

Reader LM
Overview :
Reader-LM is a compact language model developed by Jina AI, designed to transform raw, messy HTML content from the web into clean Markdown format. These models are specifically optimized for long-text handling, support multiple languages, and can process context lengths of up to 256K tokens. By providing a direct conversion from HTML to Markdown, Reader-LM reduces reliance on regular expressions and heuristic rules, thereby enhancing conversion accuracy and efficiency.
Target Users :
Reader-LM is designed for developers and content creators who need to convert web content into Markdown format, particularly those dealing with large volumes of web data and seeking to automate the conversion process. Its multilingual support and long-text handling capabilities make it an ideal choice for international teams and those managing complex web structures.
Use Cases
Convert a technical blog article from HTML format to Markdown for easy publication on GitHub.
Automate the conversion of news website content into Markdown for summary and analysis purposes.
Transform e-commerce product pages into Markdown for generating product documentation.
Features
Direct conversion from HTML to Markdown without extra cleaning steps.
Supports multiple languages, capable of handling web content in different languages.
Strong long-text handling capabilities, supporting context lengths of up to 256K tokens.
Optimized model sizes with Reader-LM-0.5B and Reader-LM-1.5B having 494M and 1.54B parameters respectively.
Outperforms larger language models while maintaining a smaller model size.
Easily accessible on Google Colab with no complex setup required.
Will soon be available on Azure Marketplace and AWS SageMaker.
How to Use
Visit Google Colab and open the demo notebook for Reader-LM.
In the notebook, replace the preset URL with the web URL you wish to convert.
Run the code in the notebook; the model will automatically process the HTML content and generate Markdown.
Review the generated Markdown content to ensure all important information has been correctly converted.
Adjust the model parameters or conversion settings as needed to optimize the output.
Use the converted Markdown content in your projects or documents.
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