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

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Using the API

No snippet available for this task.

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.