readme: ---
  language: en
  pipeline_tag: zero-shot-classification
  license: apache-2.0
  tags:
  - MiniLMv2
  datasets:
  - multi_nli
  - snli
  metrics:
  - accuracy
  ---
  
  # Cross-Encoder for Natural Language Inference
  This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
  
  ## Training Data
  The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
  
  ## Performance
  For evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli).
  
  ## Usage
  
  Pre-trained models can be used like this:
  ```python
  from sentence_transformers import CrossEncoder
  model = CrossEncoder('cross-encoder/nli-MiniLM2-L6-H768')
  scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])
  
  #Convert scores to labels
  label_mapping = ['contradiction', 'entailment', 'neutral']
  labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
  ```
  
  ## Usage with Transformers AutoModel
  You can use the model also directly with Transformers library (without SentenceTransformers library):
  ```python
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  import torch
  
  model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-MiniLM2-L6-H768')
  tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-MiniLM2-L6-H768')
  
  features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'],  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)
  ```
  
  ## Zero-Shot Classification
  This model can also be used for zero-shot-classification:
  ```python
  from transformers import pipeline
  
  classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-MiniLM2-L6-H768')
  
  sent = "Apple just announced the newest iPhone X"
  candidate_labels = ["technology", "sports", "politics"]
  res = classifier(sent, candidate_labels)
  print(res)
  ``` 