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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: icdar23-entrydetector_plaintext_breaks_indents_left_ref_right_ref |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# icdar23-entrydetector_plaintext_breaks_indents_left_ref_right_ref |
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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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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- training_steps: 7500 |
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### Training results |
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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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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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