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Model: Alibaba-DT/Logics-Parsing Source: Original Platform
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<div align="center">
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<img src="imgs/logo.jpg" width="80%" >
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</div>
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<p align="center">
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🤗 <a href="https://github.com/alibaba/Logics-Parsing">GitHub</a>   |   🤖 <a href="https://www.modelscope.cn/studios/Alibaba-DT/Logics-Parsing/summary">Demo</a>   |   📑 <a href="https://arxiv.org/abs/2509.19760">Technical Report</a>
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</p>
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## Introduction
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<div align="center">
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<img src="imgs/overview.png" alt="LogicsDocBench 概览" style="width: 800px; height: 250px;">
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</div>
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<div align="center">
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<table style="width: 800px;">
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<tr>
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<td align="center">
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<img src="imgs/report.gif" alt="研报示例">
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</td>
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<td align="center">
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<img src="imgs/chemistry.gif" alt="化学分子式示例">
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</td>
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<td align="center">
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<img src="imgs/paper.gif" alt="论文示例">
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</td>
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<td align="center">
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<img src="imgs/handwritten.gif" alt="手写示例">
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</td>
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</tr>
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<tr>
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<td align="center"><b>report</b></td>
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<td align="center"><b>chemistry</b></td>
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<td align="center"><b>paper</b></td>
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<td align="center"><b>handwritten</b></td>
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</tr>
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</table>
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</div>
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Logics-Parsing is a powerful, end-to-end document parsing model built upon a general Vision-Language Model (VLM) through Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). It excels at accurately analyzing and structuring highly complex documents.
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## Key Features
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* **Effortless End-to-End Processing**
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* Our single-model architecture eliminates the need for complex, multi-stage pipelines. Deployment and inference are straightforward, going directly from a document image to structured output.
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* It demonstrates exceptional performance on documents with challenging layouts.
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* **Advanced Content Recognition**
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* It accurately recognizes and structures difficult content, including intricate scientific formulas.
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* Chemical structures are intelligently identified and can be represented in the standard **SMILES** format.
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* **Rich, Structured HTML Output**
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* The model generates a clean HTML representation of the document, preserving its logical structure.
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* Each content block (e.g., paragraph, table, figure, formula) is tagged with its **category**, **bounding box coordinates**, and **OCR text**.
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* It automatically identifies and filters out irrelevant elements like headers and footers, focusing only on the core content.
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* **State-of-the-Art Performance**
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* Logics-Parsing achieves the best performance on our in-house benchmark, which is specifically designed to comprehensively evaluate a model’s parsing capability on complex-layout documents and STEM content.
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## Benchmark
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Existing document-parsing benchmarks often provide limited coverage of complex layouts and STEM content. To address this, we constructed an in-house benchmark comprising 1,078 page-level images across nine major categories and over twenty sub-categories. Our model achieves the best performance on this benchmark.
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<div align="center">
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<img src="imgs/BenchCls.png">
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</div>
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<table>
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<tr>
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<td rowspan="2">Model Type</td>
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<td rowspan="2">Methods</td>
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<td colspan="2">Overall <sup>Edit</sup> ↓</td>
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<td colspan="2">Text Edit <sup>Edit</sup> ↓</td>
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<td colspan="2">Formula <sup>Edit</sup> ↓</td>
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<td colspan="2">Table <sup>TEDS</sup> ↑</td>
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<td colspan="2">Table <sup>Edit</sup> ↓</td>
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<td colspan="2">ReadOrder<sup>Edit</sup> ↓</td>
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<td rowspan="1">Chemistry<sup>Edit</sup> ↓</td>
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<td rowspan="1">HandWriting<sup>Edit</sup> ↓</td>
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</tr>
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<tr>
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<td>EN</td>
|
||||
<td>ZH</td>
|
||||
<td>EN</td>
|
||||
<td>ZH</td>
|
||||
<td>EN</td>
|
||||
<td>ZH</td>
|
||||
<td>EN</td>
|
||||
<td>ZH</td>
|
||||
<td>EN</td>
|
||||
<td>ZH</td>
|
||||
<td>EN</td>
|
||||
<td>ZH</td>
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||||
<td>ALL</td>
|
||||
<td>ALL</td>
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||||
</tr>
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||||
<tr>
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||||
<td rowspan="7">Pipeline Tools</td>
|
||||
<td>doc2x</td>
|
||||
<td>0.209</td>
|
||||
<td>0.188</td>
|
||||
<td>0.128</td>
|
||||
<td>0.194</td>
|
||||
<td>0.377</td>
|
||||
<td>0.321</td>
|
||||
<td>81.1</td>
|
||||
<td>85.3</td>
|
||||
<td><ins>0.148</ins></td>
|
||||
<td><ins>0.115</ins></td>
|
||||
<td>0.146</td>
|
||||
<td>0.122</td>
|
||||
<td>1.0</td>
|
||||
<td>0.307</td>
|
||||
</tr>
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||||
<tr>
|
||||
<td>Textin</td>
|
||||
<td>0.153</td>
|
||||
<td>0.158</td>
|
||||
<td>0.132</td>
|
||||
<td>0.190</td>
|
||||
<td>0.185</td>
|
||||
<td>0.223</td>
|
||||
<td>76.7</td>
|
||||
<td><ins>86.3</ins></td>
|
||||
<td>0.176</td>
|
||||
<td><b>0.113</b></td>
|
||||
<td><b>0.118</b></td>
|
||||
<td><b>0.104</b></td>
|
||||
<td>1.0</td>
|
||||
<td>0.344</td>
|
||||
</tr>
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||||
<tr>
|
||||
<td>mathpix<sup>*</sup></td>
|
||||
<td><ins>0.128</ins></td>
|
||||
<td><ins>0.146</ins></td>
|
||||
<td>0.128</td>
|
||||
<td><ins>0.152</ins></td>
|
||||
<td><b>0.06</b></td>
|
||||
<td><b>0.142</b></td>
|
||||
<td><b>86.2</b></td>
|
||||
<td><b>86.6</b></td>
|
||||
<td><b>0.120</b></td>
|
||||
<td>0.127</td>
|
||||
<td>0.204</td>
|
||||
<td>0.164</td>
|
||||
<td>0.552</td>
|
||||
<td>0.263</td>
|
||||
</tr>
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||||
<tr>
|
||||
<td>PP_StructureV3</td>
|
||||
<td>0.220</td>
|
||||
<td>0.226</td>
|
||||
<td>0.172</td>
|
||||
<td>0.29</td>
|
||||
<td>0.272</td>
|
||||
<td>0.276</td>
|
||||
<td>66</td>
|
||||
<td>71.5</td>
|
||||
<td>0.237</td>
|
||||
<td>0.193</td>
|
||||
<td>0.201</td>
|
||||
<td>0.143</td>
|
||||
<td>1.0</td>
|
||||
<td>0.382</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Mineru2</td>
|
||||
<td>0.212</td>
|
||||
<td>0.245</td>
|
||||
<td>0.134</td>
|
||||
<td>0.195</td>
|
||||
<td>0.280</td>
|
||||
<td>0.407</td>
|
||||
<td>67.5</td>
|
||||
<td>71.8</td>
|
||||
<td>0.228</td>
|
||||
<td>0.203</td>
|
||||
<td>0.205</td>
|
||||
<td>0.177</td>
|
||||
<td>1.0</td>
|
||||
<td>0.387</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Marker</td>
|
||||
<td>0.324</td>
|
||||
<td>0.409</td>
|
||||
<td>0.188</td>
|
||||
<td>0.289</td>
|
||||
<td>0.285</td>
|
||||
<td>0.383</td>
|
||||
<td>65.5</td>
|
||||
<td>50.4</td>
|
||||
<td>0.593</td>
|
||||
<td>0.702</td>
|
||||
<td>0.23</td>
|
||||
<td>0.262</td>
|
||||
<td>1.0</td>
|
||||
<td>0.50</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Pix2text</td>
|
||||
<td>0.447</td>
|
||||
<td>0.547</td>
|
||||
<td>0.485</td>
|
||||
<td>0.577</td>
|
||||
<td>0.312</td>
|
||||
<td>0.465</td>
|
||||
<td>64.7</td>
|
||||
<td>63.0</td>
|
||||
<td>0.566</td>
|
||||
<td>0.613</td>
|
||||
<td>0.424</td>
|
||||
<td>0.534</td>
|
||||
<td>1.0</td>
|
||||
<td>0.95</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td rowspan="8">Expert VLMs</td>
|
||||
<td>Dolphin</td>
|
||||
<td>0.208</td>
|
||||
<td>0.256</td>
|
||||
<td>0.149</td>
|
||||
<td>0.189</td>
|
||||
<td>0.334</td>
|
||||
<td>0.346</td>
|
||||
<td>72.9</td>
|
||||
<td>60.1</td>
|
||||
<td>0.192</td>
|
||||
<td>0.35</td>
|
||||
<td>0.160</td>
|
||||
<td>0.139</td>
|
||||
<td>0.984</td>
|
||||
<td>0.433</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>dots.ocr</td>
|
||||
<td>0.186</td>
|
||||
<td>0.198</td>
|
||||
<td><ins>0.115</ins></td>
|
||||
<td>0.169</td>
|
||||
<td>0.291</td>
|
||||
<td>0.358</td>
|
||||
<td>79.5</td>
|
||||
<td>82.5</td>
|
||||
<td>0.172</td>
|
||||
<td>0.141</td>
|
||||
<td>0.165</td>
|
||||
<td>0.123</td>
|
||||
<td>1.0</td>
|
||||
<td><ins>0.255</ins></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>MonkeyOcr</td>
|
||||
<td>0.193</td>
|
||||
<td>0.259</td>
|
||||
<td>0.127</td>
|
||||
<td>0.236</td>
|
||||
<td>0.262</td>
|
||||
<td>0.325</td>
|
||||
<td>78.4</td>
|
||||
<td>74.7</td>
|
||||
<td>0.186</td>
|
||||
<td>0.294</td>
|
||||
<td>0.197</td>
|
||||
<td>0.180</td>
|
||||
<td>1.0</td>
|
||||
<td>0.623</td>
|
||||
</tr>
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||||
<tr>
|
||||
<td>OCRFlux</td>
|
||||
<td>0.252</td>
|
||||
<td>0.254</td>
|
||||
<td>0.134</td>
|
||||
<td>0.195</td>
|
||||
<td>0.326</td>
|
||||
<td>0.405</td>
|
||||
<td>58.3</td>
|
||||
<td>70.2</td>
|
||||
<td>0.358</td>
|
||||
<td>0.260</td>
|
||||
<td>0.191</td>
|
||||
<td>0.156</td>
|
||||
<td>1.0</td>
|
||||
<td>0.284</td>
|
||||
</tr>
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<tr>
|
||||
<td>Gotocr</td>
|
||||
<td>0.247</td>
|
||||
<td>0.249</td>
|
||||
<td>0.181</td>
|
||||
<td>0.213</td>
|
||||
<td>0.231</td>
|
||||
<td>0.318</td>
|
||||
<td>59.5</td>
|
||||
<td>74.7</td>
|
||||
<td>0.38</td>
|
||||
<td>0.299</td>
|
||||
<td>0.195</td>
|
||||
<td>0.164</td>
|
||||
<td>0.969</td>
|
||||
<td>0.446</td>
|
||||
</tr>
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||||
<tr>
|
||||
<td>Olmocr</td>
|
||||
<td>0.341</td>
|
||||
<td>0.382</td>
|
||||
<td>0.125</td>
|
||||
<td>0.205</td>
|
||||
<td>0.719</td>
|
||||
<td>0.766</td>
|
||||
<td>57.1</td>
|
||||
<td>56.6</td>
|
||||
<td>0.327</td>
|
||||
<td>0.389</td>
|
||||
<td>0.191</td>
|
||||
<td>0.169</td>
|
||||
<td>1.0</td>
|
||||
<td>0.294</td>
|
||||
</tr>
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<tr>
|
||||
<td>SmolDocling</td>
|
||||
<td>0.657</td>
|
||||
<td>0.895</td>
|
||||
<td>0.486</td>
|
||||
<td>0.932</td>
|
||||
<td>0.859</td>
|
||||
<td>0.972</td>
|
||||
<td>18.5</td>
|
||||
<td>1.5</td>
|
||||
<td>0.86</td>
|
||||
<td>0.98</td>
|
||||
<td>0.413</td>
|
||||
<td>0.695</td>
|
||||
<td>1.0</td>
|
||||
<td>0.927</td>
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</tr>
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<tr>
|
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<td><b>Logics-Parsing</b></td>
|
||||
<td><b>0.124</b></td>
|
||||
<td><b>0.145</b></td>
|
||||
<td><b>0.089</b></td>
|
||||
<td><b>0.139</b></td>
|
||||
<td><ins>0.106</ins></td>
|
||||
<td><ins>0.165</ins></td>
|
||||
<td>76.6</td>
|
||||
<td>79.5</td>
|
||||
<td>0.165</td>
|
||||
<td>0.166</td>
|
||||
<td><ins>0.136</ins></td>
|
||||
<td><ins>0.113</ins></td>
|
||||
<td><b>0.519</b></td>
|
||||
<td><b>0.252</b></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td rowspan="5">General VLMs</td>
|
||||
<td>Qwen2VL-72B</td>
|
||||
<td>0.298</td>
|
||||
<td>0.342</td>
|
||||
<td>0.142</td>
|
||||
<td>0.244</td>
|
||||
<td>0.431</td>
|
||||
<td>0.363</td>
|
||||
<td>64.2</td>
|
||||
<td>55.5</td>
|
||||
<td>0.425</td>
|
||||
<td>0.581</td>
|
||||
<td>0.193</td>
|
||||
<td>0.182</td>
|
||||
<td>0.792</td>
|
||||
<td>0.359</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Qwen2.5VL-72B</td>
|
||||
<td>0.233</td>
|
||||
<td>0.263</td>
|
||||
<td>0.162</td>
|
||||
<td>0.24</td>
|
||||
<td>0.251</td>
|
||||
<td>0.257</td>
|
||||
<td>69.6</td>
|
||||
<td>67</td>
|
||||
<td>0.313</td>
|
||||
<td>0.353</td>
|
||||
<td>0.205</td>
|
||||
<td>0.204</td>
|
||||
<td>0.597</td>
|
||||
<td>0.349</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Doubao-1.6</td>
|
||||
<td>0.188</td>
|
||||
<td>0.248</td>
|
||||
<td>0.129</td>
|
||||
<td>0.219</td>
|
||||
<td>0.273</td>
|
||||
<td>0.336</td>
|
||||
<td>74.9</td>
|
||||
<td>69.7</td>
|
||||
<td>0.180</td>
|
||||
<td>0.288</td>
|
||||
<td>0.171</td>
|
||||
<td>0.148</td>
|
||||
<td>0.601</td>
|
||||
<td>0.317</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>GPT-5</td>
|
||||
<td>0.242</td>
|
||||
<td>0.373</td>
|
||||
<td>0.119</td>
|
||||
<td>0.36</td>
|
||||
<td>0.398</td>
|
||||
<td>0.456</td>
|
||||
<td>67.9</td>
|
||||
<td>55.8</td>
|
||||
<td>0.26</td>
|
||||
<td>0.397</td>
|
||||
<td>0.191</td>
|
||||
<td>0.28</td>
|
||||
<td>0.88</td>
|
||||
<td>0.46</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Gemini2.5 pro</td>
|
||||
<td>0.185</td>
|
||||
<td>0.20</td>
|
||||
<td><ins>0.115</ins></td>
|
||||
<td>0.155</td>
|
||||
<td>0.288</td>
|
||||
<td>0.326</td>
|
||||
<td><ins>82.6</ins></td>
|
||||
<td>80.3</td>
|
||||
<td>0.154</td>
|
||||
<td>0.182</td>
|
||||
<td>0.181</td>
|
||||
<td>0.136</td>
|
||||
<td><ins>0.535</ins></td>
|
||||
<td>0.26</td>
|
||||
</tr>
|
||||
|
||||
</table>
|
||||
<!-- 脚注说明 -->
|
||||
<tr>
|
||||
<td colspan="5">
|
||||
<sup>*</sup> Tested on the v3/PDF Conversion API (August 2025 deployment).
|
||||
|
||||
</td>
|
||||
</tr>
|
||||
|
||||
|
||||
## Quick Start
|
||||
### 1. Installation
|
||||
```shell
|
||||
conda create -n logis-parsing python=3.10
|
||||
conda activate logis-parsing
|
||||
|
||||
pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124
|
||||
|
||||
```
|
||||
### 2. Download Model Weights
|
||||
|
||||
```
|
||||
# Download our model from Modelscope.
|
||||
pip install modelscope
|
||||
python download_model.py -t modelscope
|
||||
|
||||
# Download our model from huggingface.
|
||||
pip install huggingface_hub
|
||||
python download_model.py -t huggingface
|
||||
```
|
||||
|
||||
### 3. Inference
|
||||
```shell
|
||||
python3 inference.py --image_path PATH_TO_INPUT_IMG --output_path PATH_TO_OUTPUT --model_path PATH_TO_MODEL
|
||||
```
|
||||
|
||||
## Acknowledgments
|
||||
|
||||
|
||||
We would like to acknowledge the following open-source projects that provided inspiration and reference for this work:
|
||||
- [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL)
|
||||
- [OmniDocBench](https://github.com/opendatalab/OmniDocBench)
|
||||
- [Mathpix](https://mathpix.com/)
|
||||
|
||||
24
added_tokens.json
Normal file
24
added_tokens.json
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|
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|
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|
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|
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|
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|
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3
chat_template.json
Normal file
3
chat_template.json
Normal file
@@ -0,0 +1,3 @@
|
||||
{
|
||||
"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
|
||||
}
|
||||
65
config.json
Normal file
65
config.json
Normal file
@@ -0,0 +1,65 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2_5_VLForConditionalGeneration"
|
||||
],
|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 4,
|
||||
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|
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|
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"mrope_section": [
|
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|
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|
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|
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],
|
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
"transformers_version": "4.51.1",
|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
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|
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|
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|
||||
31
|
||||
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|
||||
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|
||||
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|
||||
"in_channels": 3,
|
||||
"in_chans": 3,
|
||||
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|
||||
"model_type": "qwen2_5_vl",
|
||||
"num_heads": 16,
|
||||
"out_hidden_size": 3584,
|
||||
"patch_size": 14,
|
||||
"spatial_merge_size": 2,
|
||||
"spatial_patch_size": 14,
|
||||
"temporal_patch_size": 2,
|
||||
"tokens_per_second": 2,
|
||||
"torch_dtype": "bfloat16",
|
||||
"window_size": 112
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"vocab_size": 152064
|
||||
}
|
||||
12
generation_config.json
Normal file
12
generation_config.json
Normal file
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|
||||
{
|
||||
"bos_token_id": 151643,
|
||||
"do_sample": true,
|
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|
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|
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151643
|
||||
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|
||||
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|
||||
"repetition_penalty": 1.05,
|
||||
"temperature": 1e-06,
|
||||
"transformers_version": "4.51.0"
|
||||
}
|
||||
3
imgs/BenchCls.png
Normal file
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||||
29
preprocessor_config.json
Normal file
29
preprocessor_config.json
Normal file
@@ -0,0 +1,29 @@
|
||||
{
|
||||
"do_convert_rgb": true,
|
||||
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}
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
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|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
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size 11421896
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210
tokenizer_config.json
Normal file
210
tokenizer_config.json
Normal file
@@ -0,0 +1,210 @@
|
||||
{
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
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|
||||
"pad_token": "<|endoftext|>",
|
||||
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|
||||
"processor_class": "Qwen2_5_VLProcessor",
|
||||
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|
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|
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"unk_token": null
|
||||
}
|
||||
BIN
vocab.json
(Stored with Git LFS)
Normal file
BIN
vocab.json
(Stored with Git LFS)
Normal file
Binary file not shown.
Reference in New Issue
Block a user