Create README.md

README.md

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+---
+license: mit
+pipeline_tag: document-question-answering
+tags:
+- donut
+- image-to-text
+- vision
+widget:
+- text: "What is the invoice number?"
+  src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"
+- text: "What is the purchase amount?"
+  src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/contract.jpeg"
+---
+
+# Donut (base-sized model, fine-tuned on DocVQA) 
+
+Donut model fine-tuned on DocVQA. 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).
+
+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.
+
+## Model description
+
+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. 
+
+![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/donut_architecture.jpg)
+
+## Intended uses & limitations
+
+This model is fine-tuned on DocVQA, a document visual question answering dataset.
+
+We refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/donut) which includes code examples.