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Model: cross-encoder/qnli-distilroberta-base
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- distilbert/distilroberta-base
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+tags:
+- transformers
+---
+# Cross-Encoder for SQuAD (QNLI)
+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('cross-encoder/qnli-distilroberta-base')
+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('cross-encoder/qnli-distilroberta-base')
+tokenizer = AutoTokenizer.from_pretrained('cross-encoder/qnli-distilroberta-base')
+
+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)
+```