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HueyNemud
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icdar23-entrydetector_texttokens_breaks_indents_left_diff_right_ref

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

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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: 0.0474
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- - Ebegin: {'precision': 0.9822681935722202, 'recall': 0.9870081662954714, 'f1': 0.9846324754675061, 'number': 2694}
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- - Eend: {'precision': 0.9790132547864506, 'recall': 0.9840858623242043, 'f1': 0.9815430047988187, 'number': 2702}
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- - Overall Precision: 0.9806
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- - Overall Recall: 0.9855
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- - Overall F1: 0.9831
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- - Overall Accuracy: 0.9880
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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 | 0.0916 | 0.9560 | 0.9910 | 0.9731 | 0.9821 |
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- | 0.32 | 0.14 | 600 | 0.0893 | 0.9919 | 0.9482 | 0.9695 | 0.9803 |
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- | 0.32 | 0.21 | 900 | 0.0772 | 0.9903 | 0.9419 | 0.9655 | 0.9778 |
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- | 0.114 | 0.29 | 1200 | 0.0442 | 0.9811 | 0.9798 | 0.9805 | 0.9871 |
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- | 0.0983 | 0.36 | 1500 | 0.0351 | 0.9907 | 0.9826 | 0.9866 | 0.9912 |
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- | 0.0983 | 0.43 | 1800 | 0.0325 | 0.9917 | 0.9856 | 0.9887 | 0.9926 |
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- | 0.0869 | 0.5 | 2100 | 0.0237 | 0.9905 | 0.9938 | 0.9921 | 0.9948 |
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- | 0.0869 | 0.57 | 2400 | 0.0316 | 0.9890 | 0.9870 | 0.9880 | 0.9921 |
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- | 0.0844 | 0.64 | 2700 | 0.0271 | 0.9863 | 0.9892 | 0.9877 | 0.9919 |
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- | 0.0794 | 0.72 | 3000 | 0.0287 | 0.9872 | 0.9901 | 0.9887 | 0.9926 |
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- | 0.0794 | 0.79 | 3300 | 0.0281 | 0.9862 | 0.9891 | 0.9876 | 0.9918 |
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- | 0.0561 | 0.86 | 3600 | 0.0259 | 0.9883 | 0.9934 | 0.9908 | 0.9939 |
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.0444
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+ - Ebegin: {'precision': 0.9864814119414195, 'recall': 0.9751299183370453, 'f1': 0.9807728206085495, 'number': 2694}
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+ - Eend: {'precision': 0.9851024208566108, 'recall': 0.9789045151739453, 'f1': 0.9819936885093744, 'number': 2702}
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+ - Overall Precision: 0.9858
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+ - Overall Recall: 0.9770
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+ - Overall F1: 0.9814
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+ - Overall Accuracy: 0.9868
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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 | 0.0977 | 0.9752 | 0.9604 | 0.9677 | 0.9788 |
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+ | 0.31 | 0.14 | 600 | 0.0922 | 0.9910 | 0.9415 | 0.9656 | 0.9779 |
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+ | 0.31 | 0.21 | 900 | 0.0628 | 0.9926 | 0.9534 | 0.9726 | 0.9823 |
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+ | 0.1143 | 0.29 | 1200 | 0.0570 | 0.9715 | 0.9802 | 0.9759 | 0.9838 |
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+ | 0.0878 | 0.36 | 1500 | 0.0393 | 0.9914 | 0.9731 | 0.9822 | 0.9885 |
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+ | 0.0878 | 0.43 | 1800 | 0.0437 | 0.9825 | 0.9819 | 0.9822 | 0.9883 |
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+ | 0.0884 | 0.5 | 2100 | 0.0296 | 0.9908 | 0.9861 | 0.9885 | 0.9924 |
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+ | 0.0884 | 0.57 | 2400 | 0.0340 | 0.9913 | 0.9837 | 0.9875 | 0.9918 |
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+ | 0.0898 | 0.64 | 2700 | 0.0294 | 0.9833 | 0.9932 | 0.9882 | 0.9923 |
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+ | 0.066 | 0.72 | 3000 | 0.0369 | 0.9853 | 0.9849 | 0.9851 | 0.9904 |
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+ | 0.066 | 0.79 | 3300 | 0.0245 | 0.9892 | 0.9889 | 0.9890 | 0.9928 |
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+ | 0.0575 | 0.86 | 3600 | 0.0230 | 0.9879 | 0.9924 | 0.9901 | 0.9936 |
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+ | 0.0575 | 0.93 | 3900 | 0.0282 | 0.9865 | 0.9831 | 0.9848 | 0.9908 |
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+ | 0.064 | 1.0 | 4200 | 0.0244 | 0.9945 | 0.9822 | 0.9883 | 0.9923 |
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+ | 0.0626 | 1.07 | 4500 | 0.0203 | 0.9929 | 0.9880 | 0.9905 | 0.9937 |
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+ | 0.0626 | 1.14 | 4800 | 0.0198 | 0.9920 | 0.9891 | 0.9905 | 0.9937 |
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+ | 0.0419 | 1.22 | 5100 | 0.0219 | 0.9895 | 0.9878 | 0.9886 | 0.9925 |
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+ | 0.0419 | 1.29 | 5400 | 0.0235 | 0.9890 | 0.9876 | 0.9883 | 0.9923 |
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+ | 0.0564 | 1.36 | 5700 | 0.0212 | 0.9935 | 0.9880 | 0.9908 | 0.9939 |
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+ | 0.0427 | 1.43 | 6000 | 0.0238 | 0.9934 | 0.9839 | 0.9886 | 0.9925 |
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+ | 0.0427 | 1.5 | 6300 | 0.0193 | 0.9862 | 0.9920 | 0.9891 | 0.9928 |
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+ | 0.0501 | 1.57 | 6600 | 0.0212 | 0.9919 | 0.9885 | 0.9902 | 0.9935 |
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+ | 0.0501 | 1.65 | 6900 | 0.0225 | 0.9911 | 0.9880 | 0.9896 | 0.9931 |
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+ | 0.0488 | 1.72 | 7200 | 0.0212 | 0.9904 | 0.9892 | 0.9898 | 0.9933 |
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  ### Framework versions