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lmv2-g-aadhaar-236doc-06-14

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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-aadhaar-236doc-06-14
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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-aadhaar-236doc-06-14
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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: 0.0427
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+ - Aadhaar Precision: 0.9783
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+ - Aadhaar Recall: 1.0
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+ - Aadhaar F1: 0.9890
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+ - Aadhaar Number: 45
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+ - Dob Precision: 0.9787
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+ - Dob Recall: 1.0
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+ - Dob F1: 0.9892
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+ - Dob Number: 46
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+ - Gender Precision: 1.0
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+ - Gender Recall: 0.9787
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+ - Gender F1: 0.9892
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+ - Gender Number: 47
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+ - Name Precision: 0.9574
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+ - Name Recall: 0.9375
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+ - Name F1: 0.9474
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+ - Name Number: 48
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+ - Overall Precision: 0.9785
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+ - Overall Recall: 0.9785
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+ - Overall F1: 0.9785
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+ - Overall Accuracy: 0.9939
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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 | Aadhaar Precision | Aadhaar Recall | Aadhaar F1 | Aadhaar Number | Dob Precision | Dob Recall | Dob F1 | Dob Number | Gender Precision | Gender Recall | Gender F1 | Gender Number | Name Precision | Name Recall | Name F1 | Name Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-------------:|:----------:|:------:|:----------:|:----------------:|:-------------:|:---------:|:-------------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 1.0024 | 1.0 | 188 | 0.5819 | 0.9348 | 0.9556 | 0.9451 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9574 | 0.9783 | 47 | 0.5172 | 0.625 | 0.5660 | 48 | 0.8410 | 0.8817 | 0.8609 | 0.9744 |
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+ | 0.4484 | 2.0 | 376 | 0.3263 | 0.8980 | 0.9778 | 0.9362 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.6842 | 0.8125 | 0.7429 | 48 | 0.8838 | 0.9409 | 0.9115 | 0.9733 |
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+ | 0.2508 | 3.0 | 564 | 0.2230 | 0.9318 | 0.9111 | 0.9213 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.8913 | 0.8542 | 0.8723 | 48 | 0.9560 | 0.9355 | 0.9457 | 0.9811 |
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+ | 0.165 | 4.0 | 752 | 0.1728 | 0.9362 | 0.9778 | 0.9565 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.8444 | 0.7917 | 0.8172 | 48 | 0.9457 | 0.9355 | 0.9405 | 0.9844 |
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+ | 0.1081 | 5.0 | 940 | 0.0987 | 0.8958 | 0.9556 | 0.9247 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 1.0 | 0.9167 | 0.9565 | 48 | 0.9728 | 0.9624 | 0.9676 | 0.9928 |
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+ | 0.0834 | 6.0 | 1128 | 0.0984 | 0.8980 | 0.9778 | 0.9362 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9574 | 0.9783 | 47 | 0.8148 | 0.9167 | 0.8627 | 48 | 0.9227 | 0.9624 | 0.9421 | 0.9833 |
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+ | 0.0676 | 7.0 | 1316 | 0.0773 | 0.9362 | 0.9778 | 0.9565 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.9111 | 0.8542 | 0.8817 | 48 | 0.9620 | 0.9516 | 0.9568 | 0.9894 |
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+ | 0.0572 | 8.0 | 1504 | 0.0786 | 0.8235 | 0.9333 | 0.8750 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.8936 | 0.875 | 0.8842 | 48 | 0.9263 | 0.9462 | 0.9362 | 0.9872 |
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+ | 0.0481 | 9.0 | 1692 | 0.0576 | 0.9375 | 1.0 | 0.9677 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.9362 | 0.9167 | 0.9263 | 48 | 0.9679 | 0.9731 | 0.9705 | 0.99 |
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+ | 0.0349 | 10.0 | 1880 | 0.0610 | 0.9574 | 1.0 | 0.9783 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.8958 | 0.8958 | 0.8958 | 48 | 0.9626 | 0.9677 | 0.9651 | 0.9894 |
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+ | 0.0287 | 11.0 | 2068 | 0.0978 | 0.9091 | 0.8889 | 0.8989 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.9348 | 0.8958 | 0.9149 | 48 | 0.9615 | 0.9409 | 0.9511 | 0.985 |
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+ | 0.0297 | 12.0 | 2256 | 0.0993 | 0.9375 | 1.0 | 0.9677 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.7959 | 0.8125 | 0.8041 | 48 | 0.9312 | 0.9462 | 0.9387 | 0.9833 |
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+ | 0.0395 | 13.0 | 2444 | 0.0824 | 0.9362 | 0.9778 | 0.9565 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.875 | 0.875 | 0.875 | 48 | 0.9519 | 0.9570 | 0.9544 | 0.9872 |
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+ | 0.0333 | 14.0 | 2632 | 0.0788 | 0.8913 | 0.9111 | 0.9011 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.9556 | 0.8958 | 0.9247 | 48 | 0.9617 | 0.9462 | 0.9539 | 0.9867 |
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+ | 0.0356 | 15.0 | 2820 | 0.0808 | 0.84 | 0.9333 | 0.8842 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.9565 | 0.9167 | 0.9362 | 48 | 0.9468 | 0.9570 | 0.9519 | 0.9867 |
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+ | 0.0192 | 16.0 | 3008 | 0.0955 | 0.8462 | 0.9778 | 0.9072 | 45 | 0.9787 | 1.0 | 0.9892 | 46 | 0.9583 | 0.9787 | 0.9684 | 47 | 0.9070 | 0.8125 | 0.8571 | 48 | 0.9211 | 0.9409 | 0.9309 | 0.9822 |
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+ | 0.016 | 17.0 | 3196 | 0.0936 | 0.9130 | 0.9333 | 0.9231 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.9318 | 0.8542 | 0.8913 | 48 | 0.9615 | 0.9409 | 0.9511 | 0.9867 |
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+ | 0.0218 | 18.0 | 3384 | 0.1009 | 0.9545 | 0.9333 | 0.9438 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.8571 | 0.875 | 0.8660 | 48 | 0.9514 | 0.9462 | 0.9488 | 0.9844 |
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+ | 0.0165 | 19.0 | 3572 | 0.0517 | 0.9574 | 1.0 | 0.9783 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.9333 | 0.875 | 0.9032 | 48 | 0.9728 | 0.9624 | 0.9676 | 0.9906 |
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+ | 0.0198 | 20.0 | 3760 | 0.0890 | 0.9167 | 0.9778 | 0.9462 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.9149 | 0.8958 | 0.9053 | 48 | 0.9572 | 0.9624 | 0.9598 | 0.9867 |
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+ | 0.0077 | 21.0 | 3948 | 0.0835 | 0.9574 | 1.0 | 0.9783 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.88 | 0.9167 | 0.8980 | 48 | 0.9577 | 0.9731 | 0.9653 | 0.9872 |
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+ | 0.0088 | 22.0 | 4136 | 0.0427 | 0.9783 | 1.0 | 0.9890 | 45 | 0.9787 | 1.0 | 0.9892 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.9574 | 0.9375 | 0.9474 | 48 | 0.9785 | 0.9785 | 0.9785 | 0.9939 |
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+ | 0.0078 | 23.0 | 4324 | 0.0597 | 0.9574 | 1.0 | 0.9783 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.8654 | 0.9375 | 0.9 | 48 | 0.9529 | 0.9785 | 0.9655 | 0.9889 |
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+ | 0.0178 | 24.0 | 4512 | 0.0524 | 0.9574 | 1.0 | 0.9783 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 1.0 | 0.875 | 0.9333 | 48 | 0.9890 | 0.9624 | 0.9755 | 0.9922 |
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+ | 0.012 | 25.0 | 4700 | 0.0637 | 0.9375 | 1.0 | 0.9677 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.8491 | 0.9375 | 0.8911 | 48 | 0.9430 | 0.9785 | 0.9604 | 0.9867 |
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+ | 0.0135 | 26.0 | 4888 | 0.0668 | 0.9184 | 1.0 | 0.9574 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.86 | 0.8958 | 0.8776 | 48 | 0.9424 | 0.9677 | 0.9549 | 0.9867 |
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+ | 0.0123 | 27.0 | 5076 | 0.0713 | 0.9565 | 0.9778 | 0.9670 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.9375 | 0.9375 | 0.9375 | 48 | 0.9731 | 0.9731 | 0.9731 | 0.9911 |
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+ | 0.0074 | 28.0 | 5264 | 0.0675 | 0.9362 | 0.9778 | 0.9565 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.9 | 0.9375 | 0.9184 | 48 | 0.9577 | 0.9731 | 0.9653 | 0.99 |
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+ | 0.0051 | 29.0 | 5452 | 0.0713 | 0.9362 | 0.9778 | 0.9565 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.9167 | 0.9167 | 0.9167 | 48 | 0.9626 | 0.9677 | 0.9651 | 0.9906 |
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+ | 0.0027 | 30.0 | 5640 | 0.0725 | 0.9362 | 0.9778 | 0.9565 | 45 | 1.0 | 1.0 | 1.0 | 46 | 1.0 | 0.9787 | 0.9892 | 47 | 0.9167 | 0.9167 | 0.9167 | 48 | 0.9626 | 0.9677 | 0.9651 | 0.9906 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.20.0.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