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.
cardiffnlp/twitter-roberta-base-sentiment-latest: A sentiment analysis model specialized in analyzing tweets.
papluca/xlm-roberta-base-language-detection: A model that can classify languages.
meta-llama/Prompt-Guard-86M: A model that can classify text generation attacks.

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.text_classification(
    inputs="I like you. I love you",
    model="ProsusAI/finbert",
)

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.