Sentence Similarity
TensorBoard
Safetensors
sentence-transformers
PyLate
bert
ColBERT
feature-extraction
Generated from Trainer
dataset_size:983844
loss:Contrastive
Eval Results (legacy)
text-embeddings-inference
Instructions to use yosefw/colbert-bert-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yosefw/colbert-bert-mini with sentence-transformers:
from pylate import models queries = [ "Which planet is known as the Red Planet?", "What is the largest planet in our solar system?", ] documents = [ ["Mars is the Red Planet.", "Venus is Earth's twin."], ["Jupiter is the largest planet.", "Saturn has rings."], ] model = models.ColBERT(model_name_or_path="yosefw/colbert-bert-mini") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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# Step 1: Load the ColBERT model
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model = models.ColBERT(
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model_name_or_path=yosefw/colbert-bert-mini,
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# Step 2: Initialize the Voyager index
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model = models.ColBERT(
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model_name_or_path=yosefw/colbert-bert-mini,
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queries_embeddings = model.encode(
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# Step 1: Load the ColBERT model
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model = models.ColBERT(
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model_name_or_path="yosefw/colbert-bert-mini",
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# Step 2: Initialize the Voyager index
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model = models.ColBERT(
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model_name_or_path="yosefw/colbert-bert-mini",
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queries_embeddings = model.encode(
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