udop-large

Model description

UDOP adopts an encoder-decoder Transformer architecture based on T5 for document AI tasks like document image classification, document parsing and document visual question answering.

Intended uses & limitations

You can use the model for document image classification, document parsing and document visual question answering (DocVQA).

How to use

Here's how to use the model on a document image:

from transformers import AutoProcessor, UdopForConditionalGeneration
from datasets import load_dataset

# load model and processor
# in this case, we already have performed OCR ourselves
# so we initialize the processor with `apply_ocr=False`
processor = AutoProcessor.from_pretrained("microsoft/udop-large", apply_ocr=False)
model = UdopForConditionalGeneration.from_pretrained("microsoft/udop-large")

# load an example image, along with the words and coordinates
# which were extracted using an OCR engine
dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
example = dataset[0]
image = example["image"]
words = example["tokens"]
boxes = example["bboxes"]
question = "Question answering. What is the date on the form?"

# prepare everything for the model
encoding = processor(image, question, words, boxes=boxes, return_tensors="pt")

# autoregressive generation
predicted_ids = model.generate(**encoding)
print(processor.batch_decode(predicted_ids, skip_special_tokens=True)[0])
9/30/92

Refer to the demo notebooks for fine-tuning/inference.

BibTeX entry and citation info

@misc{tang2023unifying,
      title={Unifying Vision, Text, and Layout for Universal Document Processing},
      author={Zineng Tang and Ziyi Yang and Guoxin Wang and Yuwei Fang and Yang Liu and Chenguang Zhu and Michael Zeng and Cha Zhang and Mohit Bansal},
      year={2023},
      eprint={2212.02623},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Data Summary

https://huggingface.co/microsoft/udop-large/blob/main/data_summary_card.md