Text Classification

Text Classification is the task of assigning a label or class to a given text. Some use cases are sentiment analysis, natural language inference, and assessing grammatical correctness.

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

Recommended models

distilbert/distilbert-base-uncased-finetuned-sst-2-english: A robust model trained for sentiment analysis.
ProsusAI/finbert: A sentiment analysis model specialized in financial sentiment.

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

Using the API

Language

Client

Provider

Settings

import os
from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key=os.environ["HF_TOKEN"],
)

result = client.text_classification(
    "I like you. I love you",
    model="distilbert/distilbert-base-uncased-finetuned-sst-2-english",
)

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*	string	The text to classify
parameters	object
function_to_apply	enum	Possible values: sigmoid, softmax, none.
top_k	integer	When specified, limits the output to the top K most probable classes.

Response

Body
(array)	object[]	Output is an array of objects.
label	string	The predicted class label.
score	number	The corresponding probability.