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Welcome to the Hugging Face tasks series!

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In this video we’ll take a look at the token
classification task.

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Token classification is the task of assigning
a label to each token in a sentence.

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There are various token classification tasks
and the most common are Named Entity Recognition

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and Part-of-Speech Tagging.

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Let’s take a quick look at the Named Entity
Recognition task.

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The goal of this task is to find the entities
in a piece of text, such as person, location,

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or organization.

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This task is formulated as labelling each
token with one class for each entity, and

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another class for tokens that have no entity.

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Another token classification task is part-of-speech
tagging.

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The goal of this task is to label the words
for a particular part of a speech, such as

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noun, pronoun, adjective, verb and so on.

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This task is formulated as labelling each
token with parts of speech.

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Token classification models are evaluated
on Accuracy, Recall, Precision and F1-Score.

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The metrics are calculated for each of the
classes.

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We calculate true positive, true negative
and false positives to calculate precision

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and recall, and take their harmonic mean to
get F1-Score.

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Then we calculate it for every class and take
the overall average to evaluate our model.

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An example dataset used for this task is ConLL2003.

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Here, each token belongs to a certain named
entity class, denoted as the indices of the

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list containing the labels.

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You can extract important information from
invoices using named entity recognition models,

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such as date, organization name or address.

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For more information about the Token classification
task, check out the Hugging Face course.
