Welcome to the Hugging Face tutorials section on text classification! This page provides an introduction to the concept of text classification and how it is used in various applications.

What is Text Classification?

Text classification is a method of categorizing text data into predefined classes or categories. It is widely used in natural language processing (NLP) for tasks such as sentiment analysis, topic classification, and spam detection.

Applications of Text Classification

  • Sentiment Analysis: Determine the sentiment of a piece of text, such as a review or social media post.
  • Topic Classification: Automatically categorize text into specific topics or genres.
  • Spam Detection: Identify and filter out unwanted or malicious messages.
  • Named Entity Recognition (NER): Identify and categorize entities such as people, places, and organizations in a text.

Getting Started

If you're new to text classification, we recommend starting with the following tutorials:

Example

Here's an example of a text classification model in action:

"I love this product! It's amazing."

Using a sentiment analysis model, this text would likely be classified as positive.

Resources

For more information on text classification, check out the following resources:

Note: Ensure you have the necessary Python packages installed before proceeding with the tutorials.

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Text Classification
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Sentiment Analysis
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Spam Detection
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