Model reference · open weights
donut-finetuned-cord is an open-weight language model from naver-clova-ix. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | naver-clova-ix |
|---|---|
| Type | Language models |
| Task | Image→text |
| Runs with | transformers |
| Released | 2022-07-19 |
| Popularity | 9k downloads / month |
| Licence | Open weights |
About
Donut model fine-tuned on CORD. 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 batch_size, seq_len, hidden_size), after which the decoder autoregressively generates text, conditioned on the encoding of the encoder.
This model is fine-tuned on CORD, a document parsing dataset.
We refer to the documentation which includes code examples.
@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}
}
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys donut-finetuned-cord for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (donut-finetuned-cord below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"donut-finetuned-cord","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.