Text Ranking
sentence-transformers
PyTorch
JAX
ONNX
Safetensors
OpenVINO
Transformers
English
roberta
text-classification
text-embeddings-inference
Instructions to use cross-encoder/quora-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cross-encoder/quora-roberta-base with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("cross-encoder/quora-roberta-base") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Transformers
How to use cross-encoder/quora-roberta-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cross-encoder/quora-roberta-base") model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/quora-roberta-base") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Usage and Performance
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Pre-trained models can be used like this:
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```
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from sentence_transformers import CrossEncoder
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scores = model.predict([('Question 1', 'Question 2'), ('Question 3', 'Question 4')])
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```
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## Usage and Performance
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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('cross-encoder/quora-roberta-base')
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scores = model.predict([('Question 1', 'Question 2'), ('Question 3', 'Question 4')])
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```
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