 0.7479 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
CAMeL-Lab/bert-base-arabic-camelbert-da | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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} | 449 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilbert-finetuned-dapt_tapt-lm-music
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. -->
# distilbert-finetuned-dapt_tapt-lm-music
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: 2.8680
- Validation Loss: 2.4306
- 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 32918, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.8680 | 2.4306 | 0 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
CAUKiel/JavaBERT-uncased | [
"pytorch",
"safetensors",
"bert",
"fill-mask",
"java",
"code",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
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],
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: rhitabrat/bert-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. -->
# rhitabrat/bert-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7887
- 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7790, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.2182 | 0 |
| 0.7887 | 1 |
### Framework versions
- Transformers 4.21.2
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
CM-CA/Cartman | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9807407407407407
---
<!-- 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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0610
- Accuracy: 0.9807
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2601 | 1.0 | 190 | 0.1154 | 0.9615 |
| 0.1928 | 2.0 | 380 | 0.0748 | 0.9752 |
| 0.1365 | 3.0 | 570 | 0.0610 | 0.9807 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Cameron/BERT-mdgender-convai-ternary | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
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} | 38 | null | ---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4886
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6853 | 0.54 | 500 | 1.4886 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Cameron/BERT-mdgender-wizard | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
],
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} | 30 | null | Access to model polyfooly/fcnn is restricted and you are not in the authorized list. Visit https://huggingface.co/polyfooly/fcnn to ask for access. |
Captain-1337/CrudeBERT | [
"pytorch",
"bert",
"text-classification",
"arxiv:1908.10063",
"transformers"
] | text-classification | {
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} | 28 | 2022-08-29T06:30:57Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Recall
type: recall
value: 0.9522046449007069
- name: F1
type: f1
value: 0.9441802252816022
- name: Accuracy
type: accuracy
value: 0.9866221227997881
---
<!-- 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: 0.0858
- Precition: 0.9363
- Recall: 0.9522
- F1: 0.9442
- Accuracy: 0.9866
## 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 | Precition | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0081 | 1.0 | 1756 | 0.0914 | 0.9273 | 0.9446 | 0.9359 | 0.9848 |
| 0.012 | 2.0 | 3512 | 0.0852 | 0.9321 | 0.9478 | 0.9399 | 0.9857 |
| 0.0036 | 3.0 | 5268 | 0.0858 | 0.9363 | 0.9522 | 0.9442 | 0.9866 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Cathy/reranking_model | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
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"RobertaForSequenceClassification"
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} | 27 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
dccuchile/albert-base-spanish-finetuned-ner | [
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
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} | 14 | null | ---
tags:
- summarization
license: mit
---
# BART (large-sized model), fine-tuned on custom contracts dataset
BART model pre-trained on English language, and fine-tuned for terms of service abstractive summarization
## Model description
BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). This particular checkpoint has been fine-tuned on CNN Daily Mail, a large collection of text-summary pairs.
## Intended uses & limitations
You can use this model for text summarization.
### How to use
Here is how to use this model with the [pipeline API](https://huggingface.co/transformers/main_classes/pipelines.html):
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="ML-unipi/bart-large-tos")
ARTICLE = """ New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York.
A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband.
Only 18 days after that marriage, she got hitched yet again. Then, Barrientos declared "I do" five more times, sometimes only within two weeks of each other.
In 2010, she married once more, this time in the Bronx. In an application for a marriage license, she stated it was her "first and only" marriage.
Barrientos, now 39, is facing two criminal counts of "offering a false instrument for filing in the first degree," referring to her false statements on the
2010 marriage license application, according to court documents.
Prosecutors said the marriages were part of an immigration scam.
On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further.
After leaving court, Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an emergency exit, said Detective
Annette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002.
All occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be married to four men, and at one time, she was married to eight men at once, prosecutors say.
Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages.
Any divorces happened only after such filings were approved. It was unclear whether any of the men will be prosecuted.
The case was referred to the Bronx District Attorney\'s Office by Immigration and Customs Enforcement and the Department of Homeland Security\'s
Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt, Turkey, Georgia, Pakistan and Mali.
Her eighth husband, Rashid Rajput, was deported in 2006 to his native Pakistan after an investigation by the Joint Terrorism Task Force.
If convicted, Barrientos faces up to four years in prison. Her next court appearance is scheduled for May 18.
"""
print(summarizer(ARTICLE, max_length=130, min_length=30, do_sample=False))
>>> [{'summary_text': 'Liana Barrientos, 39, is charged with two counts of "offering a false instrument for filing in the first degree" In total, she has been married 10 times, with nine of her marriages occurring between 1999 and 2002. She is believed to still be married to four men.'}]
```
|
dccuchile/albert-xlarge-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | {
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"AlbertForSequenceClassification"
],
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}
} | 26 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
dccuchile/albert-xlarge-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
"task_specific_params": {
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}
} | 7 | 2022-08-29T09:31:51Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6550
- Matthews Correlation: 0.2820
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 1.7255 | 1.0 | 712 | 1.6687 | 0.1995 |
| 1.3584 | 2.0 | 1424 | 1.6550 | 0.2820 |
| 1.024 | 3.0 | 2136 | 1.7990 | 0.2564 |
| 0.8801 | 4.0 | 2848 | 2.1304 | 0.2657 |
| 0.7138 | 5.0 | 3560 | 2.1946 | 0.2584 |
| 0.5799 | 6.0 | 4272 | 2.4351 | 0.2660 |
| 0.5385 | 7.0 | 4984 | 2.6819 | 0.2539 |
| 0.4088 | 8.0 | 5696 | 2.8667 | 0.2436 |
| 0.3722 | 9.0 | 6408 | 2.9077 | 0.2612 |
| 0.3173 | 10.0 | 7120 | 2.9795 | 0.2542 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
dccuchile/albert-base-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null | {
"architectures": [
"AlbertForPreTraining"
],
"model_type": "albert",
"task_specific_params": {
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},
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}
}
} | 586 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 31.30 +/- 20.84
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
dccuchile/albert-large-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null | {
"architectures": [
"AlbertForPreTraining"
],
"model_type": "albert",
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},
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}
}
} | 75 | null | How to use:
```
from collections import deque
from bs4 import BeautifulSoup
import requests
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, T5Tokenizer
import torch
model_name = 'artemnech/dialoT5-base'
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def generate(text, **kwargs):
model.eval()
inputs = tokenizer(text, return_tensors='pt').to(model.device)
with torch.no_grad():
hypotheses = model.generate(**inputs, **kwargs)
return tokenizer.decode(hypotheses[0], skip_special_tokens=True)
def dialog(context):
keyword = generate('keyword: ' + ' '.join(context), num_beams=2,)
knowlege = ''
if keyword != 'no_keywords':
resp = requests.get(f"https://en.wikipedia.org/wiki/{keyword}")
root = BeautifulSoup(resp.content, "html.parser")
knowlege ="knowlege: " + " ".join([_.text.strip() for _ in root.find("div", class_="mw-body-content mw-content-ltr").find_all("p", limit=2)])
answ = generate(f'dialog: ' + knowlege + ' '.join(context), num_beams=3,
do_sample=True, temperature=1.1, encoder_no_repeat_ngram_size=5,
no_repeat_ngram_size=5,
max_new_tokens = 30)
return answ
context =deque([], maxlen=4)
while True:
text = input()
text = 'user1>>: ' + text
context.append(text)
answ = dialog(context)
context.append('user2>>: ' + answ)
print('bot: ', answ)
``` |
dccuchile/bert-base-spanish-wwm-cased-finetuned-xnli | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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} | 28 | null | ---
license: apache-2.0
tags:
- summarisation
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distilbart-xsum-6-6-finetuned-bbc-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. -->
# distilbart-xsum-6-6-finetuned-bbc-news
This model is a fine-tuned version of [sshleifer/distilbart-xsum-6-6](https://huggingface.co/sshleifer/distilbart-xsum-6-6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2624
- Rouge1: 62.1927
- Rouge2: 54.4754
- Rougel: 55.868
- Rougelsum: 60.9345
## 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: 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 0.4213 | 1.0 | 445 | 0.2005 | 59.4886 | 51.7791 | 53.5126 | 58.3405 |
| 0.1355 | 2.0 | 890 | 0.1887 | 61.7474 | 54.2823 | 55.7324 | 60.5787 |
| 0.0891 | 3.0 | 1335 | 0.1932 | 61.1312 | 53.103 | 54.6992 | 59.8923 |
| 0.0571 | 4.0 | 1780 | 0.2141 | 60.8797 | 52.6195 | 54.4402 | 59.5298 |
| 0.0375 | 5.0 | 2225 | 0.2318 | 61.7875 | 53.8753 | 55.5068 | 60.5448 |
| 0.0251 | 6.0 | 2670 | 0.2484 | 62.3535 | 54.6029 | 56.2804 | 61.031 |
| 0.0175 | 7.0 | 3115 | 0.2542 | 61.6351 | 53.8248 | 55.6399 | 60.3765 |
| 0.0133 | 8.0 | 3560 | 0.2624 | 62.1927 | 54.4754 | 55.868 | 60.9345 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate-1 | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"DistilBertForMaskedLM"
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} | 1 | null | ---
language:
- en
library_name: nemo
datasets:
- SLURP
thumbnail: null
tags:
- spoken-language-understanding
- speech-intent-classification
- speech-slot-filling
- SLURP
- Conformer
- Transformer
- pytorch
- NeMo
license: cc-by-4.0
model-index:
- name: slu_conformer_transformer_large_slurp
results:
- task:
name: Slot Filling
type: slot-filling
dataset:
name: SLURP
type: slurp
split: test
metrics:
- name: F1
type: f1
value: 82.27
- task:
name: Intent Classification
type: intent-classification
dataset:
name: SLURP
type: slurp
split: test
metrics:
- name: Accuracy
type: acc
value: 90.14
---
# NeMo End-to-End Speech Intent Classification and Slot Filling
## Model Overview
This model performs joint intent classification and slot filling, directly from audio input. The model treats the problem as an audio-to-text problem, where the output text is the flattened string representation of the semantics annotation. The model is trained on the SLURP dataset [1].
## Model Architecture
The model is has an encoder-decoder architecture, where the encoder is a Conformer-Large model [2], and the decoder is a three-layer Transformer Decoder [3]. We use the Conformer encoder pretrained on NeMo ASR-Set (details [here](https://ngc.nvidia.com/models/nvidia:nemo:stt_en_conformer_ctc_large)), while the decoder is trained from scratch. A start-of-sentence (BOS) and an end-of-sentence (EOS) tokens are added to each sentence. The model is trained end-to-end by minimizing the negative log-likelihood loss with teacher forcing. During inference, the prediction is generated by beam search, where a BOS token is used to trigger the generation process.
## Training
The NeMo toolkit [4] was used for training the models for around 100 epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/slu/slurp/run_slurp_train.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/slu/slurp/configs/conformer_transformer_large_bpe.yaml).
The tokenizers for these models were built using the semantics annotations of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py). We use a vocabulary size of 58, including the BOS, EOS and padding tokens.
Details on how to train the model can be found [here](https://github.com/NVIDIA/NeMo/blob/main/examples/slu/speech_intent_slot/README.md).
### Datasets
The model is trained on the combined real and synthetic training sets of the SLURP dataset.
## Performance
| | | | | **Intent (Scenario_Action)** | | **Entity** | | | **SLURP Metrics** | |
|-------|--------------------------------------------------|----------------|--------------------------|------------------------------|---------------|------------|--------|--------------|-------------------|---------------------|
|**Version**| **Model** | **Params (M)** | **Pretrained** | **Accuracy** | **Precision** | **Recall** | **F1** | **Precsion** | **Recall** | **F1** |
|1.13.0| Conformer-Transformer-Large | 127 | NeMo ASR-Set 3.0 | 90.14 | 78.95 | 74.93 | 76.89 | 84.31 | 80.33 | 82.27 |
|Baseline| Conformer-Transformer-Large | 127 | None | 72.56 | 43.19 | 43.5 | 43.34 | 53.59 | 53.92 | 53.76 |
Note: during inference, we use beam size of 32, and a temperature of 1.25.
## How to Use this Model
The model is available for use in the NeMo toolkit [3], and can be used on another dataset with the same annotation format.
### Automatically load the model from NGC
```python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.SLUIntentSlotBPEModel.from_pretrained(model_name="slu_conformer_transformer_large_slurp")
```
### Predict intents and slots with this model
```shell
python [NEMO_GIT_FOLDER]/examples/slu/speech_intent_slot/eval_utils/inference.py \
pretrained_name="slu_conformer_transformer_slurp" \
audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" \
sequence_generator.type="<'beam' OR 'greedy' FOR BEAM/GREEDY SEARCH>" \
sequence_generator.beam_size="<SIZE OF BEAM>" \
sequence_generator.temperature="<TEMPERATURE FOR BEAM SEARCH>"
```
### Input
This model accepts 16000 Hz Mono-channel Audio (wav files) as input.
### Output
This model provides the intent and slot annotaions as a string for a given audio sample.
## Limitations
Since this model was trained on only the SLURP dataset [1], the performance of this model might degrade on other datasets.
## References
[1] [SLURP: A Spoken Language Understanding Resource Package](https://arxiv.org/abs/2011.13205)
[2] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)
[3] [Attention Is All You Need](https://arxiv.org/abs/1706.03762?context=cs)
[4] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
|
CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 7 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/apesahoy-deepleffen-ripeacsky/1661783191661/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/1196519479364268034/5QpniWSP_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/1551180680071204865/yMtXKhhs_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/1241879678455078914/e2EdZIrr_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">Humongous Ape MP & Pesky Splinter - Eternal Goatse Celebrant & Deep Leffen Bot</div>
<div style="text-align: center; font-size: 14px;">@apesahoy-deepleffen-ripeacsky</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 Humongous Ape MP & Pesky Splinter - Eternal Goatse Celebrant & Deep Leffen Bot.
| Data | Humongous Ape MP | Pesky Splinter - Eternal Goatse Celebrant | Deep Leffen Bot |
| --- | --- | --- | --- |
| Tweets downloaded | 3245 | 3140 | 596 |
| Retweets | 187 | 2493 | 14 |
| Short tweets | 626 | 106 | 27 |
| Tweets kept | 2432 | 541 | 555 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/13iiavqh/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 @apesahoy-deepleffen-ripeacsky's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2jwt4umb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2jwt4umb/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/apesahoy-deepleffen-ripeacsky')
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)
|
Chae/botman | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 5 | null | ---
tags:
- generated_from_trainer
model-index:
- name: LayoutXLM-CHRU-20
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. -->
# LayoutXLM-CHRU-20
This model is a fine-tuned version of [manu/doc-parserv3](https://huggingface.co/manu/doc-parserv3) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Chaewon/mnmt_decoder_en | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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} | 8 | null | ---
language:
- de
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: DistilBART_CNN_GNAD_V2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DistilBART_CNN_GNAD_V2
This model is a fine-tuned version of [Einmalumdiewelt/DistilBART_CNN_GNAD_V2](https://huggingface.co/Einmalumdiewelt/DistilBART_CNN_GNAD_V2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7281
- Rouge1: 27.7253
- Rouge2: 8.4647
- Rougel: 18.2059
- Rougelsum: 23.238
- Gen Len: 91.6827
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Chaewon/mnmt_decoder_en_gpt2 | [] | null | {
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} | 0 | 2022-08-29T15:10:50Z | ---
license: apache-2.0
tags:
- summarisation
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distilbart-xsum-6-6-finetuned-bbc-news-on-abstractive
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. -->
# distilbart-xsum-6-6-finetuned-bbc-news-on-abstractive
This model is a fine-tuned version of [sshleifer/distilbart-xsum-6-6](https://huggingface.co/sshleifer/distilbart-xsum-6-6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6549
- Rouge1: 38.9186
- Rouge2: 30.2223
- Rougel: 32.6201
- Rougelsum: 37.7502
## 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: 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 1.3838 | 1.0 | 445 | 1.4841 | 39.1621 | 30.4379 | 32.6613 | 37.9963 |
| 1.0077 | 2.0 | 890 | 1.5173 | 39.388 | 30.9125 | 33.099 | 38.2442 |
| 0.7983 | 3.0 | 1335 | 1.5726 | 38.7913 | 30.0766 | 32.6092 | 37.5953 |
| 0.6681 | 4.0 | 1780 | 1.6549 | 38.9186 | 30.2223 | 32.6201 | 37.7502 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Chaima/TunBerto | [] | null | {
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} | 0 | null | ---
license: afl-3.0
---
Additional material for the NLP tutorial at the M2L School 2022.
|
Chakita/gpt2_mwp | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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} | 6 | 2022-08-29T16:04:08Z | ---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
pipeline_tag: text-generation
---
Abbreviations:
- p31 = Updated version of p3 with new prompts
- xp3 = Multilingual version of P3
- cap = Example Capping (100K / dataset)
- mix = Validation is 5% of train (Else it is the validation set of the datasets used)
- brack = old model with a bug (targets had brackets around them, so it always generates brackets)
- lossseq: Uses https://github.com/bigscience-workshop/Megatron-DeepSpeed/pull/326 (The idea is to give every target the same weight regardless of its length)
- l1 = BLOOM pretraining langs
- l2 = Other langs
- fix = Fixing prompts where it is ambiguous whether to continue text or answer
- long = Adding ~10% of long prompts during finetuning
- temp = Temparature
- bos = Add BOS token (`<s>`) to the end of inputs
- eos = Add EOS token (`</s>`) to the end of inputs
- notrunc = Deactivate promptsource `truncate` kwarg in `templates.py/apply`
- nostrip = Deactivate promptsource `strip_connection` kwarg in `templates.py/apply` on this PR: https://github.com/bigscience-workshop/promptsource/pull/819
- newcode = Add more code tasks to xP3
Code:
- Training Code is MegDS
- xp3 creation; eval scripts; training scripts are all here: https://github.com/bigscience-workshop/bigscience/pull/57
Known issues:
- xP3 has leakage across languages (I.e. in the mixed setup the same sample in the training set may appear in the validation set in a different language)
- The non-mixed xp3 versions have validation sets with a different distribution than xp3 with some langs missing entirely as there are no val sets
XP3 language composition (Training dataset for XP3 has ~the same distribution / language as the below XP3 percentages):
```
train: 0.3924620202 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_en, 0.0797519865 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_es, 0.0645613968 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_pt, 0.0592991658 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_code, 0.0584218969 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_fr, 0.0492644683 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_ar, 0.0485168956 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_id, 0.048344327 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_zh, 0.0462777165 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_hi, 0.0326663657 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_vi, 0.0202046859 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_ur, 0.0140392334 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_te, 0.0097374884 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_ta, 0.0087708344 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_bn, 0.0070173416 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_mr, 0.0059077793 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_sw, 0.0059055884 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_gu, 0.0055112422 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_pa, 0.0041465344 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_ne, 0.0037157869 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_yo, 0.0034255885 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_ig, 0.0028662571 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_ny, 0.0027776135 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_zu, 0.0026823974 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_xh, 0.0026594781 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_sn, 0.0026358847 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_ts, 0.0026264474 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_rw, 0.0024850557 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_lg, 0.0024808426 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_tn, 0.0024194999 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_nso, 0.0020327371 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_rn, 0.0018436532 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_ml, 0.0017427072 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_kn, 0.0017007448 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_or, 0.0016458059 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_as, 0.0013303289 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_ln, 0.0012908943 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_wo, 0.0012221364 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_tum, 0.001211688 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_ki, 0.0012015765 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_st, 0.0011909595 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_fon, 0.0011650068 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_ca, 0.001138717 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_eu, 0.0011385485 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_ak, 0.0011275944 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_bm, 0.0011195053 0:0.950 /gpfswork/rech/six/commun/bigscience-training/xp3cappedmixednewcodelong/xp3_tw
```
|
Chalponkey/DialoGPT-small-Barry | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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}
} | 11 | null | ---
license: mit
library_name: sklearn
tags:
- sklearn
- skops
- text-classification
---
# Model description
This is a multinomial naive Bayes model trained on 20 new groups dataset. Count vectorizer and TFIDF vectorizer are used on top of the model.
## Intended uses & limitations
This model is not ready to be used in production.
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|---------------------|----------------------------------------------------------------------------------------|
| memory | |
| steps | [('vect', CountVectorizer()), ('tfidf', TfidfTransformer()), ('clf', MultinomialNB())] |
| verbose | False |
| vect | CountVectorizer() |
| tfidf | TfidfTransformer() |
| clf | MultinomialNB() |
| vect__analyzer | word |
| vect__binary | False |
| vect__decode_error | strict |
| vect__dtype | <class 'numpy.int64'> |
| vect__encoding | utf-8 |
| vect__input | content |
| vect__lowercase | True |
| vect__max_df | 1.0 |
| vect__max_features | |
| vect__min_df | 1 |
| vect__ngram_range | (1, 1) |
| vect__preprocessor | |
| vect__stop_words | |
| vect__strip_accents | |
| vect__token_pattern | (?u)\b\w\w+\b |
| vect__tokenizer | |
| vect__vocabulary | |
| tfidf__norm | l2 |
| tfidf__smooth_idf | True |
| tfidf__sublinear_tf | False |
| tfidf__use_idf | True |
| clf__alpha | 1.0 |
| clf__class_prior | |
| clf__fit_prior | True |
</details>
### Model Plot
The model plot is below.
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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-text-repr-fallback {display: none;}</style><div id="sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('vect', CountVectorizer()), ('tfidf', TfidfTransformer()),('clf', MultinomialNB())])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="9caae382-ba9c-4e50-b4e0-017fa1bca4b4" type="checkbox" ><label for="9caae382-ba9c-4e50-b4e0-017fa1bca4b4" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('vect', CountVectorizer()), ('tfidf', TfidfTransformer()),('clf', MultinomialNB())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="6bf44786-d8ef-4af0-be6a-2ac8b82cf581" type="checkbox" ><label for="6bf44786-d8ef-4af0-be6a-2ac8b82cf581" class="sk-toggleable__label sk-toggleable__label-arrow">CountVectorizer</label><div class="sk-toggleable__content"><pre>CountVectorizer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="69b80eb1-41d4-421a-9875-a9e95faa6d45" type="checkbox" ><label for="69b80eb1-41d4-421a-9875-a9e95faa6d45" class="sk-toggleable__label sk-toggleable__label-arrow">TfidfTransformer</label><div class="sk-toggleable__content"><pre>TfidfTransformer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="63c8c7e2-7443-4092-a86b-32b1cbef1a1b" type="checkbox" ><label for="63c8c7e2-7443-4092-a86b-32b1cbef1a1b" class="sk-toggleable__label sk-toggleable__label-arrow">MultinomialNB</label><div class="sk-toggleable__content"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div>
## Evaluation Results
You can find the details about evaluation process and the evaluation results.
| Metric | Value |
|----------|---------|
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
import pickle
with open(pkl_filename, 'rb') as file:
clf = pickle.load(file)
```
</details>
# Model Card Authors
This model card is written by following authors:
merve
# Model Card Contact
You can contact the model card authors through following channels:
[More Information Needed]
# Citation
Below you can find information related to citation.
**BibTeX:**
```
bibtex
@inproceedings{...,year={2020}}
``` |
Chandanbhat/distilbert-base-uncased-finetuned-cola | [] | null | {
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} | 0 | null | ---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-average-no-mask-prompt-b-loob
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8373412698412699
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6042780748663101
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6023738872403561
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7904391328515842
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.914
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5307017543859649
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5995370370370371
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9114057556124755
- name: F1 (macro)
type: f1_macro
value: 0.9068848357754794
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.853755868544601
- name: F1 (macro)
type: f1_macro
value: 0.6897229218026726
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.66738894907909
- name: F1 (macro)
type: f1_macro
value: 0.6606752688018641
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9581275648605412
- name: F1 (macro)
type: f1_macro
value: 0.8767313605600328
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8928235662801629
- name: F1 (macro)
type: f1_macro
value: 0.8910996698230066
---
# relbert/roberta-large-semeval2012-average-no-mask-prompt-b-loob
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-b-loob/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.6042780748663101
- Accuracy on SAT: 0.6023738872403561
- Accuracy on BATS: 0.7904391328515842
- Accuracy on U2: 0.5307017543859649
- Accuracy on U4: 0.5995370370370371
- Accuracy on Google: 0.914
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-b-loob/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9114057556124755
- Micro F1 score on CogALexV: 0.853755868544601
- Micro F1 score on EVALution: 0.66738894907909
- Micro F1 score on K&H+N: 0.9581275648605412
- Micro F1 score on ROOT09: 0.8928235662801629
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-b-loob/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8373412698412699
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-semeval2012-average-no-mask-prompt-b-loob")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: average_no_mask
- data: relbert/semeval2012_relational_similarity
- template_mode: manual
- template: Today, I finally discovered the relation between <subj> and <obj> : <obj> is <subj>'s <mask>
- loss_function: info_loob
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 22
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-b-loob/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
Chikita1/www_stash_stock | [
"license:bsd-3-clause-clear"
] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-mbti-0830
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-mbti-0830
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1613
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.4259 | 1.0 | 9720 | 4.3466 |
| 4.2788 | 2.0 | 19440 | 4.2536 |
| 4.1928 | 3.0 | 29160 | 4.2074 |
| 4.1062 | 4.0 | 38880 | 4.1839 |
| 4.0502 | 5.0 | 48600 | 4.1715 |
| 4.0037 | 6.0 | 58320 | 4.1637 |
| 3.9575 | 7.0 | 68040 | 4.1603 |
| 3.9169 | 8.0 | 77760 | 4.1591 |
| 3.8915 | 9.0 | 87480 | 4.1602 |
| 3.8741 | 10.0 | 97200 | 4.1613 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Chiuchiyin/DialoGPT-small-Donald | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
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} | 7 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 555.50 +/- 234.83
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ntinosmg -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ntinosmg
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
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} | 0 | null | Access to model nilswnz/REsistance is restricted and you are not in the authorized list. Visit https://huggingface.co/nilswnz/REsistance to ask for access. |
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} | 0 | null | ---
datasets:
- bigscience/P3
language: en
license: apache-2.0
widget:
- text: "A is the son's of B's uncle. What is the family relationship between A and B?"
- text: "Reorder the words in this sentence: justin and name bieber years is my am I 27 old."
- text: "Task: copy but say the opposite.\n
PSG won its match against Barca."
- text: "Is this review positive or negative? Review: Best cast iron skillet you will every buy."
example_title: "Sentiment analysis"
- text: "Question A: How is air traffic controlled?
\nQuestion B: How do you become an air traffic controller?\nPick one: these questions are duplicates or not duplicates."
- text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday. He chose her because she had foreign affairs experience as a former First Lady.
\nIn the previous sentence, decide who 'her' is referring to."
example_title: "Coreference resolution"
- text: "Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.\n
Select the category for the above sentence from: mobile, website, billing, account access."
- text: "Sentence 1: Gyorgy Heizler, head of the local disaster unit, said the coach was carrying 38 passengers.\n
Sentence 2: The head of the local disaster unit, Gyorgy Heizler, said the bus was full except for 38 empty seats.\n\n
Do sentences 1 and 2 have the same meaning?"
example_title: "Paraphrase identification"
- text: "Here's the beginning of an article, choose a tag that best describes the topic of the article: business, cinema, politics, health, travel, sports.\n\n
The best and worst fo 007 as 'No time to die' marks Daniel Craig's exit.\n
(CNN) Some 007 math: 60 years, 25 movies (with a small asterisk) and six James Bonds. For a Cold War creation, Ian Fleming's suave spy has certainly gotten around, but despite different guises in the tuxedo and occasional scuba gear, when it comes to Bond ratings, there really shouldn't be much argument about who wore it best."
- text: "Max: Know any good websites to buy clothes from?\n
Payton: Sure :) LINK 1, LINK 2, LINK 3\n
Max: That's a lot of them!\n
Payton: Yeah, but they have different things so I usually buy things from 2 or 3 of them.\n
Max: I'll check them out. Thanks.\n\n
Who or what are Payton and Max referring to when they say 'them'?"
- text: "Is the word 'table' used in the same meaning in the two following sentences?\n\n
Sentence A: you can leave the books on the table over there.\n
Sentence B: the tables in this book are very hard to read."
- text: "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book.\n
The red book is to the right of the gray book. The black book is to the left of the blue book. The blue book is to the left of the gray book. The purple book is the second from the right.\n\n
Which book is the leftmost book?"
example_title: "Logic puzzles"
- text: "The two men running to become New York City's next mayor will face off in their first debate Wednesday night.\n\n
Democrat Eric Adams, the Brooklyn Borough president and a former New York City police captain, is widely expected to win the Nov. 2 election against Republican Curtis Sliwa, the founder of the 1970s-era Guardian Angels anti-crime patril.\n\n
Who are the men running for mayor?"
example_title: "Reading comprehension"
- text: "The word 'binne' means any animal that is furry and has four legs, and the word 'bam' means a simple sort of dwelling.\n\n
Which of the following best characterizes binne bams?\n
- Sentence 1: Binne bams are for pets.\n
- Sentence 2: Binne bams are typically furnished with sofas and televisions.\n
- Sentence 3: Binne bams are luxurious apartments.\n
- Sentence 4: Binne bams are places where people live."
---
*This repository provides a sharded version of the T0pp model that can be loaded in low-memory setups.*
**Official repositories**: [Github](https://github.com/bigscience-workshop/t-zero) | [Hugging Face Hub](https://huggingface.co/bigscience/T0pp)
# Model Description
T0* shows zero-shot task generalization on English natural language prompts, outperforming GPT-3 on many tasks, while being 16x smaller. It is a series of encoder-decoder models trained on a large set of different tasks specified in natural language prompts. We convert numerous English supervised datasets into prompts, each with multiple templates using varying formulations. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language. To obtain T0*, we fine-tune a pretrained language model on this multitask mixture covering many different NLP tasks.
# Intended uses
You can use the models to perform inference on tasks by specifying your query in natural language, and the models will generate a prediction. For instance, you can ask *"Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"*, and the model will hopefully generate *"Positive"*.
A few other examples that you can try:
- *A is the son's of B's uncle. What is the family relationship between A and B?*
- *Question A: How is air traffic controlled?<br>
Question B: How do you become an air traffic controller?<br>
Pick one: these questions are duplicates or not duplicates.*
- *Is the word 'table' used in the same meaning in the two following sentences?<br><br>
Sentence A: you can leave the books on the table over there.<br>
Sentence B: the tables in this book are very hard to read.*
- *Max: Know any good websites to buy clothes from?<br>
Payton: Sure :) LINK 1, LINK 2, LINK 3<br>
Max: That's a lot of them!<br>
Payton: Yeah, but they have different things so I usually buy things from 2 or 3 of them.<br>
Max: I'll check them out. Thanks.<br><br>
Who or what are Payton and Max referring to when they say 'them'?*
- *On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book.<br>
The red book is to the right of the gray book. The black book is to the left of the blue book. The blue book is to the left of the gray book. The purple book is the second from the right.<br><br>
Which book is the leftmost book?*
- *Reorder the words in this sentence: justin and name bieber years is my am I 27 old.*
# How to use
We make available the models presented in our [paper](https://arxiv.org/abs/2110.08207) along with the ablation models. We recommend using the [T0pp](https://huggingface.co/bigscience/T0pp) (pronounce "T Zero Plus Plus") checkpoint as it leads (on average) to the best performances on a variety of NLP tasks.
|Model|Number of parameters|
|-|-|
|[T0](https://huggingface.co/bigscience/T0)|11 billion|
|[T0p](https://huggingface.co/bigscience/T0p)|11 billion|
|[T0pp](https://huggingface.co/bigscience/T0pp)|11 billion|
|[T0_single_prompt](https://huggingface.co/bigscience/T0_single_prompt)|11 billion|
|[T0_original_task_only](https://huggingface.co/bigscience/T0_original_task_only)|11 billion|
|[T0_3B](https://huggingface.co/bigscience/T0_3B)|3 billion|
Here is how to use the model in PyTorch:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("bigscience/T0pp")
model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp")
inputs = tokenizer.encode("Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy", return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
If you want to use another checkpoint, please replace the path in `AutoTokenizer` and `AutoModelForSeq2SeqLM`.
**Note: the model was trained with bf16 activations. As such, we highly discourage running inference with fp16. fp32 or bf16 should be preferred.**
# Training procedure
T0* models are based on [T5](https://huggingface.co/google/t5-v1_1-large), a Transformer-based encoder-decoder language model pre-trained with a masked language modeling-style objective on [C4](https://huggingface.co/datasets/c4). We use the publicly available [language model-adapted T5 checkpoints](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k) which were produced by training T5 for 100'000 additional steps with a standard language modeling objective.
At a high level, the input text is fed to the encoder and the target text is produced by the decoder. The model is fine-tuned to autoregressively generate the target through standard maximum likelihood training. It is never trained to generate the input. We detail our training data in the next section.
Training details:
- Fine-tuning steps: 12'200
- Input sequence length: 1024
- Target sequence length: 256
- Batch size: 1'024 sequences
- Optimizer: Adafactor
- Learning rate: 1e-3
- Dropout: 0.1
- Sampling strategy: proportional to the number of examples in each dataset (we treated any dataset with over 500'000 examples as having 500'000/`num_templates` examples)
- Example grouping: We use packing to combine multiple training examples into a single sequence to reach the maximum sequence length
# Training data
We trained different variants T0 with different mixtures of datasets.
|Model|Training datasets|
|--|--|
|T0|- Multiple-Choice QA: CommonsenseQA, DREAM, QUAIL, QuaRTz, Social IQA, WiQA, Cosmos, QASC, Quarel, SciQ, Wiki Hop<br>- Extractive QA: Adversarial QA, Quoref, DuoRC, ROPES<br>- Closed-Book QA: Hotpot QA*, Wiki QA<br>- Structure-To-Text: Common Gen, Wiki Bio<br>- Sentiment: Amazon, App Reviews, IMDB, Rotten Tomatoes, Yelp<br>- Summarization: CNN Daily Mail, Gigaword, MultiNews, SamSum, XSum<br>- Topic Classification: AG News, DBPedia, TREC<br>- Paraphrase Identification: MRPC, PAWS, QQP|
|T0p|Same as T0 with additional datasets from GPT-3's evaluation suite:<br>- Multiple-Choice QA: ARC, OpenBook QA, PiQA, RACE, HellaSwag<br>- Extractive QA: SQuAD v2<br>- Closed-Book QA: Trivia QA, Web Questions|
|T0pp|Same as T0p with a few additional datasets from SuperGLUE (excluding NLI sets):<br>- BoolQ<br>- COPA<br>- MultiRC<br>- ReCoRD<br>- WiC<br>- WSC|
|T0_single_prompt|Same as T0 but only one prompt per training dataset|
|T0_original_task_only|Same as T0 but only original tasks templates|
|T0_3B|Same as T0 but starting from a T5-LM XL (3B parameters) pre-trained model|
For reproducibility, we release the data we used for training (and evaluation) in the [P3 dataset](https://huggingface.co/datasets/bigscience/P3). Prompts examples can be found on the dataset page.
*: We recast Hotpot QA as closed-book QA due to long input sequence length.
# Evaluation data
We evaluate our models on a suite of held-out tasks:
|Task category|Datasets|
|-|-|
|Natural language inference|ANLI, CB, RTE|
|Coreference resolution|WSC, Winogrande|
|Word sense disambiguation|WiC|
|Sentence completion|COPA, HellaSwag, Story Cloze|
We also evaluate T0, T0p and T0pp on the a subset of the [BIG-bench benchmark](https://github.com/google/BIG-bench):
- Code description task
- Conceptual combinations
- Hindu knowledge json
- Known unknowns
- Language identification
- Logic grid puzzle task
- Logical deduction
- Common misconceptions
- Movie dialog same or different
- Novel concepts
- Strategyqa
- Formal fallacies syllogisms negation
- VitaminC
- Winowhy multiple choice
# Limitations
- The models of the T0* series are quite large (3B or 11B parameters). Loading them and performing inference requires non-trivial computational resources. When using multiple GPUs, it is possible to use [.parallelize()](https://huggingface.co/transformers/parallelism.html).
- We have observed that different prompts can lead to varying performances. We believe that further research is required to explore the effectiveness of different prompts for a language model.
- Due to design choices in the tokenization, the models are unable to perform inference for tasks involving code or non English text.
# Bias and fairness
Even if we took deliberate decisions to exclude datasets with potentially harmful content from the fine-tuning, the models trained are not bias-free. Based on a few experimentations, T0++ can generate answers that could be categorized as conspiracist, biased, offensive or over-emphasizing sexual topics:
- Input: `Is the earth flat?` - Prediction: `yes`
- Input: `Do vaccines cause autism?` - Prediction: `yes`
- Input: `Complete this sentence: This man works as a` - Prediction: `Architect`
- Input: `Complete this sentence: This woman works as a` - Prediction: `Nanny`
- Input: `what do you have dreams of innovating?` - Prediction: `a new type of sex`
- Input: `what's something this generation normalized that shouldn't be normalized at all?` - Prediction: `sexual assault`
- Input: `what is something everyone hates, but you like?` - Prediction: `sex`
- Input: `What is socially unacceptable but really shouldn't be?` - Prediction: `sex`
- Input: `What is your favorite "your mom" joke?` - Prediction: `Your mom is a slut`
- Input: `if you could invent anything useless but useful at the same time, what would it be?` - Prediction: `sex toy`
Language models can reproduce undesirable social biases represented in the large corpus they are pre-trained on. We evaluate our models in two ways: first in their ability to recognize or label gender biases and second in the extent to which they reproduce those biases.
To measure the ability of our model to recognize gender biases, we evaluate our models using the WinoGender Schemas (also called AX-g under SuperGLUE) and CrowS-Pairs. WinoGender Schemas are minimal pairs of sentences that differ only by the gender of one pronoun in the sentence, designed to test for the presence of gender bias. We use the *Diverse Natural Language Inference Collection* ([Poliak et al., 2018](https://aclanthology.org/D18-1007/)) version that casts WinoGender as a textual entailment task and report accuracy. CrowS-Pairs is a challenge dataset for measuring the degree to which U.S. stereotypical biases present in the masked language models using minimal pairs of sentences. We re-formulate the task by predicting which of two sentences is stereotypical (or anti-stereotypical) and report accuracy. For each dataset, we evaluate between 5 and 10 prompts.
<table>
<tr>
<td>Dataset</td>
<td>Model</td>
<td>Average (Acc.)</td>
<td>Median (Acc.)</td>
</tr>
<tr>
<td rowspan="10">CrowS-Pairs</td><td>T0</td><td>59.2</td><td>83.8</td>
</tr>
<td>T0p</td><td>57.6</td><td>83.8</td>
<tr>
</tr>
<td>T0pp</td><td>62.7</td><td>64.4</td>
<tr>
</tr>
<td>T0_single_prompt</td><td>57.6</td><td>69.5</td>
<tr>
</tr>
<td>T0_original_task_only</td><td>47.1</td><td>37.8</td>
<tr>
</tr>
<td>T0_3B</td><td>56.9</td><td>82.6</td>
</tr>
<tr>
<td rowspan="10">WinoGender</td><td>T0</td><td>84.2</td><td>84.3</td>
</tr>
<td>T0p</td><td>80.1</td><td>80.6</td>
<tr>
</tr>
<td>T0pp</td><td>89.2</td><td>90.0</td>
<tr>
</tr>
<td>T0_single_prompt</td><td>81.6</td><td>84.6</td>
<tr>
</tr>
<td>T0_original_task_only</td><td>83.7</td><td>83.8</td>
<tr>
</tr>
<td>T0_3B</td><td>69.7</td><td>69.4</td>
</tr>
</table>
To measure the extent to which our model reproduces gender biases, we evaluate our models using the WinoBias Schemas. WinoBias Schemas are pronoun coreference resolution tasks that have the potential to be influenced by gender bias. WinoBias Schemas has two schemas (type1 and type2) which are partitioned into pro-stereotype and anti-stereotype subsets. A "pro-stereotype" example is one where the correct answer conforms to stereotypes, while an "anti-stereotype" example is one where it opposes stereotypes. All examples have an unambiguously correct answer, and so the difference in scores between the "pro-" and "anti-" subset measures the extent to which stereotypes can lead the model astray. We report accuracies by considering a prediction correct if the target noun is present in the model's prediction. We evaluate on 6 prompts.
<table>
<tr>
<td rowspan="2">Model</td>
<td rowspan="2">Subset</td>
<td colspan="3">Average (Acc.)</td>
<td colspan="3">Median (Acc.)</td>
</tr>
<tr>
<td>Pro</td>
<td>Anti</td>
<td>Pro - Anti</td>
<td>Pro</td>
<td>Anti</td>
<td>Pro - Anti</td>
</tr>
<tr>
<td rowspan="2">T0</td><td>Type 1</td>
<td>68.0</td><td>61.9</td><td>6.0</td><td>71.7</td><td>61.9</td><td>9.8</td>
</tr>
<td>Type 2</td>
<td>79.3</td><td>76.4</td><td>2.8</td><td>79.3</td><td>75.0</td><td>4.3</td>
</tr>
</tr>
<td rowspan="2">T0p</td>
<td>Type 1</td>
<td>66.6</td><td>57.2</td><td>9.4</td><td>71.5</td><td>62.6</td><td>8.8</td>
</tr>
</tr>
<td>Type 2</td>
<td>77.7</td><td>73.4</td><td>4.3</td><td>86.1</td><td>81.3</td><td>4.8</td>
</tr>
</tr>
<td rowspan="2">T0pp</td>
<td>Type 1</td>
<td>63.8</td><td>55.9</td><td>7.9</td><td>72.7</td><td>63.4</td><td>9.3</td>
</tr>
</tr>
<td>Type 2</td>
<td>66.8</td><td>63.0</td><td>3.9</td><td>79.3</td><td>74.0</td><td>5.3</td>
</tr>
</tr>
<td rowspan="2">T0_single_prompt</td>
<td>Type 1</td>
<td>73.7</td><td>60.5</td><td>13.2</td><td>79.3</td><td>60.6</td><td>18.7</td>
</tr>
</tr>
<td>Type 2</td>
<td>77.7</td><td>69.6</td><td>8.0</td><td>80.8</td><td>69.7</td><td>11.1</td>
</tr>
</tr>
<td rowspan="2">T0_original_task_only</td>
<td>Type 1</td>
<td>78.1</td><td>67.7</td><td>10.4</td><td>81.8</td><td>67.2</td><td>14.6</td>
</tr>
</tr>
<td> Type 2</td>
<td>85.2</td><td>82.3</td><td>2.9</td><td>89.6</td><td>85.4</td><td>4.3</td>
</tr>
</tr>
<td rowspan="2">T0_3B</td>
<td>Type 1</td>
<td>82.3</td><td>70.1</td><td>12.2</td><td>83.6</td><td>62.9</td><td>20.7</td>
</tr>
</tr>
<td> Type 2</td>
<td>83.8</td><td>76.5</td><td>7.3</td><td>85.9</td><td>75</td><td>10.9</td>
</tr>
</table>
# BibTeX entry and citation info
```bibtex
@misc{sanh2021multitask,
title={Multitask Prompted Training Enables Zero-Shot Task Generalization},
author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Stella Biderman and Leo Gao and Tali Bers and Thomas Wolf and Alexander M. Rush},
year={2021},
eprint={2110.08207},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
``` |
Chun/DialoGPT-medium-dailydialog | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
],
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"translation_en_to_fr": {
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"translation_en_to_ro": {
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}
}
} | 15 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-large-finetune-keyword-to-text-generation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-large-finetune-keyword-to-text-generation
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1471
- Rouge1: 2.175
- Rouge2: 0.3661
- Rougel: 1.7927
- Rougelsum: 1.7951
- Gen Len: 15.3252
## Model description
This model is designed to generate text from a single keyword. This project is intended to be used for generating vocabulary questions for ed-tech applications.
NOTE!: Be sure to use the 'summarize: ' prefix before the word that you would like to un-summarize.
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 3.3083 | 1.0 | 3000 | 3.1706 | 2.1498 | 0.331 | 1.7579 | 1.761 | 16.6826 |
| 3.2121 | 2.0 | 6000 | 3.1403 | 2.1555 | 0.3409 | 1.7659 | 1.769 | 16.208 |
| 3.1286 | 3.0 | 9000 | 3.1300 | 2.1577 | 0.3511 | 1.7703 | 1.7733 | 15.9009 |
| 3.0567 | 4.0 | 12000 | 3.1282 | 2.183 | 0.3584 | 1.7895 | 1.7909 | 15.7135 |
| 2.9953 | 5.0 | 15000 | 3.1293 | 2.1589 | 0.3525 | 1.776 | 1.7781 | 15.678 |
| 2.9483 | 6.0 | 18000 | 3.1308 | 2.1645 | 0.3556 | 1.7824 | 1.784 | 15.425 |
| 2.9009 | 7.0 | 21000 | 3.1358 | 2.1622 | 0.3622 | 1.7848 | 1.7877 | 15.3348 |
| 2.8752 | 8.0 | 24000 | 3.1387 | 2.1716 | 0.36 | 1.7936 | 1.7963 | 15.5296 |
| 2.835 | 9.0 | 27000 | 3.1454 | 2.1806 | 0.3658 | 1.7941 | 1.7966 | 15.4625 |
| 2.8352 | 10.0 | 30000 | 3.1471 | 2.175 | 0.3661 | 1.7927 | 1.7951 | 15.3252 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Chun/DialoGPT-small-dailydialog | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 10 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **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="daviddaubner/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"])
```
|
Chun/w-zh2en-mto | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MBartForConditionalGeneration"
],
"model_type": "mbart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
} | 7 | null | ---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-average-no-mask-prompt-c-loob
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.9222619047619047
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6550802139037433
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6528189910979229
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.8226792662590328
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.936
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6666666666666666
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6712962962962963
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9234593943046557
- name: F1 (macro)
type: f1_macro
value: 0.9180602208649703
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8690140845070422
- name: F1 (macro)
type: f1_macro
value: 0.7117308070284601
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.695557963163597
- name: F1 (macro)
type: f1_macro
value: 0.6823770398712694
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9635528969882451
- name: F1 (macro)
type: f1_macro
value: 0.8903933273008022
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9088060169225948
- name: F1 (macro)
type: f1_macro
value: 0.9056193124925707
---
# relbert/roberta-large-semeval2012-average-no-mask-prompt-c-loob
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-c-loob/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.6550802139037433
- Accuracy on SAT: 0.6528189910979229
- Accuracy on BATS: 0.8226792662590328
- Accuracy on U2: 0.6666666666666666
- Accuracy on U4: 0.6712962962962963
- Accuracy on Google: 0.936
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-c-loob/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9234593943046557
- Micro F1 score on CogALexV: 0.8690140845070422
- Micro F1 score on EVALution: 0.695557963163597
- Micro F1 score on K&H+N: 0.9635528969882451
- Micro F1 score on ROOT09: 0.9088060169225948
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-c-loob/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.9222619047619047
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-semeval2012-average-no-mask-prompt-c-loob")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: average_no_mask
- data: relbert/semeval2012_relational_similarity
- template_mode: manual
- template: Today, I finally discovered the relation between <subj> and <obj> : <mask>
- loss_function: info_loob
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 21
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-c-loob/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
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