Model save

README.md

@@ -0,0 +1,86 @@
+---
+license: mit
+base_model: microsoft/layoutlm-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- blumatix_dataset
+model-index:
+- name: layoutlm-blumatix
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# layoutlm-blumatix
+
+This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the blumatix_dataset dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3906
+- At Table Summary: {'precision': 0.8, 'recall': 1.0, 'f1': 0.888888888888889, 'number': 8}
+- Aymentinformation: {'precision': 0.75, 'recall': 0.6923076923076923, 'f1': 0.7199999999999999, 'number': 13}
+- Eader: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}
+- Ineitemtable: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}
+- Nvoicedetails: {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20}
+- Ogo: {'precision': 0.6363636363636364, 'recall': 0.7, 'f1': 0.6666666666666666, 'number': 10}
+- Ontact: {'precision': 0.6842105263157895, 'recall': 0.8125, 'f1': 0.742857142857143, 'number': 16}
+- Ooter: {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10}
+- Overall Precision: 0.82
+- Overall Recall: 0.8454
+- Overall F1: 0.8325
+- Overall Accuracy: 0.8704
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 15
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | At Table Summary                                                                          | Aymentinformation                                                                                       | Eader                                                                     | Ineitemtable                                                                             | Nvoicedetails                                                                             | Ogo                                                                                      | Ontact                                                                                      | Ooter                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.88          | 1.0   | 7    | 1.5813          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                                 | {'precision': 0.42857142857142855, 'recall': 0.23076923076923078, 'f1': 0.3, 'number': 13}              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                               | {'precision': 0.13333333333333333, 'recall': 0.2, 'f1': 0.16, 'number': 20}               | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                               | {'precision': 0.23076923076923078, 'recall': 0.375, 'f1': 0.2857142857142857, 'number': 16} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                               | 0.2063            | 0.1340         | 0.1625     | 0.4259           |
+| 1.4414        | 2.0   | 14   | 1.1408          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                                 | {'precision': 0.4, 'recall': 0.46153846153846156, 'f1': 0.42857142857142855, 'number': 13}              | {'precision': 1.0, 'recall': 0.3, 'f1': 0.4615384615384615, 'number': 10} | {'precision': 1.0, 'recall': 0.4, 'f1': 0.5714285714285715, 'number': 10}                | {'precision': 0.52, 'recall': 0.65, 'f1': 0.5777777777777778, 'number': 20}               | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                               | {'precision': 0.4, 'recall': 0.625, 'f1': 0.48780487804878053, 'number': 16}                | {'precision': 0.625, 'recall': 0.5, 'f1': 0.5555555555555556, 'number': 10}              | 0.5125            | 0.4227         | 0.4633     | 0.5833           |
+| 1.144         | 3.0   | 21   | 0.8586          | {'precision': 1.0, 'recall': 0.625, 'f1': 0.7692307692307693, 'number': 8}                | {'precision': 0.5714285714285714, 'recall': 0.6153846153846154, 'f1': 0.5925925925925927, 'number': 13} | {'precision': 1.0, 'recall': 0.9, 'f1': 0.9473684210526316, 'number': 10} | {'precision': 1.0, 'recall': 0.7, 'f1': 0.8235294117647058, 'number': 10}                | {'precision': 0.7368421052631579, 'recall': 0.7, 'f1': 0.717948717948718, 'number': 20}   | {'precision': 0.75, 'recall': 0.3, 'f1': 0.4285714285714285, 'number': 10}               | {'precision': 0.5454545454545454, 'recall': 0.75, 'f1': 0.631578947368421, 'number': 16}    | {'precision': 0.7, 'recall': 0.7, 'f1': 0.7, 'number': 10}                               | 0.7222            | 0.6701         | 0.6952     | 0.7685           |
+| 0.8948        | 4.0   | 28   | 0.6937          | {'precision': 0.8333333333333334, 'recall': 0.625, 'f1': 0.7142857142857143, 'number': 8} | {'precision': 0.6923076923076923, 'recall': 0.6923076923076923, 'f1': 0.6923076923076923, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                | {'precision': 1.0, 'recall': 0.6, 'f1': 0.7499999999999999, 'number': 10}                | {'precision': 0.7894736842105263, 'recall': 0.75, 'f1': 0.7692307692307692, 'number': 20} | {'precision': 0.5, 'recall': 0.3, 'f1': 0.37499999999999994, 'number': 10}               | {'precision': 0.55, 'recall': 0.6875, 'f1': 0.6111111111111112, 'number': 16}               | {'precision': 0.6363636363636364, 'recall': 0.7, 'f1': 0.6666666666666666, 'number': 10} | 0.7253            | 0.6804         | 0.7021     | 0.7870           |
+| 0.7146        | 5.0   | 35   | 0.5632          | {'precision': 0.7777777777777778, 'recall': 0.875, 'f1': 0.823529411764706, 'number': 8}  | {'precision': 0.75, 'recall': 0.6923076923076923, 'f1': 0.7199999999999999, 'number': 13}               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                | {'precision': 1.0, 'recall': 0.9, 'f1': 0.9473684210526316, 'number': 10}                | {'precision': 0.8947368421052632, 'recall': 0.85, 'f1': 0.8717948717948718, 'number': 20} | {'precision': 0.7, 'recall': 0.7, 'f1': 0.7, 'number': 10}                               | {'precision': 0.7647058823529411, 'recall': 0.8125, 'f1': 0.787878787878788, 'number': 16}  | {'precision': 0.7, 'recall': 0.7, 'f1': 0.7, 'number': 10}                               | 0.8229            | 0.8144         | 0.8187     | 0.8611           |
+| 0.6475        | 6.0   | 42   | 0.5030          | {'precision': 0.6666666666666666, 'recall': 0.75, 'f1': 0.7058823529411765, 'number': 8}  | {'precision': 0.75, 'recall': 0.6923076923076923, 'f1': 0.7199999999999999, 'number': 13}               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                | {'precision': 1.0, 'recall': 0.7, 'f1': 0.8235294117647058, 'number': 10}                | {'precision': 0.8421052631578947, 'recall': 0.8, 'f1': 0.8205128205128205, 'number': 20}  | {'precision': 0.7, 'recall': 0.7, 'f1': 0.7, 'number': 10}                               | {'precision': 0.7647058823529411, 'recall': 0.8125, 'f1': 0.787878787878788, 'number': 16}  | {'precision': 0.7, 'recall': 0.7, 'f1': 0.7, 'number': 10}                               | 0.7979            | 0.7732         | 0.7853     | 0.8426           |
+| 0.5697        | 7.0   | 49   | 0.4463          | {'precision': 0.7777777777777778, 'recall': 0.875, 'f1': 0.823529411764706, 'number': 8}  | {'precision': 0.75, 'recall': 0.6923076923076923, 'f1': 0.7199999999999999, 'number': 13}               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                | {'precision': 0.8888888888888888, 'recall': 0.8, 'f1': 0.8421052631578948, 'number': 10} | {'precision': 0.8947368421052632, 'recall': 0.85, 'f1': 0.8717948717948718, 'number': 20} | {'precision': 0.7, 'recall': 0.7, 'f1': 0.7, 'number': 10}                               | {'precision': 0.7647058823529411, 'recall': 0.8125, 'f1': 0.787878787878788, 'number': 16}  | {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10} | 0.8211            | 0.8041         | 0.8125     | 0.8611           |
+| 0.4919        | 8.0   | 56   | 0.4412          | {'precision': 0.6666666666666666, 'recall': 0.75, 'f1': 0.7058823529411765, 'number': 8}  | {'precision': 0.6923076923076923, 'recall': 0.6923076923076923, 'f1': 0.6923076923076923, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                | {'precision': 1.0, 'recall': 0.9, 'f1': 0.9473684210526316, 'number': 10}                | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20}  | {'precision': 0.7, 'recall': 0.7, 'f1': 0.7, 'number': 10}                               | {'precision': 0.6842105263157895, 'recall': 0.8125, 'f1': 0.742857142857143, 'number': 16}  | {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10} | 0.8061            | 0.8144         | 0.8103     | 0.8426           |
+| 0.4344        | 9.0   | 63   | 0.4189          | {'precision': 0.7, 'recall': 0.875, 'f1': 0.7777777777777777, 'number': 8}                | {'precision': 0.8181818181818182, 'recall': 0.6923076923076923, 'f1': 0.7500000000000001, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                | {'precision': 1.0, 'recall': 0.9, 'f1': 0.9473684210526316, 'number': 10}                | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20}  | {'precision': 0.6363636363636364, 'recall': 0.7, 'f1': 0.6666666666666666, 'number': 10} | {'precision': 0.6842105263157895, 'recall': 0.8125, 'f1': 0.742857142857143, 'number': 16}  | {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10} | 0.8163            | 0.8247         | 0.8205     | 0.8704           |
+| 0.4855        | 10.0  | 70   | 0.4099          | {'precision': 0.7272727272727273, 'recall': 1.0, 'f1': 0.8421052631578948, 'number': 8}   | {'precision': 0.7272727272727273, 'recall': 0.6153846153846154, 'f1': 0.6666666666666667, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                               | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20}  | {'precision': 0.6363636363636364, 'recall': 0.7, 'f1': 0.6666666666666666, 'number': 10} | {'precision': 0.7222222222222222, 'recall': 0.8125, 'f1': 0.7647058823529411, 'number': 16} | {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10} | 0.8182            | 0.8351         | 0.8265     | 0.8704           |
+| 0.482         | 11.0  | 77   | 0.3974          | {'precision': 0.8, 'recall': 1.0, 'f1': 0.888888888888889, 'number': 8}                   | {'precision': 0.75, 'recall': 0.6923076923076923, 'f1': 0.7199999999999999, 'number': 13}               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                               | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20}  | {'precision': 0.6363636363636364, 'recall': 0.7, 'f1': 0.6666666666666666, 'number': 10} | {'precision': 0.6842105263157895, 'recall': 0.8125, 'f1': 0.742857142857143, 'number': 16}  | {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10} | 0.82              | 0.8454         | 0.8325     | 0.8704           |
+| 0.3704        | 12.0  | 84   | 0.3928          | {'precision': 0.8, 'recall': 1.0, 'f1': 0.888888888888889, 'number': 8}                   | {'precision': 0.75, 'recall': 0.6923076923076923, 'f1': 0.7199999999999999, 'number': 13}               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                               | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20}  | {'precision': 0.6363636363636364, 'recall': 0.7, 'f1': 0.6666666666666666, 'number': 10} | {'precision': 0.7222222222222222, 'recall': 0.8125, 'f1': 0.7647058823529411, 'number': 16} | {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10} | 0.8283            | 0.8454         | 0.8367     | 0.8796           |
+| 0.3888        | 13.0  | 91   | 0.3838          | {'precision': 0.8, 'recall': 1.0, 'f1': 0.888888888888889, 'number': 8}                   | {'precision': 0.75, 'recall': 0.6923076923076923, 'f1': 0.7199999999999999, 'number': 13}               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                               | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20}  | {'precision': 0.6363636363636364, 'recall': 0.7, 'f1': 0.6666666666666666, 'number': 10} | {'precision': 0.7222222222222222, 'recall': 0.8125, 'f1': 0.7647058823529411, 'number': 16} | {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10} | 0.8283            | 0.8454         | 0.8367     | 0.8796           |
+| 0.3754        | 14.0  | 98   | 0.3889          | {'precision': 0.8, 'recall': 1.0, 'f1': 0.888888888888889, 'number': 8}                   | {'precision': 0.75, 'recall': 0.6923076923076923, 'f1': 0.7199999999999999, 'number': 13}               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                               | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20}  | {'precision': 0.6363636363636364, 'recall': 0.7, 'f1': 0.6666666666666666, 'number': 10} | {'precision': 0.6842105263157895, 'recall': 0.8125, 'f1': 0.742857142857143, 'number': 16}  | {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10} | 0.82              | 0.8454         | 0.8325     | 0.8704           |
+| 0.3666        | 15.0  | 105  | 0.3906          | {'precision': 0.8, 'recall': 1.0, 'f1': 0.888888888888889, 'number': 8}                   | {'precision': 0.75, 'recall': 0.6923076923076923, 'f1': 0.7199999999999999, 'number': 13}               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                               | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20}  | {'precision': 0.6363636363636364, 'recall': 0.7, 'f1': 0.6666666666666666, 'number': 10} | {'precision': 0.6842105263157895, 'recall': 0.8125, 'f1': 0.742857142857143, 'number': 16}  | {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10} | 0.82              | 0.8454         | 0.8325     | 0.8704           |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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preprocessor_config.json

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+}