Cross-Encoder for Quora Duplicate Questions Detection
For performance results of this model, see [SBERT.net Pre-trained Cross-Encoder][https://www.sbert.net/docs/pretrained_cross-encoders.html].
Pre-trained models can be used like this:
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):
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)
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