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1d72460d55
* Add ComVE model cards * Apply suggestions from code review Co-authored-by: Julien Chaumond <chaumond@gmail.com>
68 lines
2.6 KiB
Markdown
68 lines
2.6 KiB
Markdown
---
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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-gpt2
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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 [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.
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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, 5 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 14.0547/13.6534 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-gpt2">
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<img width="300px" src="https://hf-dinosaur.huggingface.co/exbert/button.png">
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</a>
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