Inference Providers documentation
Token Classification
Token Classification
Token classification is a task in which a label is assigned to some tokens in a text. Some popular token classification subtasks are Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging.
For more details about the token-classification task, check out its dedicated page! You will find examples and related materials.
Recommended models
- dslim/bert-base-NER: A robust performance model to identify people, locations, organizations and names of miscellaneous entities.
- FacebookAI/xlm-roberta-large-finetuned-conll03-english: A strong model to identify people, locations, organizations and names in multiple languages.
- blaze999/Medical-NER: A token classification model specialized on medical entity recognition.
Explore all available models and find the one that suits you best here.
Using the API
from huggingface_hub import InferenceClient
client = InferenceClient(
provider="hf-inference",
api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)
result = client.token_classification(
inputs="My name is Sarah Jessica Parker but you can call me Jessica",
model="dslim/bert-base-NER",
)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 input text data |
| parameters | object | |
| ignore_labels | string[] | A list of labels to ignore |
| stride | integer | The number of overlapping tokens between chunks when splitting the input text. |
| aggregation_strategy | string | One of the following: |
| (#1) | ’none’ | Do not aggregate tokens |
| (#2) | ’simple’ | Group consecutive tokens with the same label in a single entity. |
| (#3) | ’first’ | Similar to “simple”, also preserves word integrity (use the label predicted for the first token in a word). |
| (#4) | ’average’ | Similar to “simple”, also preserves word integrity (uses the label with the highest score, averaged across the word’s tokens). |
| (#5) | ’max’ | Similar to “simple”, also preserves word integrity (uses the label with the highest score across the word’s tokens). |
Response
| Body | ||
|---|---|---|
| (array) | object[] | Output is an array of objects. |
| entity_group | string | The predicted label for a group of one or more tokens |
| entity | string | The predicted label for a single token |
| score | number | The associated score / probability |
| word | string | The corresponding text |
| start | integer | The character position in the input where this group begins. |
| end | integer | The character position in the input where this group ends. |