This project is a Python-based webscraper utilizing the Ollama Language Model (LLM) to enhance web scraping capabilities with natural language processing. The scraper efficiently extracts data from websites and uses Ollama’s advanced language model to parse, clean, and analyze the data, making it suitable for various applications like market research, content aggregation, or automated reporting.
- Enhanced Parsing: Uses Ollama LLM for intelligent parsing, improving data extraction accuracy from diverse website structures.
- Data Cleaning and Structuring: Leverages NLP for organizing and refining scraped content, producing structured datasets ready for analysis.
- Customizable Targets: Easily configure URLs and target elements for scraping based on project needs.
- Error Handling: Incorporates robust error handling to manage site changes, connectivity issues, and data inconsistencies.
- Python 3.8 or above
- Other dependencies listed in
requirements.txt
To extract data from a website, you can configure the scraper to target specific elements (e.g., articles, reviews) and run the script. The model’s NLP capabilities will automatically clean the extracted text.
Contributions are welcome! Please open an issue or submit a pull request to improve the project.
This project is licensed under the MIT License.
This README provides an overview, setup instructions, and usage details, ensuring that new users can get started quickly with your webscraper project.