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@@ -48,8 +48,8 @@ At last, in order to use Hugging Face notebooks on fine-tuning layout models lik
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  For all these reasons, I decided to process the DocLayNet dataset:
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  - into 3 datasets of different sizes:
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- - [DocLayNet small](https://huggingface.co/datasets/pierreguillou/DocLayNet-small) < 1.000k document images (691 train, 64 val, 49 test)
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- - [DocLayNet base](https://huggingface.co/datasets/pierreguillou/DocLayNet-base) < 10.000k document images (6910 train, 648 val, 499 test)
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  - DocLayNet large with full dataset (to be done)
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  - with associated texts,
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  - and in a format facilitating their use by HF notebooks.
@@ -58,6 +58,8 @@ For all these reasons, I decided to process the DocLayNet dataset:
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  ### Download & overview
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  ```
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  # !pip install -q datasets
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  For all these reasons, I decided to process the DocLayNet dataset:
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  - into 3 datasets of different sizes:
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+ - [DocLayNet small](https://huggingface.co/datasets/pierreguillou/DocLayNet-small) (about 1% of DocLayNet) < 1.000k document images (691 train, 64 val, 49 test)
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+ - [DocLayNet base](https://huggingface.co/datasets/pierreguillou/DocLayNet-base) (about 10% of DocLayNet) < 10.000k document images (6910 train, 648 val, 499 test)
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  - DocLayNet large with full dataset (to be done)
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  - with associated texts,
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  - and in a format facilitating their use by HF notebooks.
 
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  ### Download & overview
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+ The size of the DocLayNet small is about 10% of the DocLayNet dataset (random selection respectively in the train, val and test files).
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+
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  ```
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  # !pip install -q datasets
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