reader-lm-1.5b
Maintainer: jinaai
237
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
reader-lm-1.5b is a series of models developed by [object Object] that convert HTML content to Markdown content. The models are trained on a curated collection of HTML content and its corresponding Markdown content, allowing them to effectively perform content conversion tasks.
There are two main models in the reader-lm series:
- [object Object] with a context length of 256K
- [object Object] with a context length of 256K
These models can be used to convert HTML content to Markdown format, which is useful for tasks like content migration, blog post formatting, and more.
Model inputs and outputs
Inputs
- HTML content: The model takes raw HTML content as input, with no prefix instruction required.
Outputs
- Markdown content: The model outputs the corresponding Markdown version of the input HTML content.
Capabilities
The reader-lm models are capable of effectively converting HTML content to Markdown format, leveraging their training on a curated dataset of HTML-Markdown pairs. This allows them to accurately preserve the structure and formatting of the original HTML content when generating the Markdown output.
What can I use it for?
The reader-lm models can be a valuable tool for a variety of content-related tasks, such as:
- Content migration: Easily convert HTML content to Markdown format when moving content between platforms or websites.
- Blog post formatting: Automatically convert HTML blog posts to Markdown, which is a common format for many blogging and publishing platforms.
- Document conversion: Convert HTML documentation or reports to Markdown for better readability and portability.
Things to try
One interesting thing to try with the reader-lm models is to explore their performance on different types of HTML content, such as complex web pages, long-form articles, or even code-heavy documentation. You can also experiment with the models' ability to preserve formatting, links, and other HTML elements when generating the Markdown output.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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