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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 |
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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 |
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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: 0.0424 |
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- Ebegin: {'precision': 0.9725125822686799, 'recall': 0.9447160586686725, 'f1': 0.9584128195345288, 'number': 2659} |
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- Eend: {'precision': 0.9570211189329382, 'recall': 0.9652466367713004, 'f1': 0.9611162790697675, 'number': 2676} |
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- Overall Precision: 0.9646 |
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- Overall Recall: 0.9550 |
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- Overall F1: 0.9598 |
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- Overall Accuracy: 0.9923 |
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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 | 0.0487 | 0.9874 | 0.9565 | 0.9717 | 0.9943 | |
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| 0.1698 | 0.14 | 600 | 0.0310 | 0.9891 | 0.9709 | 0.9799 | 0.9959 | |
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| 0.1698 | 0.21 | 900 | 0.0267 | 0.9746 | 0.9764 | 0.9755 | 0.9953 | |
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| 0.0346 | 0.29 | 1200 | 0.0217 | 0.9885 | 0.9685 | 0.9784 | 0.9956 | |
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| 0.0237 | 0.36 | 1500 | 0.0201 | 0.9866 | 0.9742 | 0.9804 | 0.9960 | |
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| 0.0237 | 0.43 | 1800 | 0.0268 | 0.9883 | 0.9561 | 0.9719 | 0.9944 | |
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| 0.0205 | 0.5 | 2100 | 0.0216 | 0.9823 | 0.9779 | 0.9801 | 0.9959 | |
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| 0.0205 | 0.57 | 2400 | 0.0236 | 0.9874 | 0.9700 | 0.9787 | 0.9957 | |
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| 0.0196 | 0.64 | 2700 | 0.0246 | 0.9877 | 0.9668 | 0.9772 | 0.9954 | |
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| 0.0195 | 0.72 | 3000 | 0.0254 | 0.9789 | 0.9682 | 0.9735 | 0.9950 | |
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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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