Feature Extraction

Feature extraction is the task of converting a text into a vector (often called “embedding”).

Example applications:

Retrieving the most relevant documents for a query (for RAG applications).
Reranking a list of documents based on their similarity to a query.
Calculating the similarity between two sentences.

For more details about the feature-extraction task, check out its dedicated page! You will find examples and related materials.

Recommended models

thenlper/gte-large: A powerful feature extraction model for natural language processing tasks.

Explore all available models and find the one that suits you best here.

Using the API

Language

Client

Provider

Settings

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

result = client.feature_extraction(
    inputs="Today is a sunny day and I will get some ice cream.",
    model="intfloat/multilingual-e5-large-instruct",
)

API specification

Request

Headers
authorization	string	Authentication header in the form `'Bearer: hf_**'` when `hf_**` is a personal user access token with “Inference Providers” permission. You can generate one from your settings page.

Payload
inputs*	unknown	One of the following:
(#1)	string
(#2)	string[]
normalize	boolean
prompt_name	string	The name of the prompt that should be used by for encoding. If not set, no prompt will be applied. Must be a key in the `sentence-transformers` configuration `prompts` dictionary. For example if `prompt_name` is “query” and the `prompts` is {“query”: “query: ”, …}, then the sentence “What is the capital of France?” will be encoded as “query: What is the capital of France?” because the prompt text will be prepended before any text to encode.
truncate	boolean
truncation_direction	enum	Possible values: Left, Right.

Response

Body
(array)	array[]	Output is an array of arrays.