metadata

license: mit
tags:
  - donut
  - image-to-text
  - vision

Fork of naver-clova-ix/donut-base-finetuned-cord-v2

This is fork of 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

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 by Geewok et al. and first released in this repository.

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