Add ComVE model cards (#5884)

* Add ComVE model cards * Apply suggestions from code review Co-authored-by: Julien Chaumond <chaumond@gmail.com>

Add ComVE model cards (#5884)
* Add ComVE model cards * Apply suggestions from code review Co-authored-by: Julien Chaumond <chaumond@gmail.com>
1d72460d · Ali Hamdi Ali Fadel · GitHub · ccbf74a6 · 1d72460d · 1d72460d
Unverified Commit 1d72460d authored Jul 21, 2020 by Ali Hamdi Ali Fadel Committed by GitHub Jul 21, 2020
4 changed files
--- a/model_cards/aliosm/ComVE-distilgpt2/README.md
+++ b/model_cards/aliosm/ComVE-distilgpt2/README.md
+---
+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>
--- a/model_cards/aliosm/ComVE-gpt2-large/README.md
+++ b/model_cards/aliosm/ComVE-gpt2-large/README.md
+---
+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>
--- a/model_cards/aliosm/ComVE-gpt2-medium/README.md
+++ b/model_cards/aliosm/ComVE-gpt2-medium/README.md
+---
+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>
--- a/model_cards/aliosm/ComVE-gpt2/README.md
+++ b/model_cards/aliosm/ComVE-gpt2/README.md
+---
+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>