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
Python
JavaScript
cURL
import requests
API_URL = "https://api-inference.huggingface.co/models/distilbert/distilbert-base-uncased-finetuned-sst-2-english"
headers = {"Authorization": "Bearer hf_***"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
output = query({
"inputs": "I like you. I love you",
})To use the Python client, see huggingface_hub’s package reference.
API specification
Request
| 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. |
Some options can be configured by passing headers to the Inference API. Here are the available headers:
| Headers | ||
|---|---|---|
| authorization | string | Authentication header in the form 'Bearer: hf_****' when hf_**** is a personal user access token with Inference API permission. You can generate one from your settings page. |
| x-use-cache | boolean, default to true | There is a cache layer on the inference API to speed up requests we have already seen. Most models can use those results as they are deterministic (meaning the outputs will be the same anyway). However, if you use a nondeterministic model, you can set this parameter to prevent the caching mechanism from being used, resulting in a real new query. Read more about caching here. |
| x-wait-for-model | boolean, default to false | If the model is not ready, wait for it instead of receiving 503. It limits the number of requests required to get your inference done. It is advised to only set this flag to true after receiving a 503 error, as it will limit hanging in your application to known places. Read more about model availability here. |
For more information about Inference API headers, check out the parameters guide.
Response
| Body | ||
|---|---|---|
| (array) | object[] | Output is an array of objects. |
| label | string | The predicted class label. |
| score | number | The corresponding probability. |