readme: ---
  language: ja
  license: cc-by-4.0
  library_name: sentence-transformers
  tags:
  - xlm-roberta
  - nli
  datasets:
  - jnli
  - jsick
  ---
  
  # Japanese Natural Language Inference Model
  This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class, [gradient accumulation PR](https://github.com/UKPLab/sentence-transformers/pull/1092), and the code from [CyberAgentAILab/japanese-nli-model](https://github.com/CyberAgentAILab/japanese-nli-model).
  
  ## Training Data
  The model was trained on the [JGLUE-JNLI](https://github.com/yahoojapan/JGLUE) and [JSICK](https://github.com/verypluming/JSICK) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
  
  ## Usage
  ```python
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  tokenizer = AutoTokenizer.from_pretrained('cyberagent/xlm-roberta-large-jnli-jsick')
  model = AutoModelForSequenceClassification.from_pretrained('cyberagent/xlm-roberta-large-jnli-jsick')
  features = tokenizer(["子供が走っている猫を見ている", "猫が走っている"], ["猫が走っている", "子供が走っている"], padding=True, truncation=True, return_tensors="pt")
  model.eval()
  with torch.no_grad():
      scores = model(**features).logits
      label_mapping = ['contradiction', 'entailment', 'neutral']
      labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
      print(labels)
  ```
  