reader-lm-0.5b
Maintainer: jinaai
82
🧠
| 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 |
Create account to get full access
Model overview
The reader-lm-0.5b model is a series of models from Jina AI that can convert HTML content to Markdown content. This is useful for content conversion tasks, as the model is trained on a curated collection of HTML and corresponding Markdown content. The model is available in two sizes: reader-lm-0.5b and reader-lm-1.5b, which have 256K context lengths.
The similar models in this series include the reader-lm-1.5b model, which has the same context length as the reader-lm-0.5b model. Both models can be loaded and used in a similar way.
Model inputs and outputs
Inputs
- Raw HTML content
Outputs
- Markdown content corresponding to the input HTML
Capabilities
The reader-lm-0.5b model can convert HTML content to Markdown format, which is useful for tasks such as content migration, formatting, and processing. The model can handle a wide range of HTML structures and produce clean, well-formatted Markdown output.
What can I use it for?
The reader-lm-0.5b model can be used in a variety of content conversion and processing tasks. For example, you could use it to convert blog posts, articles, or other web content from HTML to Markdown format, making it easier to work with the content in a variety of tools and platforms. The model could also be used as part of a content management system or web scraping pipeline to automatically convert HTML content to a more portable format.
Things to try
One interesting thing to try with the reader-lm-0.5b model is to experiment with the input HTML content and see how the model handles different types of structures and formatting. You could try feeding the model a range of HTML content, from simple pages to more complex, nested structures, and observe how the Markdown output varies. This could help you understand the model's capabilities and limitations, and identify any areas for improvement or fine-tuning.
Another thing to try is to use the model as part of a larger content processing pipeline, integrating it with other tools and services to create a more comprehensive content management workflow. For example, you could use the model to convert HTML to Markdown, and then use the Markdown content as input to a text summarization or natural language processing model to extract key insights or generate related content.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🌐
reader-lm-1.5b
251
reader-lm-1.5b is a series of models developed by Jina AI 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: reader-lm-0.5b with a context length of 256K reader-lm-1.5b 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.
Updated Invalid Date
🎲
jina-colbert-v1-en
76
Jina-ColBERT Jina-ColBERT is a variant of the ColBERT retrieval model that is based on the JinaBERT architecture. Like the original ColBERT, Jina-ColBERT uses a late interaction approach to achieve fast and accurate retrieval. The key difference is that Jina-ColBERT supports a longer context length of up to 8,192 tokens, enabled by the JinaBERT backbone which incorporates the symmetric bidirectional variant of ALiBi. Model inputs and outputs Inputs Text passages to be indexed and searched Outputs Ranked lists of the most relevant passages for a given query Capabilities Jina-ColBERT is designed for efficient and effective passage retrieval, outperforming standard BERT-based models. Its ability to handle long documents up to 8,192 tokens makes it well-suited for tasks involving large amounts of text, such as document search and question-answering over long-form content. What can I use it for? Jina-ColBERT can be used to power a wide range of search and retrieval applications, including enterprise search, academic literature search, and question-answering systems. Its performance characteristics make it particularly useful in scenarios where users need to search large document collections quickly and accurately. Things to try One interesting aspect of Jina-ColBERT is its ability to leverage the JinaBERT architecture to support longer input sequences. Practitioners could experiment with using Jina-ColBERT to search through long-form content like books, legal documents, or research papers, and compare its performance to other retrieval models.
Updated Invalid Date
🔮
LaMini-T5-738M
45
The LaMini-T5-738M is one of the models in the LaMini-LM series developed by MBZUAI. It is a fine-tuned version of the t5-large model that has been further trained on the LaMini-instruction dataset, which contains 2.58M samples for instruction fine-tuning. The LaMini-LM series includes several models with different parameter sizes, ranging from 61M to 1.3B, allowing users to choose the one that best fits their needs. The maintainer, MBZUAI, provides a profile page with more information about their work. Model inputs and outputs The LaMini-T5-738M model is a text-to-text generation model, meaning it takes in natural language prompts as input and generates relevant text as output. The model can be used to respond to human instructions written in natural language. Inputs Natural language prompts**: The model accepts natural language prompts as input, such as "Please let me know your thoughts on the given place and why you think it deserves to be visited: 'Barcelona, Spain'". Outputs Generated text**: The model generates relevant text in response to the input prompt. The output can be up to 512 tokens long. Capabilities The LaMini-T5-738M model has been trained on a diverse set of instructions, allowing it to perform a wide range of natural language processing tasks such as question answering, task completion, and text generation. The model has demonstrated strong performance on various benchmarks, outperforming larger models like Llama2-13B, MPT-30B, and Falcon-40B in certain areas. What can I use it for? The LaMini-T5-738M model can be used for a variety of applications that involve responding to human instructions written in natural language. This could include customer service chatbots, virtual assistants, content generation, and task automation. The model's performance and relatively small size make it a suitable choice for deployment on edge devices or in resource-constrained environments. Things to try One interesting aspect of the LaMini-T5-738M model is its ability to handle diverse instructions and generate coherent and relevant responses. Users could experiment with prompts that cover a wide range of topics, from open-ended questions to specific task descriptions, to see the model's flexibility and capabilities. Additionally, users could compare the performance of the LaMini-T5-738M model to other models in the LaMini-LM series to determine the optimal trade-off between model size and performance for their specific use case.
Updated Invalid Date
🔄
LaMini-Flan-T5-248M
61
The LaMini-Flan-T5-248M model is part of the LaMini-LM series developed by MBZUAI. It is a fine-tuned version of the google/flan-t5-base model, further trained on the LaMini-instruction dataset containing 2.58M samples. This series includes several other models like LaMini-Flan-T5-77M, LaMini-Flan-T5-783M, and more, providing a range of model sizes to choose from. The models are designed to perform well on a variety of instruction-based tasks. Model inputs and outputs Inputs Text prompts in natural language that describe a task or instruction for the model to perform Outputs Text responses generated by the model to complete the given task or instruction Capabilities The LaMini-Flan-T5-248M model is capable of understanding and responding to a wide range of natural language instructions, from simple translations to more complex problem-solving tasks. It demonstrates strong performance on benchmarks covering reasoning, question-answering, and other instruction-based challenges. What can I use it for? The LaMini-Flan-T5-248M model can be used for research on language models, including exploring zero-shot and few-shot learning on NLP tasks. It may also be useful for applications that require natural language interaction, such as virtual assistants, content generation, and task automation. However, as with any large language model, care should be taken to assess potential safety and fairness concerns before deploying it in real-world applications. Things to try Experiment with the model's few-shot capabilities by providing it with minimal instructions and observing its responses. You can also try fine-tuning the model on domain-specific datasets to see how it adapts to specialized tasks. Additionally, exploring the model's multilingual capabilities by testing it on prompts in different languages could yield interesting insights.
Updated Invalid Date