readme: ---
  language: "en"
  tags:
  - gpt2
  - exbert
  - commonsense
  - semeval2020
  - comve
  license: "mit"
  datasets:
  - ComVE
  metrics:
  - bleu
  widget:
  - text: "Chicken can swim in water. <|continue|>"
  ---
  
  # ComVE-gpt2-medium
  
  ## 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-medium](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 fifth place with 16.7153/16.1187 BLEU scores and third place with 1.94 Human Evaluation score on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.
  
  These are some examples generated by the model:
  |             Against Commonsense Statement             |               Generated Reason               |
  |:-----------------------------------------------------:|:--------------------------------------------:|
  | Chicken can swim in water.                            | Chicken can't swim.                          |
  | shoes can fly                                         | Shoes are not able to fly.                   |
  | Chocolate can be used to make a coffee pot            | Chocolate is not used to make coffee pots.   |
  | you can also buy tickets online with an identity card | You can't buy tickets with an identity card. |
  | a ball is square and can roll                         | A ball is round and cannot roll.             |
  | You can use detergent to dye your hair.               | Detergent is used to wash clothes.           |
  | you can eat mercury                                   | mercury is poisonous                         |
  | A gardener can follow a suspect                       | gardener is not a police officer             |
  | cars can float in the ocean just like a boat          | Cars are too heavy to float in the ocean.    |
  | I am going to work so I can lose money.               | Working is not a way to lose money.          |
  
  ### 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-medium">
  	<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
  </a>
  