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license: apache-2.0 |
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--- |
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# Cross-Encoder for Quora Duplicate Questions Detection |
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This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. |
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## Training Data |
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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. |
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## Performance |
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For performance results of this model, see [SBERT.net Pre-trained Cross-Encoder][https://www.sbert.net/docs/pretrained_cross-encoders.html]. |
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## Usage |
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Pre-trained models can be used like this: |
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```python |
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from sentence_transformers import CrossEncoder |
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model = CrossEncoder('model_name') |
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scores = model.predict([('Query1', 'Paragraph1'), ('Query2', 'Paragraph2')]) |
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#e.g. |
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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.')]) |
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``` |
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## Usage with Transformers AutoModel |
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You can use the model also directly with Transformers library (without SentenceTransformers library): |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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model = AutoModelForSequenceClassification.from_pretrained('model_name') |
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tokenizer = AutoTokenizer.from_pretrained('model_name') |
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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") |
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model.eval() |
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with torch.no_grad(): |
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scores = torch.nn.functional.sigmoid(model(**features).logits) |
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print(scores) |
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``` |
