readme: ---
  license: apache-2.0
  ---
  # Cross-Encoder for Quora Duplicate Questions Detection
  This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
  
  ## Training Data
  Given a question and paragraph, can the question be answered by the paragraph? The models have been trained on the [GLUE QNLI](https://arxiv.org/abs/1804.07461) dataset, which transformed the [SQuAD dataset](https://rajpurkar.github.io/SQuAD-explorer/) into an NLI task.
  
  ## Performance
  For performance results of this model, see [SBERT.net Pre-trained Cross-Encoder][https://www.sbert.net/docs/pretrained_cross-encoders.html].
  
  ## Usage
  
  Pre-trained models can be used like this:
  ```python
  from sentence_transformers import CrossEncoder
  model = CrossEncoder('model_name')
  scores = model.predict([('Query1', 'Paragraph1'), ('Query2', 'Paragraph2')])
  
  #e.g.
  scores = model.predict([('How many people live in Berlin?', 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.'), ('What is the size of New York?', 'New York City is famous for the Metropolitan Museum of Art.')])
  ```
  
  ## 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('model_name')
  tokenizer = AutoTokenizer.from_pretrained('model_name')
  
  features = tokenizer(['How many people live in Berlin?', 'What is the size of New York?'], ['Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'],  padding=True, truncation=True, return_tensors="pt")
  
  model.eval()
  with torch.no_grad():
      scores = torch.nn.functional.sigmoid(model(**features).logits)
      print(scores)
  ```