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layoutlm-mcocr

Token Classification
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Safetensors
layoutlm
Generated from Trainer
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Files changed (8) hide show
  1. README.md +81 -0
  2. logs/events.out.tfevents.1733254332.63d9064179a3.23.3 +2 -2
  3. model.safetensors +1 -1
  4. preprocessor_config.json +13 -0
  5. special_tokens_map.json +37 -0
  6. tokenizer.json +0 -0
  7. tokenizer_config.json +80 -0
  8. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: mit
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+ base_model: microsoft/layoutlm-base-uncased
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: layoutlm-mcocr
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+ results: []
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+ ---
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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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+
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+ # layoutlm-mcocr
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+
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+ This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1783
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+ - Ddress: {'precision': 0.9259259259259259, 'recall': 0.9259259259259259, 'f1': 0.9259259259259259, 'number': 54}
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+ - Eller: {'precision': 0.9464285714285714, 'recall': 0.9636363636363636, 'f1': 0.9549549549549549, 'number': 55}
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+ - Imestamp: {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54}
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+ - Otal Cost: {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55}
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+ - Overall Precision: 0.9585
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+ - Overall Recall: 0.9541
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+ - Overall F1: 0.9563
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+ - Overall Accuracy: 0.9787
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 3e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 15
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Ddress | Eller | Imestamp | Otal Cost | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 0.0001 | 1.0 | 7 | 0.1773 | {'precision': 0.9454545454545454, 'recall': 0.9629629629629629, 'f1': 0.9541284403669724, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | 0.9767 | 0.9633 | 0.9700 | 0.9817 |
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+ | 0.0 | 2.0 | 14 | 0.1568 | {'precision': 0.9454545454545454, 'recall': 0.9629629629629629, 'f1': 0.9541284403669724, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | 0.9767 | 0.9633 | 0.9700 | 0.9817 |
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+ | 0.0 | 3.0 | 21 | 0.1664 | {'precision': 0.9454545454545454, 'recall': 0.9629629629629629, 'f1': 0.9541284403669724, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | 0.9767 | 0.9633 | 0.9700 | 0.9817 |
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+ | 0.0 | 4.0 | 28 | 0.1649 | {'precision': 0.9454545454545454, 'recall': 0.9629629629629629, 'f1': 0.9541284403669724, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | 0.9767 | 0.9633 | 0.9700 | 0.9817 |
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+ | 0.0 | 5.0 | 35 | 0.1713 | {'precision': 0.9454545454545454, 'recall': 0.9629629629629629, 'f1': 0.9541284403669724, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | 0.9767 | 0.9633 | 0.9700 | 0.9817 |
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+ | 0.0 | 6.0 | 42 | 0.1678 | {'precision': 0.9454545454545454, 'recall': 0.9629629629629629, 'f1': 0.9541284403669724, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9722 | 0.9633 | 0.9677 | 0.9817 |
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+ | 0.0 | 7.0 | 49 | 0.1669 | {'precision': 0.9454545454545454, 'recall': 0.9629629629629629, 'f1': 0.9541284403669724, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9722 | 0.9633 | 0.9677 | 0.9817 |
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+ | 0.0 | 8.0 | 56 | 0.1690 | {'precision': 0.9454545454545454, 'recall': 0.9629629629629629, 'f1': 0.9541284403669724, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9722 | 0.9633 | 0.9677 | 0.9817 |
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+ | 0.0 | 9.0 | 63 | 0.1686 | {'precision': 0.9629629629629629, 'recall': 0.9629629629629629, 'f1': 0.9629629629629629, 'number': 54} | {'precision': 0.9814814814814815, 'recall': 0.9636363636363636, 'f1': 0.9724770642201834, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9767 | 0.9633 | 0.9700 | 0.9848 |
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+ | 0.0 | 10.0 | 70 | 0.1718 | {'precision': 0.9444444444444444, 'recall': 0.9444444444444444, 'f1': 0.9444444444444444, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9676 | 0.9587 | 0.9631 | 0.9817 |
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+ | 0.0 | 11.0 | 77 | 0.1893 | {'precision': 0.9259259259259259, 'recall': 0.9259259259259259, 'f1': 0.9259259259259259, 'number': 54} | {'precision': 0.9464285714285714, 'recall': 0.9636363636363636, 'f1': 0.9549549549549549, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9585 | 0.9541 | 0.9563 | 0.9787 |
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+ | 0.0 | 12.0 | 84 | 0.1943 | {'precision': 0.9259259259259259, 'recall': 0.9259259259259259, 'f1': 0.9259259259259259, 'number': 54} | {'precision': 0.9464285714285714, 'recall': 0.9636363636363636, 'f1': 0.9549549549549549, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9585 | 0.9541 | 0.9563 | 0.9787 |
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+ | 0.0 | 13.0 | 91 | 0.1914 | {'precision': 0.9259259259259259, 'recall': 0.9259259259259259, 'f1': 0.9259259259259259, 'number': 54} | {'precision': 0.9464285714285714, 'recall': 0.9636363636363636, 'f1': 0.9549549549549549, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9585 | 0.9541 | 0.9563 | 0.9787 |
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+ | 0.0 | 14.0 | 98 | 0.1835 | {'precision': 0.9259259259259259, 'recall': 0.9259259259259259, 'f1': 0.9259259259259259, 'number': 54} | {'precision': 0.9464285714285714, 'recall': 0.9636363636363636, 'f1': 0.9549549549549549, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9585 | 0.9541 | 0.9563 | 0.9787 |
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+ | 0.0 | 15.0 | 105 | 0.1783 | {'precision': 0.9259259259259259, 'recall': 0.9259259259259259, 'f1': 0.9259259259259259, 'number': 54} | {'precision': 0.9464285714285714, 'recall': 0.9636363636363636, 'f1': 0.9549549549549549, 'number': 55} | {'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54} | {'precision': 0.9636363636363636, 'recall': 0.9636363636363636, 'f1': 0.9636363636363636, 'number': 55} | 0.9585 | 0.9541 | 0.9563 | 0.9787 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.46.3
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+ - Pytorch 2.4.0
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+ - Datasets 3.1.0
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+ - Tokenizers 0.20.3
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+ {
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vocab.txt ADDED
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