@@ -134,8 +134,8 @@ The `gt_parse` follows the format of `{"class" : {class_name}}`, for example, `{
#### For Document Information Extraction
The `gt_parse` is a JSON object that contains full information of the document image, for example, the JSON object for a receipt may look like `{"menu" : [{"nm": "ICE BLACKCOFFEE", "cnt": "2", ...}, ...], ...}`.
- More examples are available at [CORD dataset](https://huggingface.co/datasets/naver-clova-ix/cord-v2).
- Google colab demo is available [here](https://huggingface.co/spaces/naver-clova-ix/donut-base-finetuned-cord-v2).
- Gradio web demo is available [here](https://colab.research.google.com/drive/1o07hty-3OQTvGnc_7lgQFLvvKQuLjqiw?usp=sharing).
- Google colab demo is available [here](https://colab.research.google.com/drive/1o07hty-3OQTvGnc_7lgQFLvvKQuLjqiw?usp=sharing).
- Gradio web demo is available [here](https://huggingface.co/spaces/naver-clova-ix/donut-base-finetuned-cord-v2).
#### For Document Visual Question Answering
The `gt_parses` follows the format of `[{"question" : {question_sentence}, "answer" : {answer_candidate_1}}, {"question" : {question_sentence}, "answer" : {answer_candidate_2}}, ...]`, for example, `[{"question" : "what is the model name?", "answer" : "donut"}, {"question" : "what is the model name?", "answer" : "document understanding transformer"}]`.
