Sentence Transformer ↗

chroma guide intermediate embeddings models
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Chroma provides a convenient wrapper around the Sentence Transformers library. This embedding function runs locally and uses pre-trained models from Hugging Face.

This embedding function relies on the sentence_transformers python package, which you can install with pip install sentence_transformers.

    from chromadb.utils.embedding_functions import SentenceTransformerEmbeddingFunction

    sentence_transformer_ef = SentenceTransformerEmbeddingFunction(
        model_name="all-MiniLM-L6-v2",
        device="cpu",
        normalize_embeddings=False
    )

    texts = ["Hello, world!", "How are you?"]
    embeddings = sentence_transformer_ef(texts)
    ```

You can pass in optional arguments:

* `model_name`: The name of the Sentence Transformer model to use (default: "all-MiniLM-L6-v2")
* `device`: Device used for computation, "cpu" or "cuda" (default: "cpu")
* `normalize_embeddings`: Whether to normalize returned vectors (default: False)

For a full list of available models, visit [Sentence Transformers models on Hugging Face](https://huggingface.co/models?library=sentence-transformers) or [SBERT documentation](https://www.sbert.net/docs/pretrained_models.html).
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```typescript
    // npm install @chroma-core/sentence-transformer

    import { SentenceTransformersEmbeddingFunction } from "@chroma-core/sentence-transformer";

    const sentenceTransformerEF = new SentenceTransformersEmbeddingFunction({
        modelName: "all-MiniLM-L6-v2",
        device: "cpu",
        normalizeEmbeddings: false,
    });

    const texts = ["Hello, world!", "How are you?"];
    const embeddings = await sentenceTransformerEF.generate(texts);
    ```
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<span class="callout-start" data-callout-type="note"></span>
  Sentence Transformers are great for semantic search tasks. Popular models include `all-MiniLM-L6-v2` (fast and efficient) and `all-mpnet-base-v2` (higher quality). Visit [SBERT documentation](https://www.sbert.net/docs/pretrained_models.html) for more model recommendations.
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Link last verified June 7, 2026. View original ↗
Source: Chroma Docs
Link last verified: 2026-03-04