Sentence Similarity
sentence-transformers
Safetensors
English
bert
ColBERT
multi-vector
feature-extraction
Generated from Trainer
dataset_size:497901
loss:Contrastive
text-embeddings-inference
Instructions to use NeuML/colbert-bert-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NeuML/colbert-bert-tiny with sentence-transformers:
from sentence_transformers import MultiVectorEncoder model = MultiVectorEncoder("NeuML/colbert-bert-tiny") queries = ["Which planet is known as the Red Planet?"] documents = [ "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.", ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 912 Bytes
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