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udop

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 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+license: mit
+tags:
+- vision
+inference: false
 ---
+# UDOP model
+The UDOP model was proposed in [Unifying Vision, Text, and Layout for Universal Document Processing](https://arxiv.org/abs/2212.02623) by Zineng Tang, Ziyi Yang, Guoxin Wang, Yuwei Fang, Yang Liu, Chenguang Zhu, Michael Zeng, Cha Zhang, Mohit Bansal.
+## 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 for one-shot semantic segmentation:
+```python
+from transformers import AutoProcessor, UdopForConditionalGeneration
+from datasets import load_dataset
+# load model and processor
+processor = AutoProcessor.from_pretrained("microsoft/udop-large", apply_ocr=False)
+model = UdopForConditionalGeneration.from_pretrained("microsoft/udop-large")
+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?"
+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
+```
+### BibTeX entry and citation info
+```bibtex
+@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}
+}
+```