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
committed by
GitHub
GitHub
parent
ccbf74a685
commit
1d72460d55
67
model_cards/aliosm/ComVE-distilgpt2/README.md
Normal file
67
model_cards/aliosm/ComVE-distilgpt2/README.md
Normal file
@@ -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>
|
||||||
68
model_cards/aliosm/ComVE-gpt2-large/README.md
Normal file
68
model_cards/aliosm/ComVE-gpt2-large/README.md
Normal file
@@ -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>
|
||||||
82
model_cards/aliosm/ComVE-gpt2-medium/README.md
Normal file
82
model_cards/aliosm/ComVE-gpt2-medium/README.md
Normal file
@@ -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>
|
||||||
67
model_cards/aliosm/ComVE-gpt2/README.md
Normal file
67
model_cards/aliosm/ComVE-gpt2/README.md
Normal file
@@ -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>
|
||||||
Reference in New Issue
Block a user