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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: 0.0084 |
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- Ebegin: {'precision': 0.9916415914409896, 'recall': 0.9880079946702198, 'f1': 0.9898214583680961, 'number': 3002} |
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- Eend: {'precision': 0.9879919946631087, 'recall': 0.9873333333333333, 'f1': 0.9876625541847281, 'number': 3000} |
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- Overall Precision: 0.9898 |
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- Overall Recall: 0.9877 |
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- Overall F1: 0.9887 |
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- Overall Accuracy: 0.9982 |
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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: 6000 |
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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 | 0.0364 | 0.9697 | 0.9694 | 0.9695 | 0.9949 | |
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| 0.1737 | 0.14 | 600 | 0.0146 | 0.9849 | 0.9880 | 0.9865 | 0.9977 | |
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| 0.1737 | 0.21 | 900 | 0.0092 | 0.9835 | 0.9929 | 0.9881 | 0.9980 | |
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| 0.0158 | 0.29 | 1200 | 0.0074 | 0.9904 | 0.9912 | 0.9908 | 0.9984 | |
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| 0.0098 | 0.36 | 1500 | 0.0058 | 0.9866 | 0.9943 | 0.9904 | 0.9984 | |
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| 0.0098 | 0.43 | 1800 | 0.0075 | 0.9883 | 0.9898 | 0.9890 | 0.9982 | |
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| 0.0073 | 0.5 | 2100 | 0.0068 | 0.9962 | 0.9815 | 0.9888 | 0.9981 | |
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| 0.0073 | 0.57 | 2400 | 0.0065 | 0.9899 | 0.9900 | 0.9899 | 0.9983 | |
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### Framework versions |
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- Transformers 4.26.0 |
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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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