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icdar23-entrydetector_plaintext

Token Classification
Transformers
PyTorch
camembert
Generated from Trainer
Inference Endpoints
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update model card README.md

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  1. README.md +17 -19
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@@ -13,13 +13,13 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0337
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- - Ebegin: {'precision': 0.9737045630317092, 'recall': 0.9469725460699511, 'f1': 0.9601525262154433, 'number': 2659}
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- - Eend: {'precision': 0.9644312708410523, 'recall': 0.9727204783258595, 'f1': 0.9685581395348838, 'number': 2676}
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- - Overall Precision: 0.9690
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- - Overall Recall: 0.9599
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- - Overall F1: 0.9644
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- - Overall Accuracy: 0.9931
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 0.07 | 300 | 0.0380 | 0.9713 | 0.9691 | 0.9702 | 0.9942 |
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- | 0.1537 | 0.14 | 600 | 0.0318 | 0.9933 | 0.9550 | 0.9738 | 0.9947 |
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- | 0.1537 | 0.21 | 900 | 0.0185 | 0.9842 | 0.9780 | 0.9811 | 0.9962 |
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- | 0.0262 | 0.29 | 1200 | 0.0176 | 0.9883 | 0.9754 | 0.9818 | 0.9963 |
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- | 0.0171 | 0.36 | 1500 | 0.0174 | 0.9915 | 0.9650 | 0.9781 | 0.9955 |
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- | 0.0171 | 0.43 | 1800 | 0.0139 | 0.9869 | 0.9787 | 0.9828 | 0.9965 |
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- | 0.0151 | 0.5 | 2100 | 0.0142 | 0.9845 | 0.9814 | 0.9830 | 0.9965 |
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- | 0.0151 | 0.57 | 2400 | 0.0185 | 0.9894 | 0.9713 | 0.9803 | 0.9960 |
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- | 0.0144 | 0.64 | 2700 | 0.0150 | 0.9864 | 0.9789 | 0.9827 | 0.9965 |
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- | 0.0134 | 0.72 | 3000 | 0.0197 | 0.9848 | 0.9734 | 0.9791 | 0.9957 |
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- | 0.0134 | 0.79 | 3300 | 0.0201 | 0.9809 | 0.9804 | 0.9806 | 0.9960 |
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- | 0.012 | 0.86 | 3600 | 0.0163 | 0.9794 | 0.9832 | 0.9813 | 0.9961 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0424
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+ - Ebegin: {'precision': 0.9725125822686799, 'recall': 0.9447160586686725, 'f1': 0.9584128195345288, 'number': 2659}
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+ - Eend: {'precision': 0.9570211189329382, 'recall': 0.9652466367713004, 'f1': 0.9611162790697675, 'number': 2676}
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+ - Overall Precision: 0.9646
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+ - Overall Recall: 0.9550
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+ - Overall F1: 0.9598
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+ - Overall Accuracy: 0.9923
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 0.07 | 300 | 0.0487 | 0.9874 | 0.9565 | 0.9717 | 0.9943 |
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+ | 0.1698 | 0.14 | 600 | 0.0310 | 0.9891 | 0.9709 | 0.9799 | 0.9959 |
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+ | 0.1698 | 0.21 | 900 | 0.0267 | 0.9746 | 0.9764 | 0.9755 | 0.9953 |
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+ | 0.0346 | 0.29 | 1200 | 0.0217 | 0.9885 | 0.9685 | 0.9784 | 0.9956 |
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+ | 0.0237 | 0.36 | 1500 | 0.0201 | 0.9866 | 0.9742 | 0.9804 | 0.9960 |
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+ | 0.0237 | 0.43 | 1800 | 0.0268 | 0.9883 | 0.9561 | 0.9719 | 0.9944 |
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+ | 0.0205 | 0.5 | 2100 | 0.0216 | 0.9823 | 0.9779 | 0.9801 | 0.9959 |
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+ | 0.0205 | 0.57 | 2400 | 0.0236 | 0.9874 | 0.9700 | 0.9787 | 0.9957 |
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+ | 0.0196 | 0.64 | 2700 | 0.0246 | 0.9877 | 0.9668 | 0.9772 | 0.9954 |
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+ | 0.0195 | 0.72 | 3000 | 0.0254 | 0.9789 | 0.9682 | 0.9735 | 0.9950 |
 
 
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  ### Framework versions