readme: ---
  datasets:
  - snli
  - anli
  - multi_nli
  - multi_nli_mismatch
  - fever
  license: mit
  ---
  This is a strong pre-trained RoBERTa-Large NLI model.  
  
  The training data is a combination of well-known NLI datasets: [`SNLI`](https://nlp.stanford.edu/projects/snli/), [`MNLI`](https://cims.nyu.edu/~sbowman/multinli/), [`FEVER-NLI`](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [`ANLI (R1, R2, R3)`](https://github.com/facebookresearch/anli).  
  Other pre-trained NLI models including `RoBERTa`, `ALBert`, `BART`, `ELECTRA`, `XLNet` are also available.  
  
  Trained by [Yixin Nie](https://easonnie.github.io), [original source](https://github.com/facebookresearch/anli).
  
  Try the code snippet below.
  ```
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  import torch
  
  if __name__ == '__main__':
      max_length = 256
  
      premise = "Two women are embracing while holding to go packages."
      hypothesis = "The men are fighting outside a deli."
  
      hg_model_hub_name = "ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli"
      # hg_model_hub_name = "ynie/albert-xxlarge-v2-snli_mnli_fever_anli_R1_R2_R3-nli"
      # hg_model_hub_name = "ynie/bart-large-snli_mnli_fever_anli_R1_R2_R3-nli"
      # hg_model_hub_name = "ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli"
      # hg_model_hub_name = "ynie/xlnet-large-cased-snli_mnli_fever_anli_R1_R2_R3-nli"
  
      tokenizer = AutoTokenizer.from_pretrained(hg_model_hub_name)
      model = AutoModelForSequenceClassification.from_pretrained(hg_model_hub_name)
  
      tokenized_input_seq_pair = tokenizer.encode_plus(premise, hypothesis,
                                                       max_length=max_length,
                                                       return_token_type_ids=True, truncation=True)
  
      input_ids = torch.Tensor(tokenized_input_seq_pair['input_ids']).long().unsqueeze(0)
      # remember bart doesn't have 'token_type_ids', remove the line below if you are using bart.
      token_type_ids = torch.Tensor(tokenized_input_seq_pair['token_type_ids']).long().unsqueeze(0)
      attention_mask = torch.Tensor(tokenized_input_seq_pair['attention_mask']).long().unsqueeze(0)
  
      outputs = model(input_ids,
                      attention_mask=attention_mask,
                      token_type_ids=token_type_ids,
                      labels=None)
      # Note:
      # "id2label": {
      #     "0": "entailment",
      #     "1": "neutral",
      #     "2": "contradiction"
      # },
  
      predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()  # batch_size only one
  
      print("Premise:", premise)
      print("Hypothesis:", hypothesis)
      print("Entailment:", predicted_probability[0])
      print("Neutral:", predicted_probability[1])
      print("Contradiction:", predicted_probability[2])
  ```
  
  More in [here](https://github.com/facebookresearch/anli/blob/master/src/hg_api/interactive_eval.py).
  
  Citation:
  ```
  @inproceedings{nie-etal-2020-adversarial,
      title = "Adversarial {NLI}: A New Benchmark for Natural Language Understanding",
      author = "Nie, Yixin  and
        Williams, Adina  and
        Dinan, Emily  and
        Bansal, Mohit  and
        Weston, Jason  and
        Kiela, Douwe",
      booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
      year = "2020",
      publisher = "Association for Computational Linguistics",
  }
  ```
  