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nougat-latex-base

Maintainer: Norm

Total Score

56

Last updated 5/15/2024

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The nougat-latex-base model is a Donut-based model fine-tuned from the facebook/nougat-base model to boost its proficiency in generating LaTeX code from images. The model was developed by the maintainer Norm and aims to improve upon the initial nougat-base model, which struggled with generating high-quality LaTeX code from image inputs due to the unsuitable input resolution.

The nougat-latex-base model addresses this issue by adjusting the input resolution and using an adaptive padding approach to ensure that equation image segments are resized to closely match the resolution of the training data. This helps to mitigate potential rescaling artifacts and improve the generation quality of LaTeX code.

Similar models like the nougat model and the Nous-Hermes-Llama2-70b model also focus on tasks related to academic document understanding and generation, though with different approaches and specialized capabilities.

Model inputs and outputs

Inputs

  • Image inputs containing mathematical equations or scientific formulas

Outputs

  • LaTeX code generated to represent the mathematical content in the input image

Capabilities

The nougat-latex-base model excels at generating high-quality LaTeX code from image inputs, particularly for equation and formula-heavy content. It has been evaluated on an image-equation pair dataset collected from Wikipedia, arXiv, and the im2latex-100k dataset, outperforming the pix2tex model on both token accuracy and normalized edit distance metrics.

What can I use it for?

The nougat-latex-base model can be a valuable tool for researchers, academics, and anyone working with scientific or mathematical content. It can be used to automate the process of converting handwritten or typeset equations into LaTeX format, which is widely used in academic and technical publications.

This model could be integrated into various applications, such as academic paper writing tools, educational platforms, or research analysis software, to streamline the process of incorporating mathematical expressions into digital documents.

Things to try

One interesting aspect of the nougat-latex-base model is its ability to handle a wide range of equation and formula types, from simple expressions to more complex mathematical notation. Users can experiment with different input images, ranging from scanned handwritten notes to typeset equations, and observe how the model performs in generating the corresponding LaTeX code.

Additionally, users can explore the model's limitations, such as its handling of edge cases or its ability to generate LaTeX code for more advanced mathematical concepts. By testing the model's capabilities and understanding its strengths and weaknesses, users can find creative ways to incorporate it into their workflows and leverage its potential to enhance their work with mathematical and scientific content.



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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