| --- |
| 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://cdn-media.huggingface.co/exbert/button.png"> |
| </a> |
| |
