Improve model card: Add metadata, update paper & add GitHub links
Browse filesThis Pull Request significantly enhances the model card for POINTS-Reader.
Key improvements include:
- **Metadata addition**: Added `license: apache-2.0`, `library_name: transformers`, and `pipeline_tag: image-text-to-text`. This ensures the model is easily discoverable on the Hugging Face Hub and correctly displays the "how to use" widget.
- **Paper Link Update**: The existing `arXiv` badge link has been fixed to point to the official Hugging Face paper page: https://huggingface.co/papers/2509.01215.
- **GitHub Link Inclusion**: A prominent GitHub badge linking to https://github.com/Tencent/POINTS-Reader has been added to improve code discoverability.
- **Introduction**: Briefly updated the introductory sentence to include a direct link to the paper.
These changes will make the model card more informative and user-friendly.
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<p align="center">
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<img src="images/logo.png" width="700"/>
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<p>
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@@ -8,17 +14,20 @@ POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Docume
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<p align="center">
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<a href="https://huggingface.co/tencent/POINTS-Reader">
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<img src="https://img.shields.io/badge
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</a>
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<a href="">
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<img src="https://img.shields.io/badge/
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</a>
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<a href="">
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<img src="https://komarev.com/ghpvc/?username=tencent&repo=POINTS-Reader&color=brightgreen&label=Views" alt="view">
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</a>
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</p>
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We are delighted to announce that the WePOINTS family has welcomed a new member: POINTS-Reader, a vision-language model for end-to-end document conversion.
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## News
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@@ -578,8 +587,10 @@ import torch
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# We recommend using the following prompt to better performance,
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# since it is used throughout the training process.
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prompt = (
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'Please extract all the text from the image with the following requirements
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'2. Return all other text in Markdown format.'
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)
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image_path = '/path/to/your/local/image'
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return response
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prompt = (
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'Please extract all the text from the image with the following requirements
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'2. Return all other text in Markdown format.'
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)
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journal={arXiv preprint arXiv:2405.11850},
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year={2024}
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}
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```
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---
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license: apache-2.0
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library_name: transformers
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pipeline_tag: image-text-to-text
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---
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<p align="center">
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<img src="images/logo.png" width="700"/>
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<p>
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<p align="center">
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<a href="https://huggingface.co/tencent/POINTS-Reader">
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<img src="https://img.shields.io/badge/%F0%9F%A4%97_HuggingFace-Model-ffbd45.svg" alt="HuggingFace">
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</a>
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<a href="https://github.com/Tencent/POINTS-Reader">
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<img src="https://img.shields.io/badge/GitHub-Code-blue.svg?logo=github&" alt="GitHub Code">
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</a>
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<a href="https://huggingface.co/papers/2509.01215">
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<img src="https://img.shields.io/badge/Paper-POINTS--Reader-d4333f?logo=arxiv&logoColor=white&colorA=cccccc&colorB=d4333f&style=flat" alt="Paper">
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</a>
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<a href="https://komarev.com/ghpvc/?username=tencent&repo=POINTS-Reader&color=brightgreen&label=Views" alt="view">
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<img src="https://komarev.com/ghpvc/?username=tencent&repo=POINTS-Reader&color=brightgreen&label=Views" alt="view">
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</a>
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</p>
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We are delighted to announce that the WePOINTS family has welcomed a new member: POINTS-Reader, a vision-language model for end-to-end document conversion, as introduced in the paper [POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion](https://huggingface.co/papers/2509.01215).
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## News
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# We recommend using the following prompt to better performance,
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# since it is used throughout the training process.
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prompt = (
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'Please extract all the text from the image with the following requirements:
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'
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'1. Return tables in HTML format.
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'
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'2. Return all other text in Markdown format.'
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)
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image_path = '/path/to/your/local/image'
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return response
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prompt = (
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'Please extract all the text from the image with the following requirements:
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'
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'1. Return tables in HTML format.
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'
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'2. Return all other text in Markdown format.'
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)
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journal={arXiv preprint arXiv:2405.11850},
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year={2024}
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}
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```
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