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HueyNemud
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icdar23-entrydetector_plaintext_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: 0.0045
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- - Ebegin: {'precision': 0.9977203647416414, 'recall': 0.9875893192929672, 'f1': 0.9926289926289926, 'number': 2659}
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- - Eend: {'precision': 0.9962221382697394, 'recall': 0.9854260089686099, 'f1': 0.9907946646627841, 'number': 2676}
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- - Overall Precision: 0.9970
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- - Overall Recall: 0.9865
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- - Overall F1: 0.9917
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- - Overall Accuracy: 0.9986
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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 | 0.0294 | 0.9739 | 0.9925 | 0.9831 | 0.9972 |
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- | 0.1819 | 0.14 | 600 | 0.0152 | 0.9911 | 0.9804 | 0.9857 | 0.9978 |
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- | 0.1819 | 0.21 | 900 | 0.0067 | 0.9871 | 0.9959 | 0.9915 | 0.9986 |
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- | 0.0165 | 0.29 | 1200 | 0.0077 | 0.9871 | 0.9961 | 0.9916 | 0.9986 |
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- | 0.0103 | 0.36 | 1500 | 0.0065 | 0.9872 | 0.9962 | 0.9917 | 0.9986 |
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- | 0.0103 | 0.43 | 1800 | 0.0053 | 0.9903 | 0.9952 | 0.9927 | 0.9988 |
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- | 0.0087 | 0.5 | 2100 | 0.0068 | 0.9974 | 0.9886 | 0.9930 | 0.9988 |
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- | 0.0087 | 0.57 | 2400 | 0.0073 | 0.9951 | 0.9877 | 0.9914 | 0.9985 |
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- | 0.0078 | 0.64 | 2700 | 0.0045 | 0.9899 | 0.9946 | 0.9923 | 0.9987 |
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- | 0.0054 | 0.72 | 3000 | 0.0043 | 0.9978 | 0.9905 | 0.9941 | 0.9990 |
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- | 0.0054 | 0.79 | 3300 | 0.0042 | 0.9976 | 0.9899 | 0.9938 | 0.9989 |
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- | 0.0047 | 0.86 | 3600 | 0.0042 | 0.9955 | 0.9925 | 0.9940 | 0.9990 |
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- | 0.0047 | 0.93 | 3900 | 0.0048 | 0.9865 | 0.9974 | 0.9920 | 0.9986 |
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- | 0.0044 | 1.0 | 4200 | 0.0034 | 0.9979 | 0.9919 | 0.9949 | 0.9991 |
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- | 0.0026 | 1.07 | 4500 | 0.0041 | 0.9954 | 0.9944 | 0.9949 | 0.9991 |
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- | 0.0026 | 1.14 | 4800 | 0.0036 | 0.9979 | 0.9922 | 0.9950 | 0.9992 |
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- | 0.0029 | 1.22 | 5100 | 0.0037 | 0.9956 | 0.9931 | 0.9944 | 0.9991 |
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- | 0.0029 | 1.29 | 5400 | 0.0050 | 0.9899 | 0.9956 | 0.9927 | 0.9988 |
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- | 0.0029 | 1.36 | 5700 | 0.0034 | 0.9975 | 0.9935 | 0.9955 | 0.9993 |
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- | 0.0028 | 1.43 | 6000 | 0.0036 | 0.9970 | 0.9937 | 0.9954 | 0.9992 |
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- | 0.0028 | 1.5 | 6300 | 0.0038 | 0.9932 | 0.9951 | 0.9942 | 0.9990 |
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- | 0.0027 | 1.57 | 6600 | 0.0034 | 0.9969 | 0.9933 | 0.9951 | 0.9992 |
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- | 0.0027 | 1.65 | 6900 | 0.0034 | 0.9974 | 0.9929 | 0.9952 | 0.9992 |
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- | 0.0027 | 1.72 | 7200 | 0.0036 | 0.9970 | 0.9934 | 0.9952 | 0.9992 |
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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: 0.0078
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+ - Ebegin: {'precision': 0.9920303605313093, 'recall': 0.9830763444904099, 'f1': 0.9875330562901399, 'number': 2659}
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+ - Eend: {'precision': 0.9958443520967133, 'recall': 0.9850523168908819, 'f1': 0.9904189366898367, 'number': 2676}
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+ - Overall Precision: 0.9939
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+ - Overall Recall: 0.9841
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+ - Overall F1: 0.9890
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+ - Overall Accuracy: 0.9982
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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 | 0.0314 | 0.9572 | 0.9870 | 0.9719 | 0.9956 |
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+ | 0.1574 | 0.14 | 600 | 0.0145 | 0.9897 | 0.9834 | 0.9866 | 0.9979 |
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+ | 0.1574 | 0.21 | 900 | 0.0098 | 0.9896 | 0.9917 | 0.9907 | 0.9985 |
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+ | 0.0161 | 0.29 | 1200 | 0.0079 | 0.9919 | 0.9921 | 0.9920 | 0.9987 |
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+ | 0.0107 | 0.36 | 1500 | 0.0072 | 0.9895 | 0.9928 | 0.9911 | 0.9986 |
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+ | 0.0107 | 0.43 | 1800 | 0.0116 | 0.9900 | 0.9877 | 0.9888 | 0.9981 |
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+ | 0.0114 | 0.5 | 2100 | 0.0069 | 0.9965 | 0.9898 | 0.9931 | 0.9988 |
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+ | 0.0114 | 0.57 | 2400 | 0.0055 | 0.9955 | 0.9907 | 0.9931 | 0.9989 |
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+ | 0.0082 | 0.64 | 2700 | 0.0051 | 0.9870 | 0.9956 | 0.9913 | 0.9985 |
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+ | 0.0062 | 0.72 | 3000 | 0.0046 | 0.9903 | 0.9957 | 0.9930 | 0.9988 |
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+ | 0.0062 | 0.79 | 3300 | 0.0038 | 0.9957 | 0.9929 | 0.9943 | 0.9990 |
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+ | 0.0051 | 0.86 | 3600 | 0.0038 | 0.9956 | 0.9943 | 0.9949 | 0.9992 |
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+ | 0.0051 | 0.93 | 3900 | 0.0047 | 0.9902 | 0.9942 | 0.9921 | 0.9987 |
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+ | 0.0041 | 1.0 | 4200 | 0.0035 | 0.9979 | 0.9917 | 0.9948 | 0.9991 |
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+ | 0.0029 | 1.07 | 4500 | 0.0036 | 0.9973 | 0.9926 | 0.9949 | 0.9992 |
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+ | 0.0029 | 1.14 | 4800 | 0.0038 | 0.9969 | 0.9916 | 0.9942 | 0.9990 |
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+ | 0.0034 | 1.22 | 5100 | 0.0036 | 0.9953 | 0.9935 | 0.9944 | 0.9991 |
 
 
 
 
 
 
 
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  ### Framework versions