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HueyNemud
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icdar23-entrydetector_plaintext_breaks_indents_left_diff

Token Classification
Transformers
PyTorch
camembert
Generated from Trainer
Inference Endpoints
Model card Files Community
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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: 0.0058
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- - Ebegin: {'precision': 0.9973404255319149, 'recall': 0.9872132380594209, 'f1': 0.9922509922509923, 'number': 2659}
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- - Eend: {'precision': 0.9950924877312193, 'recall': 0.9850523168908819, 'f1': 0.9900469483568075, 'number': 2676}
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- - Overall Precision: 0.9962
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- - Overall Recall: 0.9861
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- - Overall F1: 0.9911
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- - Overall Accuracy: 0.9985
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  ## Model description
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@@ -50,18 +50,26 @@ 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 | 0.0372 | 0.9749 | 0.9785 | 0.9767 | 0.9961 |
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- | 0.186 | 0.14 | 600 | 0.0149 | 0.9920 | 0.9876 | 0.9898 | 0.9981 |
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- | 0.186 | 0.21 | 900 | 0.0093 | 0.9901 | 0.9896 | 0.9898 | 0.9982 |
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- | 0.0196 | 0.29 | 1200 | 0.0109 | 0.9830 | 0.9937 | 0.9883 | 0.9979 |
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- | 0.0101 | 0.36 | 1500 | 0.0091 | 0.9877 | 0.9926 | 0.9901 | 0.9982 |
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- | 0.0101 | 0.43 | 1800 | 0.0095 | 0.9953 | 0.9820 | 0.9886 | 0.9980 |
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- | 0.0108 | 0.5 | 2100 | 0.0055 | 0.9947 | 0.9922 | 0.9935 | 0.9988 |
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- | 0.0108 | 0.57 | 2400 | 0.0052 | 0.9932 | 0.9928 | 0.9930 | 0.9988 |
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- | 0.008 | 0.64 | 2700 | 0.0054 | 0.9906 | 0.9900 | 0.9903 | 0.9983 |
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- | 0.0064 | 0.72 | 3000 | 0.0066 | 0.9953 | 0.9911 | 0.9932 | 0.9988 |
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- | 0.0064 | 0.79 | 3300 | 0.0093 | 0.9903 | 0.9838 | 0.9870 | 0.9977 |
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- | 0.0095 | 0.86 | 3600 | 0.0092 | 0.9899 | 0.9863 | 0.9881 | 0.9978 |
 
 
 
 
 
 
 
 
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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: 0.0072
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+ - Ebegin: {'precision': 0.9935508345978755, 'recall': 0.9849567506581421, 'f1': 0.9892351274787535, 'number': 2659}
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+ - Eend: {'precision': 0.9980857580398163, 'recall': 0.9742152466367713, 'f1': 0.9860060514372163, 'number': 2676}
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+ - Overall Precision: 0.9958
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+ - Overall Recall: 0.9796
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+ - Overall F1: 0.9876
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+ - Overall Accuracy: 0.9980
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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 | 0.9593 | 0.9879 | 0.9734 | 0.9955 |
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+ | 0.161 | 0.14 | 600 | 0.0126 | 0.9890 | 0.9911 | 0.9900 | 0.9982 |
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+ | 0.161 | 0.21 | 900 | 0.0116 | 0.9730 | 0.9894 | 0.9811 | 0.9971 |
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+ | 0.0165 | 0.29 | 1200 | 0.0087 | 0.9938 | 0.9918 | 0.9928 | 0.9987 |
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+ | 0.0119 | 0.36 | 1500 | 0.0093 | 0.9851 | 0.9937 | 0.9894 | 0.9981 |
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+ | 0.0119 | 0.43 | 1800 | 0.0055 | 0.9942 | 0.9913 | 0.9928 | 0.9987 |
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+ | 0.0091 | 0.5 | 2100 | 0.0057 | 0.9951 | 0.9904 | 0.9928 | 0.9987 |
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+ | 0.0091 | 0.57 | 2400 | 0.0058 | 0.9920 | 0.9936 | 0.9928 | 0.9987 |
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+ | 0.0083 | 0.64 | 2700 | 0.0059 | 0.9896 | 0.9918 | 0.9907 | 0.9983 |
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+ | 0.0065 | 0.72 | 3000 | 0.0045 | 0.9968 | 0.9917 | 0.9942 | 0.9990 |
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+ | 0.0065 | 0.79 | 3300 | 0.0047 | 0.9920 | 0.9937 | 0.9929 | 0.9987 |
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+ | 0.0054 | 0.86 | 3600 | 0.0050 | 0.9945 | 0.9909 | 0.9926 | 0.9987 |
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+ | 0.0054 | 0.93 | 3900 | 0.0064 | 0.9838 | 0.9968 | 0.9903 | 0.9983 |
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+ | 0.0056 | 1.0 | 4200 | 0.0046 | 0.9971 | 0.9920 | 0.9946 | 0.9990 |
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+ | 0.0034 | 1.07 | 4500 | 0.0037 | 0.9959 | 0.9936 | 0.9948 | 0.9990 |
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+ | 0.0034 | 1.14 | 4800 | 0.0047 | 0.9983 | 0.9900 | 0.9941 | 0.9989 |
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+ | 0.0035 | 1.22 | 5100 | 0.0043 | 0.9936 | 0.9951 | 0.9944 | 0.9990 |
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+ | 0.0035 | 1.29 | 5400 | 0.0061 | 0.9892 | 0.9957 | 0.9925 | 0.9986 |
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+ | 0.002 | 1.36 | 5700 | 0.0057 | 0.9898 | 0.9947 | 0.9923 | 0.9986 |
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+ | 0.0048 | 1.43 | 6000 | 0.0042 | 0.9954 | 0.9933 | 0.9944 | 0.9990 |
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  ### Framework versions