> ## Documentation Index
> Fetch the complete documentation index at: https://docs.trychroma.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Hugging Face

Chroma provides wrappers for both dense and sparse embedding models from Hugging Face.

## Dense Embeddings

Chroma provides a convenient wrapper around HuggingFace's embedding API. This embedding function runs remotely on HuggingFace's servers, and requires an API key. You can get an API key by signing up for an account at [HuggingFace](https://huggingface.co/).

```python theme={null}
import chromadb.utils.embedding_functions as embedding_functions
huggingface_ef = embedding_functions.HuggingFaceEmbeddingFunction(
    api_key="YOUR_API_KEY",
    model_name="sentence-transformers/all-MiniLM-L6-v2"
)
```

You can pass in an optional `model_name` argument, which lets you choose which HuggingFace model to use. By default, Chroma uses `sentence-transformers/all-MiniLM-L6-v2`. You can see a list of all available models [here](https://huggingface.co/models).

## Sparse Embeddings

Chroma also supports sparse embedding models from Hugging Face using `HuggingFaceSparseEmbeddingFunction`.

This embedding function requires the `sentence_transformers` package, which you can install with `pip install sentence_transformers`.

```python theme={null}
from chromadb.utils.embedding_functions import HuggingFaceSparseEmbeddingFunction

ef = HuggingFaceSparseEmbeddingFunction(
    model_name="BAAI/bge-m3",
    device="cpu"
)

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