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