NLP API Reference

Welcome to the Natural Language Processing (NLP) API documentation for ai_toolkit. This section provides detailed information on the available NLP tools, their functionalities, and usage examples.

Key Features

• Text Analysis: Process and analyze text data for sentiment, entity recognition, and keyword extraction.
• Language Detection: Automatically identify the language of input text.
• Machine Learning Models: Pre-trained models for tasks like text classification and translation.
• Custom Pipelines: Build and deploy your own NLP workflows.

Usage Examples

Here are some common use cases:

1. Sentiment Analysis

   from ai_toolkit.nlp import analyze_sentiment  
   result = analyze_sentiment("I love this product!")  
   print(result)  # Output: {"sentiment": "positive", "confidence": 0.95}
   

2. Entity Recognition

   from ai_toolkit.nlp import extract_entities  
   entities = extract_entities("Apple is planning to launch a new iPhone in 2024.")  
   print(entities)  # Output: {"entities": ["Apple", "iPhone"], "types": ["ORG", "PRODUCT"]}
   

3. Language Detection

   from ai_toolkit.nlp import detect_language  
   lang = detect_language("Bonjour! Comment ça va?")  
   print(lang)  # Output: "fr" (French)
   

Related Resources

Need more guidance? Check out our Quick Start Guide to get started with AI Toolkit.