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.
Explore all available models and find the one that suits you best here.
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="NousResearch/Minos-v1",
)| 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. |
| Body | ||
|---|---|---|
| (array) | object[] | Output is an array of objects. |
| label | string | The predicted class label. |
| score | number | The corresponding probability. |