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vt5-base-spdocvqa
rubentito/vt5-base-spdocvqa
vt5-base-spdocvqa is a machine learning model from rubentito. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as gpl-3.0.
This is VT5 base fine-tuned on Single-Page DocVQA (SP-DocVQA) dataset using the MP-DocVQA framework. VT5 is a version of the Hi-VT5 described in MP-DocVQA paper, arranged in a non-hierarchical paradigm (using only one…
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At a glance
- Library
- transformers
- License
- gpl-3.0
- Model type
- t5
- Access
- Public
- Created
- Jul 17, 2023
- Updated
- Jul 21, 2023
- SHA
- c91ba1c0
Datasets
Model card
From the Hugging Face model README
VT5 base fine-tuned on SP-DocVQA
This is VT5 base fine-tuned on Single-Page DocVQA (SP-DocVQA) dataset using the MP-DocVQA framework. VT5 is a version of the Hi-VT5 described in MP-DocVQA paper, arranged in a non-hierarchical paradigm (using only one page for each question-answer pair). Before fine-tuning, we start from pre-trained t5-base for the language backbone, and pre-trained DiT-base to embed visual features (which we keep frozen during fine-tune phase).
Please, note that VT5 is not integrated into Hugginface, and therefore you must use the MP-DocVQA framework (WIP) or PFL-DocVQA competition framework to use it.
This method is the base architecture for the PFL-DocVQA Competition that will will take place from the 1st of July to the 1st of November, 2023. If you are interested in Federated Learning and Differential Privacy we invite you to have a look at the PFL-DocVQA Challenge and Competition hold on these topics.
- Library
- transformers
- Type
- t5
- License
- gpl-3.0
- Languages
- en
- Created
- Jul 17, 2023
- Updated
- Jul 21, 2023