Update README.md

README.md

@@ -6,46 +6,18 @@ tags:
 - 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).
 
-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.
+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 description
+# Use with Inference Endpoints 
 
-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 CORD, a document parsing dataset.
-
-We refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/donut) which includes code examples.
-
-### BibTeX entry and citation info
-
-```bibtex
-@article{DBLP:journals/corr/abs-2111-15664,
-  author    = {Geewook Kim and
-               Teakgyu Hong and
-               Moonbin Yim and
-               Jinyoung Park and
-               Jinyeong Yim and
-               Wonseok Hwang and
-               Sangdoo Yun and
-               Dongyoon Han and
-               Seunghyun Park},
-  title     = {Donut: Document Understanding Transformer without {OCR}},
-  journal   = {CoRR},
-  volume    = {abs/2111.15664},
-  year      = {2021},
-  url       = {https://arxiv.org/abs/2111.15664},
-  eprinttype = {arXiv},
-  eprint    = {2111.15664},
-  timestamp = {Thu, 02 Dec 2021 10:50:44 +0100},
-  biburl    = {https://dblp.org/rec/journals/corr/abs-2111-15664.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}
-```