Text Classification Models

Text classification is a fundamental task in natural language processing (NLP), where the goal is to assign a category to a piece of text. Our AI Toolkit offers several models designed to handle various text classification tasks efficiently.

Supported Languages

• English: /en/docs/models/text-classification
• Chinese: /zh/docs/models/text-classification

Models Overview

Here are some of the text classification models available in our AI Toolkit:

• Sentiment Analysis: Classify the sentiment of a text into positive, negative, or neutral.
• Topic Classification: Assign a text to one of the predefined topics.
• Spam Detection: Identify whether a piece of text is spam or not.
• Entity Recognition: Classify entities within a text into predefined categories.

Getting Started

To start using our text classification models, you can follow these steps:

1. Sign up for an account
2. Create a new project
3. Integrate the text classification API

Example

Here's an example of how you can use our sentiment analysis model:

import requests

# Replace 'your_api_key' with your actual API key
headers = {'Authorization': 'Bearer your_api_key'}
data = {'text': 'I love this product!'}
response = requests.post('https://api.ai_toolkit.com/sentiment-analysis', headers=headers, json=data)

# Parse the response
sentiment = response.json()['sentiment']
print(f'The sentiment of the text is: {sentiment}')

Resources

For more information and examples, please refer to our Text Classification Documentation.

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