Build Custom Text Classification Model with Only Few Sample | Spacy and Setfit

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You can train state-of-the-art text classification models with only a few samples straight in SpaCy now. Together with Spacy SetFit which is a new few-shot text classification library you can do that very easily just by following steps in this hands-on tutorial.

The combination of Spacy and Setfit allows you to add your small training set (few text samples) that will be used for fine tune the base Spacy model. For example, as in this tutorial, the new text will be classified to two classes: inlier and outlier.

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The content of the tutorial:
0:00 - Intro
0:27 - Install Spacy and Spacy Setfit
1:00 - Install en_core_web_sm
1:33 - Setup a Python file to implement text classification
6:16 - Test the fine-tuned NLP model on test data
7:41 - Bonus: Github repository and the best data science school

#nlp #python #setfit #spacy #textclassification

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DataScienceGarage

Does not work. Even from first lines:

> import spacy_setfit

okopyl

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