Add Sentence Transformers usage
#1
by tomaarsen HF Staff - opened
README.md
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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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@@ -485,14 +486,14 @@ colgrep set-model lightonai/LateOn-Code # default: lightonai/LateOn-Code-edge
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For more information about ColGrep, please refer to the [official documentation](https://github.com/lightonai/next-plaid/tree/main/colgrep)
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#
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This is a
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## Model Details
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### Model Description
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- **Model Type:**
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<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
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- **Document Length:** 2048 tokens
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- **Query Length:** 256 tokens
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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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For more information about ColGrep, please refer to the [official documentation](https://github.com/lightonai/next-plaid/tree/main/colgrep)
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# Model
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This is a multi-vector (ColBERT-style late interaction) embedding model finetuned from [lightonai/LateOn-Code-pretrain](https://huggingface.co/lightonai/LateOn-Code-pretrain) on the [apps](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [synthetictext2sql](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [cosqa](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [codefeedbackst](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [codefeedbackmt](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [stackoverflowqa](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [codetranscontest](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [codetransdl](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_go](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_java](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_javascript](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_php](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_python](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_ruby](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_ccr_go](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_ccr_java](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_ccr_javascript](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_ccr_php](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code), [CodeSearchNet_ccr_python](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) and [CodeSearchNet_ccr_ruby](https://huggingface.co/datasets/lightonai/nv-embed-supervised-distill-dedup-code) datasets. It maps sentences & paragraphs to sequences of 128-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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### Model Description
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- **Model Type:** Multi-vector embedding model
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<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
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- **Document Length:** 2048 tokens
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- **Query Length:** 256 tokens
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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-Code")
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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([[4.7803, 8.2878, 6.5700, 7.0952]])
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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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