Add report link and benchmarks
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README.md
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pipeline_tag: document-question-answering
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library_name: transformers
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---
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#
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<!-- Provide a quick summary of what the model is/does. -->
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Vintern-1B-v2-ViTable-docvqa is a fine-tuned version of the 5CD-AI/Vintern-1B-v2 multimodal model for the Vietnamese DocVQA (Table data)
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## Benchmarks
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##
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**Citation:**
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pipeline_tag: document-question-answering
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library_name: transformers
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---
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# Vintern-1B-v2-ViTable-docvqa
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<p align="center">
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<a href="https://drive.google.com/file/d/1MU8bgsAwaWWcTl9GN1gXJcSPUSQoyWXy/view?usp=sharing"><b>Report Link</b>👁️</a>
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</p>
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<!-- Provide a quick summary of what the model is/does. -->
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Vintern-1B-v2-ViTable-docvqa is a fine-tuned version of the 5CD-AI/Vintern-1B-v2 multimodal model for the Vietnamese DocVQA (Table data)
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## Benchmarks
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<div align="center">
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| Model | ANLS | Semantic Similarity | MLLM-as-judge (Gemini) |
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|-----------------------------|------------------------|------------------------|------------------------|
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| Gemini 1.5 Flash | 0.35 | 0.56 | 0.40 |
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| Vintern-1B-v2 | 0.04 | 0.45 | 0.50 |
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| Vintern-1B-v2-ViTable-docvq | **0.50** | **0.71** | **0.59** |
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</div>
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<!-- Code benchmark: to be written later -->
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<!-- To be written later ## Usage
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You can use this notebook <a href="https://colab.research.google.com/"> <img src="https://colab.research.google.com/img/colab_favicon_256px.png" width="30"></a> -->
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**Citation:**
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