readme: ---
  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>
  