|
--- |
|
license: mit |
|
tags: |
|
- donut |
|
- image-to-text |
|
- vision |
|
--- |
|
|
|
# Fork of [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2) |
|
|
|
> This is fork of [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2) implementing a custom `handler.py` as an example for how to use `flair` models with [inference-endpoints](https://hf.co/inference-endpoints) |
|
|
|
--- |
|
|
|
# Donut (base-sized model, fine-tuned on CORD) |
|
|
|
Donut model fine-tuned on CORD. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut). |
|
|
|
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. |
|
|
|
# Use with Inference Endpoints |
|
|
|
|
|
|
