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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: microsoft/layoutlmv3-base |
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tags: |
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- generated_from_trainer |
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- layoutmlv3 |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: layoutlmv3-finetuned-passport |
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results: [] |
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datasets: |
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- EphronM/Annotated_passport_images |
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language: |
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- en |
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pipeline_tag: token-classification |
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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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# layoutlmv3-finetuned-passport |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co./microsoft/layoutlmv3-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0655 |
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- Precision: 0.9735 |
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- Recall: 0.9847 |
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- F1: 0.9790 |
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- Accuracy: 0.9892 |
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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: 1e-05 |
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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: 4000 |
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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 | 3.4483 | 100 | 0.6286 | 0.7930 | 0.7778 | 0.7853 | 0.8898 | |
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| No log | 6.8966 | 200 | 0.1945 | 0.9423 | 0.9387 | 0.9405 | 0.9719 | |
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| No log | 10.3448 | 300 | 0.0832 | 0.9730 | 0.9655 | 0.9692 | 0.9870 | |
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| No log | 13.7931 | 400 | 0.0558 | 0.9660 | 0.9808 | 0.9734 | 0.9870 | |
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| 0.398 | 17.2414 | 500 | 0.0524 | 0.9624 | 0.9808 | 0.9715 | 0.9870 | |
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| 0.398 | 20.6897 | 600 | 0.0462 | 0.9735 | 0.9847 | 0.9790 | 0.9892 | |
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| 0.398 | 24.1379 | 700 | 0.0543 | 0.9660 | 0.9808 | 0.9734 | 0.9870 | |
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| 0.398 | 27.5862 | 800 | 0.0463 | 0.9735 | 0.9847 | 0.9790 | 0.9892 | |
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| 0.398 | 31.0345 | 900 | 0.0569 | 0.9624 | 0.9808 | 0.9715 | 0.9870 | |
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| 0.0139 | 34.4828 | 1000 | 0.0729 | 0.9515 | 0.9770 | 0.9641 | 0.9849 | |
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| 0.0139 | 37.9310 | 1100 | 0.0656 | 0.9624 | 0.9808 | 0.9715 | 0.9870 | |
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| 0.0139 | 41.3793 | 1200 | 0.0609 | 0.9624 | 0.9808 | 0.9715 | 0.9870 | |
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| 0.0139 | 44.8276 | 1300 | 0.0525 | 0.9732 | 0.9732 | 0.9732 | 0.9892 | |
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| 0.0139 | 48.2759 | 1400 | 0.0735 | 0.9515 | 0.9770 | 0.9641 | 0.9849 | |
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| 0.0072 | 51.7241 | 1500 | 0.0491 | 0.9547 | 0.9693 | 0.9620 | 0.9870 | |
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| 0.0072 | 55.1724 | 1600 | 0.0416 | 0.9773 | 0.9885 | 0.9829 | 0.9914 | |
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| 0.0072 | 58.6207 | 1700 | 0.0472 | 0.9773 | 0.9885 | 0.9829 | 0.9914 | |
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| 0.0072 | 62.0690 | 1800 | 0.0543 | 0.9735 | 0.9847 | 0.9790 | 0.9892 | |
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| 0.0072 | 65.5172 | 1900 | 0.0619 | 0.9662 | 0.9847 | 0.9753 | 0.9892 | |
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| 0.0029 | 68.9655 | 2000 | 0.0670 | 0.9624 | 0.9808 | 0.9715 | 0.9870 | |
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| 0.0029 | 72.4138 | 2100 | 0.0770 | 0.9624 | 0.9808 | 0.9715 | 0.9870 | |
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| 0.0029 | 75.8621 | 2200 | 0.0700 | 0.9624 | 0.9808 | 0.9715 | 0.9870 | |
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| 0.0029 | 79.3103 | 2300 | 0.0655 | 0.9624 | 0.9808 | 0.9715 | 0.9870 | |
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| 0.0029 | 82.7586 | 2400 | 0.0684 | 0.9624 | 0.9808 | 0.9715 | 0.9870 | |
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| 0.0012 | 86.2069 | 2500 | 0.0700 | 0.9624 | 0.9808 | 0.9715 | 0.9870 | |
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| 0.0012 | 89.6552 | 2600 | 0.0696 | 0.9624 | 0.9808 | 0.9715 | 0.9870 | |
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| 0.0012 | 93.1034 | 2700 | 0.0619 | 0.9735 | 0.9847 | 0.9790 | 0.9892 | |
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| 0.0012 | 96.5517 | 2800 | 0.0630 | 0.9735 | 0.9847 | 0.9790 | 0.9892 | |
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| 0.0012 | 100.0 | 2900 | 0.0703 | 0.9733 | 0.9770 | 0.9751 | 0.9892 | |
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| 0.0009 | 103.4483 | 3000 | 0.0655 | 0.9735 | 0.9847 | 0.9790 | 0.9892 | |
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| 0.0009 | 106.8966 | 3100 | 0.0653 | 0.9735 | 0.9847 | 0.9790 | 0.9892 | |
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| 0.0009 | 110.3448 | 3200 | 0.0657 | 0.9735 | 0.9847 | 0.9790 | 0.9892 | |
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| 0.0009 | 113.7931 | 3300 | 0.0660 | 0.9735 | 0.9847 | 0.9790 | 0.9892 | |
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| 0.0009 | 117.2414 | 3400 | 0.0655 | 0.9735 | 0.9847 | 0.9790 | 0.9892 | |
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| 0.0008 | 120.6897 | 3500 | 0.0663 | 0.9735 | 0.9847 | 0.9790 | 0.9892 | |
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| 0.0008 | 124.1379 | 3600 | 0.0663 | 0.9735 | 0.9847 | 0.9790 | 0.9892 | |
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| 0.0008 | 127.5862 | 3700 | 0.0666 | 0.9735 | 0.9847 | 0.9790 | 0.9892 | |
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| 0.0008 | 131.0345 | 3800 | 0.0648 | 0.9735 | 0.9847 | 0.9790 | 0.9892 | |
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| 0.0008 | 134.4828 | 3900 | 0.0660 | 0.9735 | 0.9847 | 0.9790 | 0.9892 | |
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| 0.0009 | 137.9310 | 4000 | 0.0660 | 0.9735 | 0.9847 | 0.9790 | 0.9892 | |
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### Framework versions |
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- Transformers 4.44.0.dev0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
