Document Question Answering
Transformers
Safetensors
Vietnamese
internvl_chat
image-feature-extraction
custom_code
Instructions to use YuukiAsuna/Vintern-1B-v2-ViTable-docvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YuukiAsuna/Vintern-1B-v2-ViTable-docvqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="YuukiAsuna/Vintern-1B-v2-ViTable-docvqa", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("YuukiAsuna/Vintern-1B-v2-ViTable-docvqa", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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---
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license: mit
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---
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license: mit
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datasets:
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- YuukiAsuna/VietnameseTableVQA
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language:
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- vi
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base_model:
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- 5CD-AI/Vintern-1B-v2
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pipeline_tag: document-question-answering
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library_name: transformers
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---
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# Model Card for Model ID
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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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To be developed later
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## Quickstart
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To be developed later
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**Citation:**
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```bibtex
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@misc{doan2024vintern1befficientmultimodallarge,
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title={Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese},
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author={Khang T. Doan and Bao G. Huynh and Dung T. Hoang and Thuc D. Pham and Nhat H. Pham and Quan T. M. Nguyen and Bang Q. Vo and Suong N. Hoang},
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year={2024},
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eprint={2408.12480},
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archivePrefix={arXiv},
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primaryClass={cs.LG},
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url={https://arxiv.org/abs/2408.12480},
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}
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```
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