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lmv2-g-w9-2018-148-doc-07-07_1

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+ ---
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+ license: cc-by-nc-sa-4.0
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: lmv2-g-w9-2018-148-doc-07-07_1
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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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+ # lmv2-g-w9-2018-148-doc-07-07_1
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+
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+ This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0160
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+ - Address Precision: 0.9667
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+ - Address Recall: 0.9667
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+ - Address F1: 0.9667
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+ - Address Number: 30
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+ - Business Name Precision: 1.0
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+ - Business Name Recall: 1.0
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+ - Business Name F1: 1.0
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+ - Business Name Number: 29
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+ - City State Zip Code Precision: 1.0
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+ - City State Zip Code Recall: 1.0
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+ - City State Zip Code F1: 1.0
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+ - City State Zip Code Number: 30
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+ - Ein Precision: 0.0
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+ - Ein Recall: 0.0
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+ - Ein F1: 0.0
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+ - Ein Number: 1
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+ - List Account Number Precision: 1.0
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+ - List Account Number Recall: 1.0
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+ - List Account Number F1: 1.0
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+ - List Account Number Number: 11
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+ - Name Precision: 1.0
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+ - Name Recall: 1.0
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+ - Name F1: 1.0
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+ - Name Number: 30
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+ - Ssn Precision: 0.8333
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+ - Ssn Recall: 1.0
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+ - Ssn F1: 0.9091
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+ - Ssn Number: 10
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+ - Overall Precision: 0.9789
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+ - Overall Recall: 0.9858
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+ - Overall F1: 0.9823
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+ - Overall Accuracy: 0.9995
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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: 4e-05
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+ - train_batch_size: 1
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+ - eval_batch_size: 1
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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: constant
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+ - num_epochs: 30
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Address Precision | Address Recall | Address F1 | Address Number | Business Name Precision | Business Name Recall | Business Name F1 | Business Name Number | City State Zip Code Precision | City State Zip Code Recall | City State Zip Code F1 | City State Zip Code Number | Ein Precision | Ein Recall | Ein F1 | Ein Number | List Account Number Precision | List Account Number Recall | List Account Number F1 | List Account Number Number | Name Precision | Name Recall | Name F1 | Name Number | Ssn Precision | Ssn Recall | Ssn F1 | Ssn Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:-------------:|:----------:|:------:|:----------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:--------------:|:-----------:|:-------:|:-----------:|:-------------:|:----------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 1.5672 | 1.0 | 118 | 1.1527 | 0.0 | 0.0 | 0.0 | 30 | 0.0 | 0.0 | 0.0 | 29 | 0.0 | 0.0 | 0.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 0.0 | 0.0 | 0.0 | 11 | 0.0 | 0.0 | 0.0 | 30 | 0.0 | 0.0 | 0.0 | 10 | 0.0 | 0.0 | 0.0 | 0.9642 |
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+ | 0.8804 | 2.0 | 236 | 0.5661 | 0.2095 | 0.7333 | 0.3259 | 30 | 0.0 | 0.0 | 0.0 | 29 | 0.0 | 0.0 | 0.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 0.0 | 0.0 | 0.0 | 11 | 0.0 | 0.0 | 0.0 | 30 | 0.0 | 0.0 | 0.0 | 10 | 0.2095 | 0.1560 | 0.1789 | 0.9704 |
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+ | 0.3739 | 3.0 | 354 | 0.2118 | 0.9375 | 1.0 | 0.9677 | 30 | 0.7143 | 0.1724 | 0.2778 | 29 | 0.9375 | 1.0 | 0.9677 | 30 | 0.0 | 0.0 | 0.0 | 1 | 0.8182 | 0.8182 | 0.8182 | 11 | 0.5 | 1.0 | 0.6667 | 30 | 0.75 | 0.9 | 0.8182 | 10 | 0.7338 | 0.8014 | 0.7661 | 0.9932 |
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+ | 0.1626 | 4.0 | 472 | 0.1155 | 0.9375 | 1.0 | 0.9677 | 30 | 0.8710 | 0.9310 | 0.9 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 0.6923 | 0.8182 | 0.7500 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.7 | 0.7 | 0.7 | 10 | 0.9110 | 0.9433 | 0.9268 | 0.9976 |
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+ | 0.1031 | 5.0 | 590 | 0.0817 | 0.9355 | 0.9667 | 0.9508 | 30 | 0.8125 | 0.8966 | 0.8525 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 0.6923 | 0.8182 | 0.7500 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.8182 | 0.9 | 0.8571 | 10 | 0.9048 | 0.9433 | 0.9236 | 0.9981 |
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+ | 0.0769 | 6.0 | 708 | 0.0634 | 0.9355 | 0.9667 | 0.9508 | 30 | 0.9333 | 0.9655 | 0.9492 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 0.6923 | 0.8182 | 0.7500 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.8182 | 0.9 | 0.8571 | 10 | 0.9310 | 0.9574 | 0.9441 | 0.9984 |
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+ | 0.0614 | 7.0 | 826 | 0.0518 | 0.9667 | 0.9667 | 0.9667 | 30 | 1.0 | 1.0 | 1.0 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 0.6923 | 0.8182 | 0.7500 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.8182 | 0.9 | 0.8571 | 10 | 0.9510 | 0.9645 | 0.9577 | 0.9991 |
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+ | 0.0509 | 8.0 | 944 | 0.0432 | 0.9667 | 0.9667 | 0.9667 | 30 | 1.0 | 1.0 | 1.0 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 0.8333 | 0.9091 | 0.8696 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.8182 | 0.9 | 0.8571 | 10 | 0.9648 | 0.9716 | 0.9682 | 0.9994 |
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+ | 0.0431 | 9.0 | 1062 | 0.0369 | 0.9667 | 0.9667 | 0.9667 | 30 | 1.0 | 1.0 | 1.0 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.8182 | 0.9 | 0.8571 | 10 | 0.9787 | 0.9787 | 0.9787 | 0.9994 |
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+ | 0.037 | 10.0 | 1180 | 0.0313 | 0.9667 | 0.9667 | 0.9667 | 30 | 1.0 | 1.0 | 1.0 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.8182 | 0.9 | 0.8571 | 10 | 0.9787 | 0.9787 | 0.9787 | 0.9994 |
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+ | 0.0328 | 11.0 | 1298 | 0.0281 | 0.9667 | 0.9667 | 0.9667 | 30 | 1.0 | 1.0 | 1.0 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.7143 | 1.0 | 0.8333 | 10 | 0.9653 | 0.9858 | 0.9754 | 0.9994 |
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+ | 0.0295 | 12.0 | 1416 | 0.0246 | 0.7429 | 0.8667 | 0.8 | 30 | 1.0 | 1.0 | 1.0 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.6667 | 0.8 | 0.7273 | 10 | 0.9116 | 0.9504 | 0.9306 | 0.9991 |
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+ | 0.0251 | 13.0 | 1534 | 0.0207 | 0.9677 | 1.0 | 0.9836 | 30 | 0.9333 | 0.9655 | 0.9492 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.8333 | 1.0 | 0.9091 | 10 | 0.9653 | 0.9858 | 0.9754 | 0.9994 |
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+ | 0.0231 | 14.0 | 1652 | 0.0210 | 0.9667 | 0.9667 | 0.9667 | 30 | 1.0 | 0.9655 | 0.9825 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.8333 | 1.0 | 0.9091 | 10 | 0.9787 | 0.9787 | 0.9787 | 0.9991 |
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+ | 0.0184 | 15.0 | 1770 | 0.0160 | 0.9667 | 0.9667 | 0.9667 | 30 | 1.0 | 1.0 | 1.0 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.8333 | 1.0 | 0.9091 | 10 | 0.9789 | 0.9858 | 0.9823 | 0.9995 |
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+ | 0.0162 | 16.0 | 1888 | 0.0142 | 0.9667 | 0.9667 | 0.9667 | 30 | 1.0 | 1.0 | 1.0 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.8333 | 1.0 | 0.9091 | 10 | 0.9789 | 0.9858 | 0.9823 | 0.9995 |
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+ | 0.0142 | 17.0 | 2006 | 0.0127 | 0.9667 | 0.9667 | 0.9667 | 30 | 1.0 | 1.0 | 1.0 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.8333 | 1.0 | 0.9091 | 10 | 0.9789 | 0.9858 | 0.9823 | 0.9995 |
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+ | 0.0123 | 18.0 | 2124 | 0.0114 | 0.9667 | 0.9667 | 0.9667 | 30 | 1.0 | 1.0 | 1.0 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.8333 | 1.0 | 0.9091 | 10 | 0.9789 | 0.9858 | 0.9823 | 0.9995 |
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+ | 0.0118 | 19.0 | 2242 | 0.0152 | 0.9677 | 1.0 | 0.9836 | 30 | 0.6765 | 0.7931 | 0.7302 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 0.8333 | 0.9091 | 0.8696 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.8182 | 0.9 | 0.8571 | 10 | 0.8859 | 0.9362 | 0.9103 | 0.9986 |
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+ | 0.0104 | 20.0 | 2360 | 0.0125 | 0.9677 | 1.0 | 0.9836 | 30 | 1.0 | 0.9655 | 0.9825 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.9091 | 1.0 | 0.9524 | 10 | 0.9789 | 0.9858 | 0.9823 | 0.9992 |
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+ | 0.0092 | 21.0 | 2478 | 0.0113 | 0.9677 | 1.0 | 0.9836 | 30 | 1.0 | 0.9655 | 0.9825 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.8333 | 1.0 | 0.9091 | 10 | 0.9653 | 0.9858 | 0.9754 | 0.9993 |
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+ | 0.0089 | 22.0 | 2596 | 0.0111 | 0.9677 | 1.0 | 0.9836 | 30 | 1.0 | 0.9655 | 0.9825 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.8333 | 1.0 | 0.9091 | 10 | 0.9789 | 0.9858 | 0.9823 | 0.9992 |
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+ | 0.0076 | 23.0 | 2714 | 0.0107 | 0.9677 | 1.0 | 0.9836 | 30 | 0.9310 | 0.9310 | 0.9310 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.8333 | 1.0 | 0.9091 | 10 | 0.9650 | 0.9787 | 0.9718 | 0.9991 |
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+ | 0.0074 | 24.0 | 2832 | 0.0105 | 0.9677 | 1.0 | 0.9836 | 30 | 0.9310 | 0.9310 | 0.9310 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.8182 | 0.9 | 0.8571 | 10 | 0.9514 | 0.9716 | 0.9614 | 0.9990 |
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+ | 0.007 | 25.0 | 2950 | 0.0092 | 0.9677 | 1.0 | 0.9836 | 30 | 1.0 | 0.9655 | 0.9825 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.7692 | 1.0 | 0.8696 | 10 | 0.9720 | 0.9858 | 0.9789 | 0.9991 |
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+ | 0.0062 | 26.0 | 3068 | 0.0061 | 0.9677 | 1.0 | 0.9836 | 30 | 1.0 | 1.0 | 1.0 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.7143 | 1.0 | 0.8333 | 10 | 0.9655 | 0.9929 | 0.9790 | 0.9994 |
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+ | 0.0057 | 27.0 | 3186 | 0.0056 | 0.9677 | 1.0 | 0.9836 | 30 | 1.0 | 1.0 | 1.0 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.8182 | 0.9 | 0.8571 | 10 | 0.9720 | 0.9858 | 0.9789 | 0.9995 |
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+ | 0.0047 | 28.0 | 3304 | 0.0054 | 0.9677 | 1.0 | 0.9836 | 30 | 1.0 | 1.0 | 1.0 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.7143 | 1.0 | 0.8333 | 10 | 0.9655 | 0.9929 | 0.9790 | 0.9994 |
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+ | 0.0042 | 29.0 | 3422 | 0.0052 | 0.9677 | 1.0 | 0.9836 | 30 | 1.0 | 1.0 | 1.0 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.7143 | 1.0 | 0.8333 | 10 | 0.9655 | 0.9929 | 0.9790 | 0.9994 |
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+ | 0.0039 | 30.0 | 3540 | 0.0049 | 0.9677 | 1.0 | 0.9836 | 30 | 1.0 | 1.0 | 1.0 | 29 | 1.0 | 1.0 | 1.0 | 30 | 0.0 | 0.0 | 0.0 | 1 | 1.0 | 1.0 | 1.0 | 11 | 1.0 | 1.0 | 1.0 | 30 | 0.7143 | 1.0 | 0.8333 | 10 | 0.9655 | 0.9929 | 0.9790 | 0.9994 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.21.0.dev0
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+ - Pytorch 1.11.0+cu113
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+ - Datasets 2.2.2
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+ - Tokenizers 0.12.1