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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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<!-- 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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# lmv2-g-aadhaar-236doc-06-14 |
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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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## 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: 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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### Training results |
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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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### Framework versions |
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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 |
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