Sentence Similarity
Safetensors
sentence-transformers
English
PyLate
bert
ColBERT
feature-extraction
Generated from Trainer
dataset_size:983844
loss:Contrastive
Eval Results (legacy)
text-embeddings-inference
Instructions to use rasyosef/ColBERT-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use rasyosef/ColBERT-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="rasyosef/ColBERT-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="
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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="
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queries_embeddings = model.encode(
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## Training Details
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### Training Dataset
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year={2024}
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```
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<!--
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## Glossary
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## Model Card Contact
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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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="rasyosef/colbert-mini",
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)
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# Step 2: Initialize the Voyager index
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]
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model = models.ColBERT(
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model_name_or_path="rasyosef/colbert-mini",
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queries_embeddings = model.encode(
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## Training Details
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<details><summary>Click to expand</summary>
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### Training Dataset
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year={2024}
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}
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```
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</details>
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<!--
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## Glossary
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## Model Card Contact
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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-->
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