Sentence Similarity
ONNX
Safetensors
sentence-transformers
English
PyLate
modernbert
ColBERT
multi-vector
feature-extraction
text-embeddings-inference
🇪🇺 Region: EU
Instructions to use lightonai/LateOn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lightonai/LateOn 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/LateOn") queries_emb = model.encode(queries, is_query=True) docs_emb = model.encode(documents, is_query=False) - Inference
- Notebooks
- Google Colab
- Kaggle
Add Sentence Transformers usage
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by tomaarsen HF Staff - opened
README.md
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---
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tags:
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- ColBERT
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- PyLate
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- sentence-transformers
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- sentence-similarity
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```
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## Usage
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First install the PyLate library:
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```bash
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---
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tags:
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- ColBERT
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- multi-vector
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- PyLate
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- sentence-transformers
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- sentence-similarity
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```
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## Usage
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### Sentence Transformers
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This model can be used with [Sentence Transformers](https://www.sbert.net/) as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`:
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```bash
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pip install "sentence-transformers>=6.0.0"
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```
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```python
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from sentence_transformers import MultiVectorEncoder
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model = MultiVectorEncoder("lightonai/LateOn")
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query = "Which planet is known as the Red Planet?"
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documents = [
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"Venus is often called Earth's twin because of its similar size and proximity.",
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"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
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"Jupiter, the largest planet in our solar system, has a prominent red spot.",
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"Saturn, famous for its rings, is sometimes mistaken for the Red Planet.",
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]
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query_embeddings = model.encode_query(query)
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document_embeddings = model.encode_document(documents)
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print(query_embeddings.shape, document_embeddings[0].shape)
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# (12, 128) (18, 128)
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# MaxSim late-interaction scoring (higher is more relevant)
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scores = model.similarity(query_embeddings, document_embeddings)
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print(scores)
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# tensor([[10.7942, 11.1104, 10.9743, 11.0811]])
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```
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### PyLate
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First install the PyLate library:
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```bash
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