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HueyNemud
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icdar23-entrydetector_plaintext_breaks_indents_left_ref_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: 0.0033
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- - Ebegin: {'precision': 0.9935849056603774, 'recall': 0.9902218879277924, 'f1': 0.9919005462422301, 'number': 2659}
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- - Eend: {'precision': 0.996606334841629, 'recall': 0.9876681614349776, 'f1': 0.9921171171171171, 'number': 2676}
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- - Overall Precision: 0.9951
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- - Overall Recall: 0.9889
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- - Overall F1: 0.9920
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- - Overall Accuracy: 0.9989
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  ## Model description
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@@ -50,26 +50,25 @@ The following hyperparameters were used during training:
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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 | 0.0262 | 0.9758 | 0.9906 | 0.9832 | 0.9973 |
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- | 0.1599 | 0.14 | 600 | 0.0116 | 0.9866 | 0.9919 | 0.9892 | 0.9982 |
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- | 0.1599 | 0.21 | 900 | 0.0111 | 0.9907 | 0.9856 | 0.9882 | 0.9980 |
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- | 0.0162 | 0.29 | 1200 | 0.0096 | 0.9813 | 0.9966 | 0.9889 | 0.9981 |
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- | 0.0099 | 0.36 | 1500 | 0.0060 | 0.9820 | 0.9955 | 0.9887 | 0.9982 |
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- | 0.0099 | 0.43 | 1800 | 0.0046 | 0.9925 | 0.9934 | 0.9929 | 0.9988 |
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- | 0.0074 | 0.5 | 2100 | 0.0057 | 0.9961 | 0.9880 | 0.9920 | 0.9987 |
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- | 0.0074 | 0.57 | 2400 | 0.0039 | 0.9911 | 0.9953 | 0.9932 | 0.9988 |
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- | 0.0072 | 0.64 | 2700 | 0.0075 | 0.9842 | 0.9949 | 0.9895 | 0.9982 |
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- | 0.0061 | 0.72 | 3000 | 0.0040 | 0.9906 | 0.9963 | 0.9934 | 0.9989 |
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- | 0.0061 | 0.79 | 3300 | 0.0034 | 0.9955 | 0.9936 | 0.9946 | 0.9991 |
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- | 0.005 | 0.86 | 3600 | 0.0034 | 0.9933 | 0.9946 | 0.9939 | 0.9990 |
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- | 0.005 | 0.93 | 3900 | 0.0047 | 0.9847 | 0.9976 | 0.9911 | 0.9985 |
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- | 0.0041 | 1.0 | 4200 | 0.0031 | 0.9972 | 0.9936 | 0.9954 | 0.9992 |
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- | 0.0031 | 1.07 | 4500 | 0.0030 | 0.9967 | 0.9945 | 0.9956 | 0.9992 |
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- | 0.0031 | 1.14 | 4800 | 0.0032 | 0.9966 | 0.9938 | 0.9952 | 0.9992 |
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- | 0.003 | 1.22 | 5100 | 0.0029 | 0.9960 | 0.9939 | 0.9949 | 0.9991 |
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- | 0.003 | 1.29 | 5400 | 0.0030 | 0.9935 | 0.9947 | 0.9941 | 0.9990 |
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- | 0.0023 | 1.36 | 5700 | 0.0028 | 0.9973 | 0.9933 | 0.9953 | 0.9992 |
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- | 0.0027 | 1.43 | 6000 | 0.0029 | 0.9968 | 0.9936 | 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: 0.0068
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+ - Ebegin: {'precision': 1.0, 'recall': 0.9793155321549455, 'f1': 0.9895496864905947, 'number': 2659}
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+ - Eend: {'precision': 0.9988562714449104, 'recall': 0.9790732436472347, 'f1': 0.9888658237403285, 'number': 2676}
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+ - Overall Precision: 0.9994
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+ - Overall Recall: 0.9792
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+ - Overall F1: 0.9892
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+ - Overall Accuracy: 0.9983
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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 | 0.0309 | 0.9637 | 0.9910 | 0.9771 | 0.9964 |
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+ | 0.181 | 0.14 | 600 | 0.0144 | 0.9777 | 0.9863 | 0.9819 | 0.9974 |
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+ | 0.181 | 0.21 | 900 | 0.0095 | 0.9969 | 0.9845 | 0.9906 | 0.9985 |
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+ | 0.0168 | 0.29 | 1200 | 0.0105 | 0.9869 | 0.9913 | 0.9891 | 0.9982 |
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+ | 0.011 | 0.36 | 1500 | 0.0063 | 0.9937 | 0.9915 | 0.9926 | 0.9988 |
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+ | 0.011 | 0.43 | 1800 | 0.0064 | 0.9883 | 0.9940 | 0.9911 | 0.9986 |
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+ | 0.01 | 0.5 | 2100 | 0.0203 | 0.9552 | 0.9507 | 0.9529 | 0.9922 |
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+ | 0.01 | 0.57 | 2400 | 0.0049 | 0.9946 | 0.9925 | 0.9935 | 0.9989 |
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+ | 0.0144 | 0.64 | 2700 | 0.0056 | 0.9871 | 0.9944 | 0.9907 | 0.9984 |
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+ | 0.0058 | 0.72 | 3000 | 0.0051 | 0.9928 | 0.9930 | 0.9929 | 0.9988 |
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+ | 0.0058 | 0.79 | 3300 | 0.0036 | 0.9969 | 0.9920 | 0.9945 | 0.9991 |
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+ | 0.0048 | 0.86 | 3600 | 0.0047 | 0.9930 | 0.9947 | 0.9938 | 0.9990 |
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+ | 0.0048 | 0.93 | 3900 | 0.0053 | 0.9863 | 0.9965 | 0.9914 | 0.9985 |
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+ | 0.0052 | 1.0 | 4200 | 0.0033 | 0.9985 | 0.9909 | 0.9947 | 0.9991 |
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+ | 0.0029 | 1.07 | 4500 | 0.0039 | 0.9938 | 0.9954 | 0.9946 | 0.9991 |
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+ | 0.0029 | 1.14 | 4800 | 0.0038 | 0.9981 | 0.9906 | 0.9943 | 0.9991 |
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+ | 0.0034 | 1.22 | 5100 | 0.0044 | 0.9937 | 0.9934 | 0.9936 | 0.9989 |
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+ | 0.0034 | 1.29 | 5400 | 0.0040 | 0.9884 | 0.9959 | 0.9921 | 0.9987 |
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+ | 0.0027 | 1.36 | 5700 | 0.0040 | 0.9975 | 0.9910 | 0.9942 | 0.9990 |
 
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  ### Framework versions