> For the complete documentation index, see [llms.txt](https://docs.texti.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.texti.ai/finetuning-task/text-classification.md).

# Text Classification

Text classification involves assigning predefined categories or labels to text documents.

 Here are some real-life business use cases for text classification:

1. ***Sentiment Analysis***: Classify customer feedback to identify areas for improvement in product or service.
2. ***Fraud Detection***: Classify the transaction as fraudulent or not based on other features of the text, without explicitly identifying specific entities.
3. ***Topic Categorization***: Automatically sort customer support tickets based on topic to improve response time and efficiency.
4. ***News Article Categorization***: Categorize news articles to monitor trends and stay up-to-date with industry developments.
5. ***Email Filtering***: Identify spam emails to improve email filtering and security.
6. ***Resume Screening***: Classify job resumes to streamline the recruitment process.

check how to prepare data for Text Classification task.


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# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.texti.ai/finetuning-task/text-classification.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.