readme: ---
  language: 
  - en
  tags:
  - text-classification
  widget:
   - text: "I almost forgot to eat lunch.</s></s>I didn't forget to eat lunch."
   - text: "I almost forgot to eat lunch.</s></s>I forgot to eat lunch."
   - text: "I ate lunch.</s></s>I almost forgot to eat lunch."
  datasets:
   - alisawuffles/WANLI
  ---
  
  This is an off-the-shelf roberta-large model finetuned on WANLI, the Worker-AI Collaborative NLI dataset ([Liu et al., 2022](https://arxiv.org/abs/2201.05955)). It outperforms the `roberta-large-mnli` model on eight out-of-domain test sets, including by 11% on HANS and 9% on Adversarial NLI.
  
  ### How to use
  ```python
  from transformers import RobertaTokenizer, RobertaForSequenceClassification
  
  model = RobertaForSequenceClassification.from_pretrained('alisawuffles/roberta-large-wanli')
  tokenizer = RobertaTokenizer.from_pretrained('alisawuffles/roberta-large-wanli')
  
  x = tokenizer("I almost forgot to eat lunch.", "I didn't forget to eat lunch.", return_tensors='pt', max_length=128, truncation=True)
  logits = model(**x).logits
  probs = logits.softmax(dim=1).squeeze(0)
  label_id = torch.argmax(probs).item()
  prediction = model.config.id2label[label_id]
  ```
  
  ### Citation
  ```
  @misc{liu-etal-2022-wanli,
      title = "WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation",
      author = "Liu, Alisa  and
        Swayamdipta, Swabha  and
        Smith, Noah A.  and
        Choi, Yejin",
      month = jan,
      year = "2022",
      url = "https://arxiv.org/pdf/2201.05955",
  }
  ```