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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: 0.0120 |
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- Ebegin: {'precision': 0.9901997738409348, 'recall': 0.9879654005265137, 'f1': 0.9890813253012049, 'number': 2659} |
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- Eend: {'precision': 0.9916824196597354, 'recall': 0.9801943198804185, 'f1': 0.9859049050930276, 'number': 2676} |
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- Overall Precision: 0.9909 |
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- Overall Recall: 0.9841 |
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- Overall F1: 0.9875 |
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- Overall Accuracy: 0.9977 |
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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 | 0.0318 | 0.9847 | 0.9761 | 0.9803 | 0.9966 | |
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| 0.1683 | 0.14 | 600 | 0.0164 | 0.9878 | 0.9890 | 0.9884 | 0.9978 | |
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| 0.1683 | 0.21 | 900 | 0.0146 | 0.9900 | 0.9853 | 0.9876 | 0.9976 | |
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| 0.0203 | 0.29 | 1200 | 0.0112 | 0.9862 | 0.9902 | 0.9882 | 0.9978 | |
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| 0.0123 | 0.36 | 1500 | 0.0089 | 0.9943 | 0.9878 | 0.9910 | 0.9983 | |
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| 0.0123 | 0.43 | 1800 | 0.0139 | 0.9970 | 0.9814 | 0.9891 | 0.9979 | |
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| 0.0109 | 0.5 | 2100 | 0.0101 | 0.9937 | 0.9882 | 0.9909 | 0.9982 | |
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| 0.0109 | 0.57 | 2400 | 0.0087 | 0.9949 | 0.9896 | 0.9922 | 0.9985 | |
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| 0.0092 | 0.64 | 2700 | 0.0081 | 0.9849 | 0.9919 | 0.9884 | 0.9978 | |
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| 0.0084 | 0.72 | 3000 | 0.0087 | 0.9937 | 0.9867 | 0.9902 | 0.9981 | |
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| 0.0084 | 0.79 | 3300 | 0.0089 | 0.9915 | 0.9889 | 0.9902 | 0.9981 | |
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| 0.0069 | 0.86 | 3600 | 0.0092 | 0.9899 | 0.9901 | 0.9900 | 0.9981 | |
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| 0.0069 | 0.93 | 3900 | 0.0097 | 0.9845 | 0.9915 | 0.9880 | 0.9977 | |
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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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