Add ComVE model cards (#5884)
* Add ComVE model cards * Apply suggestions from code review Co-authored-by: Julien Chaumond <chaumond@gmail.com>
This commit is contained in:
Ali Hamdi Ali Fadel
GitHub
parent
ccbf74a685
commit
1d72460d55
|
|
@ -0,0 +1,67 @@
|
|||
---
|
||||
language: "en"
|
||||
tags:
|
||||
- exbert
|
||||
- commonsense
|
||||
- semeval2020
|
||||
- comve
|
||||
license: "mit"
|
||||
datasets:
|
||||
- ComVE
|
||||
metrics:
|
||||
- bleu
|
||||
widget:
|
||||
- text: "Chicken can swim in water. <|continue|>"
|
||||
---
|
||||
|
||||
# ComVE-distilgpt2
|
||||
|
||||
## Model description
|
||||
|
||||
Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval2020 Task4](https://competitions.codalab.org/competitions/21080) using a causal language modeling (CLM) objective.
|
||||
The model is able to generate a reason why a given natural language statement is against commonsense.
|
||||
|
||||
## Intended uses & limitations
|
||||
|
||||
You can use the raw model for text generation to generate reasons why natural language statements are against commonsense.
|
||||
|
||||
#### How to use
|
||||
|
||||
You can use this model directly to generate reasons why the given statement is against commonsense using [`generate.sh`](https://github.com/AliOsm/SemEval2020-Task4-ComVE/tree/master/TaskC-Generation) script.
|
||||
|
||||
*Note:* make sure that you are using version `2.4.1` of `transformers` package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.
|
||||
|
||||
#### Limitations and bias
|
||||
|
||||
The model biased to negate the entered sentence usually instead of producing a factual reason.
|
||||
|
||||
## Training data
|
||||
|
||||
The model is initialized from the [distilgpt2](https://github.com/huggingface/transformers/blob/master/model_cards/distilgpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.
|
||||
|
||||
## Training procedure
|
||||
|
||||
Each natural language statement that against commonsense is concatenated with its reference reason with `<|continue|>` as a separator, then the model finetuned using CLM objective.
|
||||
The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 15 epochs, 128 maximum sequence length and 64 batch size.
|
||||
|
||||
<center>
|
||||
<img src="https://i.imgur.com/xKbrwBC.png">
|
||||
</center>
|
||||
|
||||
## Eval results
|
||||
|
||||
The model achieved 13.7582/13.8026 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.
|
||||
|
||||
### BibTeX entry and citation info
|
||||
|
||||
```bibtex
|
||||
@article{fadel2020justers,
|
||||
title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation},
|
||||
author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik},
|
||||
year={2020}
|
||||
}
|
||||
```
|
||||
|
||||
<a href="https://huggingface.co/exbert/?model=aliosm/ComVE-distilgpt2">
|
||||
<img width="300px" src="https://hf-dinosaur.huggingface.co/exbert/button.png">
|
||||
</a>
|
||||
|
|
@ -0,0 +1,68 @@
|
|||
---
|
||||
language: "en"
|
||||
tags:
|
||||
- gpt2
|
||||
- exbert
|
||||
- commonsense
|
||||
- semeval2020
|
||||
- comve
|
||||
license: "mit"
|
||||
datasets:
|
||||
- https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation
|
||||
metrics:
|
||||
- bleu
|
||||
widget:
|
||||
- text: "Chicken can swim in water. <|continue|>"
|
||||
---
|
||||
|
||||
# ComVE-gpt2-large
|
||||
|
||||
## Model description
|
||||
|
||||
Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval2020 Task4](https://competitions.codalab.org/competitions/21080) using a causal language modeling (CLM) objective.
|
||||
The model is able to generate a reason why a given natural language statement is against commonsense.
|
||||
|
||||
## Intended uses & limitations
|
||||
|
||||
You can use the raw model for text generation to generate reasons why natural language statements are against commonsense.
|
||||
|
||||
#### How to use
|
||||
|
||||
You can use this model directly to generate reasons why the given statement is against commonsense using [`generate.sh`](https://github.com/AliOsm/SemEval2020-Task4-ComVE/tree/master/TaskC-Generation) script.
|
||||
|
||||
*Note:* make sure that you are using version `2.4.1` of `transformers` package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.
|
||||
|
||||
#### Limitations and bias
|
||||
|
||||
The model biased to negate the entered sentence usually instead of producing a factual reason.
|
||||
|
||||
## Training data
|
||||
|
||||
The model is initialized from the [gpt2-large](https://github.com/huggingface/transformers/blob/master/model_cards/gpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.
|
||||
|
||||
## Training procedure
|
||||
|
||||
Each natural language statement that against commonsense is concatenated with its reference reason with `<|conteniue|>` as a separator, then the model finetuned using CLM objective.
|
||||
The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.
|
||||
|
||||
<center>
|
||||
<img src="https://i.imgur.com/xKbrwBC.png">
|
||||
</center>
|
||||
|
||||
## Eval results
|
||||
|
||||
The model achieved 16.5110/15.9299 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.
|
||||
|
||||
### BibTeX entry and citation info
|
||||
|
||||
```bibtex
|
||||
@article{fadel2020justers,
|
||||
title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation},
|
||||
author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik},
|
||||
year={2020}
|
||||
}
|
||||
```
|
||||
|
||||
<a href="https://huggingface.co/exbert/?model=aliosm/ComVE-gpt2-large">
|
||||
<img width="300px" src="https://hf-dinosaur.huggingface.co/exbert/button.png">
|
||||
</a>
|
||||
|
|
@ -0,0 +1,82 @@
|
|||
---
|
||||
language: "en"
|
||||
tags:
|
||||
- gpt2
|
||||
- exbert
|
||||
- commonsense
|
||||
- semeval2020
|
||||
- comve
|
||||
license: "mit"
|
||||
datasets:
|
||||
- ComVE
|
||||
metrics:
|
||||
- bleu
|
||||
widget:
|
||||
- text: "Chicken can swim in water. <|continue|>"
|
||||
---
|
||||
|
||||
# ComVE-gpt2-medium
|
||||
|
||||
## Model description
|
||||
|
||||
Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval2020 Task4](https://competitions.codalab.org/competitions/21080) using a causal language modeling (CLM) objective.
|
||||
The model is able to generate a reason why a given natural language statement is against commonsense.
|
||||
|
||||
## Intended uses & limitations
|
||||
|
||||
You can use the raw model for text generation to generate reasons why natural language statements are against commonsense.
|
||||
|
||||
#### How to use
|
||||
|
||||
You can use this model directly to generate reasons why the given statement is against commonsense using [`generate.sh`](https://github.com/AliOsm/SemEval2020-Task4-ComVE/tree/master/TaskC-Generation) script.
|
||||
|
||||
*Note:* make sure that you are using version `2.4.1` of `transformers` package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.
|
||||
|
||||
#### Limitations and bias
|
||||
|
||||
The model biased to negate the entered sentence usually instead of producing a factual reason.
|
||||
|
||||
## Training data
|
||||
|
||||
The model is initialized from the [gpt2-medium](https://github.com/huggingface/transformers/blob/master/model_cards/gpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.
|
||||
|
||||
## Training procedure
|
||||
|
||||
Each natural language statement that against commonsense is concatenated with its reference reason with `<|continue|>` as a separator, then the model finetuned using CLM objective.
|
||||
The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.
|
||||
|
||||
<center>
|
||||
<img src="https://i.imgur.com/xKbrwBC.png">
|
||||
</center>
|
||||
|
||||
## Eval results
|
||||
|
||||
The model achieved fifth place with 16.7153/16.1187 BLEU scores and third place with 1.94 Human Evaluation score on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.
|
||||
|
||||
These are some examples generated by the model:
|
||||
| Against Commonsense Statement | Generated Reason |
|
||||
|:-----------------------------------------------------:|:--------------------------------------------:|
|
||||
| Chicken can swim in water. | Chicken can't swim. |
|
||||
| shoes can fly | Shoes are not able to fly. |
|
||||
| Chocolate can be used to make a coffee pot | Chocolate is not used to make coffee pots. |
|
||||
| you can also buy tickets online with an identity card | You can't buy tickets with an identity card. |
|
||||
| a ball is square and can roll | A ball is round and cannot roll. |
|
||||
| You can use detergent to dye your hair. | Detergent is used to wash clothes. |
|
||||
| you can eat mercury | mercury is poisonous |
|
||||
| A gardener can follow a suspect | gardener is not a police officer |
|
||||
| cars can float in the ocean just like a boat | Cars are too heavy to float in the ocean. |
|
||||
| I am going to work so I can lose money. | Working is not a way to lose money. |
|
||||
|
||||
### BibTeX entry and citation info
|
||||
|
||||
```bibtex
|
||||
@article{fadel2020justers,
|
||||
title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation},
|
||||
author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik},
|
||||
year={2020}
|
||||
}
|
||||
```
|
||||
|
||||
<a href="https://huggingface.co/exbert/?model=aliosm/ComVE-gpt2-medium">
|
||||
<img width="300px" src="https://hf-dinosaur.huggingface.co/exbert/button.png">
|
||||
</a>
|
||||
|
|
@ -0,0 +1,67 @@
|
|||
---
|
||||
language: "en"
|
||||
tags:
|
||||
- exbert
|
||||
- commonsense
|
||||
- semeval2020
|
||||
- comve
|
||||
license: "mit"
|
||||
datasets:
|
||||
- ComVE
|
||||
metrics:
|
||||
- bleu
|
||||
widget:
|
||||
- text: "Chicken can swim in water. <|continue|>"
|
||||
---
|
||||
|
||||
# ComVE-gpt2
|
||||
|
||||
## Model description
|
||||
|
||||
Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval2020 Task4](https://competitions.codalab.org/competitions/21080) using a causal language modeling (CLM) objective.
|
||||
The model is able to generate a reason why a given natural language statement is against commonsense.
|
||||
|
||||
## Intended uses & limitations
|
||||
|
||||
You can use the raw model for text generation to generate reasons why natural language statements are against commonsense.
|
||||
|
||||
#### How to use
|
||||
|
||||
You can use this model directly to generate reasons why the given statement is against commonsense using [`generate.sh`](https://github.com/AliOsm/SemEval2020-Task4-ComVE/tree/master/TaskC-Generation) script.
|
||||
|
||||
*Note:* make sure that you are using version `2.4.1` of `transformers` package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.
|
||||
|
||||
#### Limitations and bias
|
||||
|
||||
The model biased to negate the entered sentence usually instead of producing a factual reason.
|
||||
|
||||
## Training data
|
||||
|
||||
The model is initialized from the [gpt2](https://github.com/huggingface/transformers/blob/master/model_cards/gpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.
|
||||
|
||||
## Training procedure
|
||||
|
||||
Each natural language statement that against commonsense is concatenated with its reference reason with `<|continue|>` as a separator, then the model finetuned using CLM objective.
|
||||
The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.
|
||||
|
||||
<center>
|
||||
<img src="https://i.imgur.com/xKbrwBC.png">
|
||||
</center>
|
||||
|
||||
## Eval results
|
||||
|
||||
The model achieved 14.0547/13.6534 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.
|
||||
|
||||
### BibTeX entry and citation info
|
||||
|
||||
```bibtex
|
||||
@article{fadel2020justers,
|
||||
title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation},
|
||||
author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik},
|
||||
year={2020}
|
||||
}
|
||||
```
|
||||
|
||||
<a href="https://huggingface.co/exbert/?model=aliosm/ComVE-gpt2">
|
||||
<img width="300px" src="https://hf-dinosaur.huggingface.co/exbert/button.png">
|
||||
</a>
|
||||
Loading…
Reference in New Issue