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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 |
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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 |
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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: 0.0084 |
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- Ebegin: {'precision': 0.9988514548238897, 'recall': 0.9811959383226777, 'f1': 0.9899449819768545, 'number': 2659} |
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- Eend: {'precision': 0.9984726995036274, 'recall': 0.977204783258595, 'f1': 0.987724268177526, 'number': 2676} |
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- Overall Precision: 0.9987 |
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- Overall Recall: 0.9792 |
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- Overall F1: 0.9888 |
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- Overall Accuracy: 0.9980 |
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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 | 0.0367 | 0.9567 | 0.9818 | 0.9691 | 0.9948 | |
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| 0.1432 | 0.14 | 600 | 0.0181 | 0.9809 | 0.9811 | 0.9810 | 0.9971 | |
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| 0.1432 | 0.21 | 900 | 0.0111 | 0.9877 | 0.9920 | 0.9899 | 0.9981 | |
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| 0.0188 | 0.29 | 1200 | 0.0111 | 0.9955 | 0.9869 | 0.9912 | 0.9983 | |
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| 0.0121 | 0.36 | 1500 | 0.0094 | 0.9902 | 0.9899 | 0.9901 | 0.9981 | |
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| 0.0121 | 0.43 | 1800 | 0.0083 | 0.9914 | 0.9912 | 0.9913 | 0.9983 | |
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| 0.0106 | 0.5 | 2100 | 0.0078 | 0.9932 | 0.9902 | 0.9917 | 0.9984 | |
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| 0.0106 | 0.57 | 2400 | 0.0083 | 0.9906 | 0.9911 | 0.9909 | 0.9982 | |
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| 0.0105 | 0.64 | 2700 | 0.0083 | 0.9871 | 0.9927 | 0.9899 | 0.9981 | |
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| 0.0093 | 0.72 | 3000 | 0.0085 | 0.9938 | 0.9851 | 0.9894 | 0.9980 | |
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| 0.0093 | 0.79 | 3300 | 0.0075 | 0.9962 | 0.9879 | 0.9920 | 0.9985 | |
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| 0.0073 | 0.86 | 3600 | 0.0081 | 0.9927 | 0.9901 | 0.9914 | 0.9984 | |
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| 0.0073 | 0.93 | 3900 | 0.0083 | 0.9856 | 0.9923 | 0.9890 | 0.9980 | |
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| 0.0073 | 1.0 | 4200 | 0.0063 | 0.9936 | 0.9912 | 0.9924 | 0.9985 | |
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| 0.0041 | 1.07 | 4500 | 0.0063 | 0.9959 | 0.9902 | 0.9931 | 0.9987 | |
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| 0.0041 | 1.14 | 4800 | 0.0068 | 0.9948 | 0.9907 | 0.9928 | 0.9986 | |
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| 0.0048 | 1.22 | 5100 | 0.0074 | 0.9937 | 0.9905 | 0.9921 | 0.9985 | |
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| 0.0048 | 1.29 | 5400 | 0.0074 | 0.9912 | 0.9906 | 0.9909 | 0.9982 | |
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| 0.0043 | 1.36 | 5700 | 0.0070 | 0.9947 | 0.9907 | 0.9927 | 0.9986 | |
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| 0.0046 | 1.43 | 6000 | 0.0072 | 0.9948 | 0.9914 | 0.9931 | 0.9987 | |
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| 0.0046 | 1.5 | 6300 | 0.0080 | 0.9939 | 0.9915 | 0.9927 | 0.9986 | |
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| 0.0038 | 1.57 | 6600 | 0.0072 | 0.9939 | 0.9921 | 0.9930 | 0.9986 | |
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| 0.0038 | 1.65 | 6900 | 0.0061 | 0.9952 | 0.9916 | 0.9934 | 0.9987 | |
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| 0.0051 | 1.72 | 7200 | 0.0060 | 0.9959 | 0.9913 | 0.9936 | 0.9988 | |
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| 0.005 | 1.79 | 7500 | 0.0060 | 0.9959 | 0.9913 | 0.9936 | 0.9988 | |
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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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