Inference Providers documentation
Text Classification
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-classificationtask, 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.
Explore all available models and find the one that suits you best here.
Using the API
Language
Client
Provider
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="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. |