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--- |
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language: "en" |
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tags: |
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- exbert |
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- commonsense |
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- semeval2020 |
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- comve |
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license: "mit" |
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datasets: |
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- ComVE |
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metrics: |
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- bleu |
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widget: |
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- text: "Chicken can swim in water. <|continue|>" |
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--- |
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# ComVE-distilgpt2 |
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## Model description |
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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. |
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The model is able to generate a reason why a given natural language statement is against commonsense. |
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## Intended uses & limitations |
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You can use the raw model for text generation to generate reasons why natural language statements are against commonsense. |
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#### How to use |
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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. |
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*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. |
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#### Limitations and bias |
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The model biased to negate the entered sentence usually instead of producing a factual reason. |
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## Training data |
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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. |
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## Training procedure |
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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. |
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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. |
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<center> |
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<img src="https://i.imgur.com/xKbrwBC.png"> |
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</center> |
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## Eval results |
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The model achieved 13.7582/13.8026 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset. |
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### BibTeX entry and citation info |
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```bibtex |
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@article{fadel2020justers, |
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title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation}, |
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author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik}, |
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year={2020} |
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} |
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``` |
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<a href="https://huggingface.co/exbert/?model=aliosm/ComVE-distilgpt2"> |
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<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> |
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</a> |
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