Text Ranking
sentence-transformers
Safetensors
bert
cross-encoder
Generated from Trainer
dataset_size:100
loss:BinaryCrossEntropyLoss
text-embeddings-inference
Instructions to use clturner23/cross_encoder_trained_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use clturner23/cross_encoder_trained_model with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("clturner23/cross_encoder_trained_model") 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) - Notebooks
- Google Colab
- Kaggle
File size: 133 Bytes
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