Sentence Similarity
Safetensors
sentence-transformers
PyLate
bert
ColBERT
feature-extraction
text-embeddings-inference
Instructions to use lightonai/answerai-colbert-small-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lightonai/answerai-colbert-small-v1 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="lightonai/answerai-colbert-small-v1") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Inference
- Notebooks
- Google Colab
- Kaggle
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# PyLate model based on answerdotai/answerai-colbert-small-v1
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This is a [answerdotai/answerai-colbert-small-v1](https://huggingface.co/answerdotai/answerai-colbert-small-v1) model converted to PyLate format. It maps sentences & paragraphs to sequences of 96-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
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## Model Details
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# PyLate model based on answerdotai/answerai-colbert-small-v1
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This is a [answerdotai/answerai-colbert-small-v1](https://huggingface.co/answerdotai/answerai-colbert-small-v1) model converted to [PyLate](https://github.com/lightonai/pylate) format. It maps sentences & paragraphs to sequences of 96-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
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## Model Details
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