Open-weight model · Document question answering
donut-base-finetuned-docvqa
by NAVER CLOVA INFORMATION EXTRACTION naver-clova-ix/donut-base-finetuned-docvqa
Donut model fine-tuned on DocVQA. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository.
Model Card
By NAVER CLOVA INFORMATION EXTRACTION, published under mit, revision b19d2e332684.
Donut model fine-tuned on DocVQA. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository. Disclaimer: The team releasing Donut did not write a model card for this model so this model card has been written by the Hugging Face team. Donut consists of a vision encoder (Swin Transformer) and a text decoder (BART). Given an image, the encoder first encodes the image into a tensor of embeddings (of shape batchsize, seqlen, hiddensize), after which the decoder autoregressively generates text, conditioned on the encoding of the encoder. This model is fine-tuned on DocVQA, a document visual question answering dataset. We…
Read NAVER CLOVA INFORMATION EXTRACTION's full model card
Donut (base-sized model, fine-tuned on DocVQA)
Donut model fine-tuned on DocVQA. It was introduced in the paper OCR-free Document Understanding Transformer by Geewok et al. and first released in this repository.
Disclaimer: The team releasing Donut did not write a model card for this model so this model card has been written by the Hugging Face team.
Model description
Donut consists of a vision encoder (Swin Transformer) and a text decoder (BART). Given an image, the encoder first encodes the image into a tensor of embeddings (of shape batch_size, seq_len, hidden_size), after which the decoder autoregressively generates text, conditioned on the encoding of the encoder.
Intended uses & limitations
This model is fine-tuned on DocVQA, a document visual question answering dataset.
We refer to the documentation which includes code examples.
BibTeX entry and citation info
@article{DBLP:journals/corr/abs-2111-15664,
author = {Geewook Kim and
Teakgyu Hong and
Moonbin Yim and
Jinyoung Park and
Jinyeong Yim and
Wonseok Hwang and
Sangdoo Yun and
Dongyoon Han and
Seunghyun Park},
title = {Donut: Document Understanding Transformer without {OCR}},
journal = {CoRR},
volume = {abs/2111.15664},
year = {2021},
url = {https://arxiv.org/abs/2111.15664},
eprinttype = {arXiv},
eprint = {2111.15664},
timestamp = {Thu, 02 Dec 2021 10:50:44 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2111-15664.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Configuration
- Architecture
- VisionEncoderDecoderModel
- Stored precision
- float32
- Model type
- vision-encoder-decoder
Identity and Version
- Repository
- naver-clova-ix/donut-base-finetuned-docvqa
- Publisher
- NAVER CLOVA INFORMATION EXTRACTION
- Task
- Document question answering
- Modality
- Other
- Library
- transformers
- Parameters
- Not stated by the source
- Languages
- Not stated by the source
- Revision
- b19d2e332684b0e2d35d9144ce34047767335cf8
- First published
- 2022-07-19
- Last updated
- 2024-03-09
Files and Weights
11 files, 808.7 MB in total. The weights are 1 file totalling 803.4 MB in bin.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| pytorch_model.bin | Weights | 803.4 MB | 0783ed4980d0 |
| added_tokens.json | Configuration | 229 B | — |
| config.json | Configuration | 4.7 KB | — |
| preprocessor_config.json | Configuration | 359 B | — |
| special_tokens_map.json | Configuration | 478 B | — |
| README.md | Documentation | 2.4 KB | — |
| sentencepiece.bpe.model | Other | 1.3 MB | cb9e3dce4c32 |
| .gitattributes | Repository | 1.3 KB | — |
| .gitignore | Repository | 10 B | — |
| tokenizer.json | Tokenizer | 4.0 MB | — |
| tokenizer_config.json | Tokenizer | 535 B | — |
License and Download
- License
- mit
- Access
- Open weights, no gate
- Download size
- 803.4 MB
Released by NAVER CLOVA INFORMATION EXTRACTION through its official repository on Hugging Face. Read the license.
Built From
- Described by arXiv:2111.15664
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 803.4 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About donut-base-finetuned-docvqa
Can I use donut-base-finetuned-docvqa commercially?
Yes. donut-base-finetuned-docvqa is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.