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StructTable InternVL2 1B

StructEqTable-Deploy: A High-efficiency Open-source Toolkit for Table-to-Latex Transformation

ArtText modelVersionmodel

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Art
Text model
Dateigröße
1,75 GB
Version
vmodel
Aktualisiert
17. Sept. 2026
Downloads
1578
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Beschriftung
Quelle
Hugging Face

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StructEqTable-Deploy: A High-efficiency Open-source Toolkit for Table-to-Latex Transformation

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1 Quelle
Hugging FacePrimärVersion 7f46f7b8172568a455b423956ecb65b340a944ed
huggingface.co/InternScience/StructTable-InternVL2-1B/tree/7f46f7b8172568a455b423956ecb65b340a944ed

Verifiziert 17. Sept.

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1 Artefakt

model.safetensors

safetensors · 1,75 GB · SHA-256 59901e249fd2…57fb · Hugging Face

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--- language: - en - zh pipeline_tag: image-to-text inference: false license: apache-2.0 --- <div align="center"> <h1>StructEqTable-Deploy: A High-efficiency Open-source Toolkit for Table-to-Latex Transformation</h1> [[ Github Repo ]](https://github.com/UniModal4Reasoning/StructEqTable-Deploy) [[ Related Paper ]](https://arxiv.org/abs/2505.16938) [[ Website ]](https://discovery.intern-ai.org.cn/home) [[ Dataset🤗 ]](https://huggingface.co/datasets/U4R/DocGenome/tree/main) [[ Models🤗 ]](https://huggingface.co/U4R/StructTable-InternVL2-1B/tree/main) [[ Demo💬 ]](https://www.modelscope.cn/studios/HongbinZhou/StructEqTable-Demo/) </div> Welcome to the official repository of StructEqTable-Deploy, a solution that converts images of Table into LaTeX/HTML/MarkDown, powered by scalable data from [DocGenome benchmark](https://unimodal4reasoning.github.io/DocGenome_page/). ## Overview Table is an effective way to represent structured data in scientific publications, financial statements, invoices, web pages, and many other scenarios. Extracting tabular data from a visual table image and performing the downstream reasoning tasks according to the extracted data is challenging, mainly due to that tables often present complicated column and row headers with spanning cell operation. To address these challenges, we present TableX, a large-scale multi-modal table benchmark extracted from [DocGenome benchmark](https://unimodal4reasoning.github.io/DocGenome_page/) for table pre-training, comprising more than 2 million high-quality Image-LaTeX pair data covering 156 disciplinary classes. Besides, benefiting from such large-scale data, we train an end-to-end model, StructEqTable, which provides the capability to precisely obtain the corresponding LaTeX description from a visual table image and perform multiple table-related reasoning tasks, including structural extraction and question answering, broadening its application scope and potential. ## Changelog - [2024/12/12] 🔥 We have released latest model **[StructTable-InternVL2-1B v0.2](https://huggingface.co/U4R/StructTable-InternVL2-1B/tree/main)** with enhanced recognition stability for HTML and Markdown formats! - [2024/10/19] We have released our latest model StructTable-InternVL2-1B! Thanks to IntenrVL2 powerful foundational capabilities, and through fine-tuning on the synthetic tabular data and DocGenome dat...

Source context: 1578 downloads · 49 likes · Pipeline image-to-text · Repo InternScience/StructTable-InternVL2-1B