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lmv2-g-pan-143doc-06-12

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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-pan-143doc-06-12
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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-pan-143doc-06-12
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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: 0.0443
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+ - Dob Precision: 1.0
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+ - Dob Recall: 1.0
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+ - Dob F1: 1.0
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+ - Dob Number: 27
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+ - Fname Precision: 1.0
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+ - Fname Recall: 0.9643
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+ - Fname F1: 0.9818
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+ - Fname Number: 28
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+ - Name Precision: 0.9630
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+ - Name Recall: 0.9630
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+ - Name F1: 0.9630
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+ - Name Number: 27
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+ - Pan Precision: 1.0
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+ - Pan Recall: 1.0
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+ - Pan F1: 1.0
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+ - Pan Number: 26
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+ - Overall Precision: 0.9907
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+ - Overall Recall: 0.9815
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+ - Overall F1: 0.9860
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+ - Overall Accuracy: 0.9978
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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 | Dob Precision | Dob Recall | Dob F1 | Dob Number | Fname Precision | Fname Recall | Fname F1 | Fname Number | Name Precision | Name Recall | Name F1 | Name Number | Pan Precision | Pan Recall | Pan F1 | Pan Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:-------------:|:----------:|:------:|:----------:|:---------------:|:------------:|:--------:|:------------:|:--------------:|:-----------:|:-------:|:-----------:|:-------------:|:----------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 1.274 | 1.0 | 114 | 0.9098 | 0.9310 | 1.0 | 0.9643 | 27 | 0.1481 | 0.1429 | 0.1455 | 28 | 0.1639 | 0.3704 | 0.2273 | 27 | 0.8125 | 1.0 | 0.8966 | 26 | 0.4497 | 0.6204 | 0.5214 | 0.9143 |
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+ | 0.7133 | 2.0 | 228 | 0.5771 | 0.9310 | 1.0 | 0.9643 | 27 | 0.2093 | 0.3214 | 0.2535 | 28 | 0.6562 | 0.7778 | 0.7119 | 27 | 0.9630 | 1.0 | 0.9811 | 26 | 0.6336 | 0.7685 | 0.6946 | 0.9443 |
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+ | 0.4593 | 3.0 | 342 | 0.4018 | 0.9643 | 1.0 | 0.9818 | 27 | 0.8276 | 0.8571 | 0.8421 | 28 | 0.9259 | 0.9259 | 0.9259 | 27 | 1.0 | 1.0 | 1.0 | 26 | 0.9273 | 0.9444 | 0.9358 | 0.9655 |
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+ | 0.3011 | 4.0 | 456 | 0.2638 | 0.9643 | 1.0 | 0.9818 | 27 | 1.0 | 0.9286 | 0.9630 | 28 | 0.9259 | 0.9259 | 0.9259 | 27 | 0.9630 | 1.0 | 0.9811 | 26 | 0.9630 | 0.9630 | 0.9630 | 0.9811 |
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+ | 0.2209 | 5.0 | 570 | 0.2108 | 0.9643 | 1.0 | 0.9818 | 27 | 0.8621 | 0.8929 | 0.8772 | 28 | 0.9286 | 0.9630 | 0.9455 | 27 | 0.9286 | 1.0 | 0.9630 | 26 | 0.9204 | 0.9630 | 0.9412 | 0.9811 |
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+ | 0.1724 | 6.0 | 684 | 0.1671 | 0.9643 | 1.0 | 0.9818 | 27 | 0.9286 | 0.9286 | 0.9286 | 28 | 0.8667 | 0.9630 | 0.9123 | 27 | 0.8966 | 1.0 | 0.9455 | 26 | 0.9130 | 0.9722 | 0.9417 | 0.9844 |
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+ | 0.1285 | 7.0 | 798 | 0.1754 | 0.9643 | 1.0 | 0.9818 | 27 | 0.8929 | 0.8929 | 0.8929 | 28 | 0.9630 | 0.9630 | 0.9630 | 27 | 0.9630 | 1.0 | 0.9811 | 26 | 0.9455 | 0.9630 | 0.9541 | 0.9788 |
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+ | 0.0999 | 8.0 | 912 | 0.1642 | 0.9643 | 1.0 | 0.9818 | 27 | 0.9615 | 0.8929 | 0.9259 | 28 | 0.9630 | 0.9630 | 0.9630 | 27 | 0.9630 | 1.0 | 0.9811 | 26 | 0.9630 | 0.9630 | 0.9630 | 0.9811 |
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+ | 0.0862 | 9.0 | 1026 | 0.1417 | 0.9643 | 1.0 | 0.9818 | 27 | 0.8966 | 0.9286 | 0.9123 | 28 | 0.8966 | 0.9630 | 0.9286 | 27 | 0.9630 | 1.0 | 0.9811 | 26 | 0.9292 | 0.9722 | 0.9502 | 0.9788 |
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+ | 0.0722 | 10.0 | 1140 | 0.1317 | 0.9643 | 1.0 | 0.9818 | 27 | 0.9630 | 0.9286 | 0.9455 | 28 | 0.9286 | 0.9630 | 0.9455 | 27 | 0.9630 | 1.0 | 0.9811 | 26 | 0.9545 | 0.9722 | 0.9633 | 0.9822 |
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+ | 0.0748 | 11.0 | 1254 | 0.1220 | 0.9643 | 1.0 | 0.9818 | 27 | 1.0 | 0.8929 | 0.9434 | 28 | 1.0 | 0.9630 | 0.9811 | 27 | 0.9286 | 1.0 | 0.9630 | 26 | 0.9720 | 0.9630 | 0.9674 | 0.9833 |
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+ | 0.0549 | 12.0 | 1368 | 0.1157 | 0.9643 | 1.0 | 0.9818 | 27 | 0.8966 | 0.9286 | 0.9123 | 28 | 0.8667 | 0.9630 | 0.9123 | 27 | 0.8966 | 1.0 | 0.9455 | 26 | 0.9052 | 0.9722 | 0.9375 | 0.9811 |
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+ | 0.0444 | 13.0 | 1482 | 0.1198 | 0.9643 | 1.0 | 0.9818 | 27 | 1.0 | 0.8929 | 0.9434 | 28 | 0.9630 | 0.9630 | 0.9630 | 27 | 0.9630 | 1.0 | 0.9811 | 26 | 0.9720 | 0.9630 | 0.9674 | 0.9811 |
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+ | 0.0371 | 14.0 | 1596 | 0.1082 | 0.9643 | 1.0 | 0.9818 | 27 | 0.8966 | 0.9286 | 0.9123 | 28 | 0.8966 | 0.9630 | 0.9286 | 27 | 0.7879 | 1.0 | 0.8814 | 26 | 0.8824 | 0.9722 | 0.9251 | 0.9833 |
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+ | 0.036 | 15.0 | 1710 | 0.1257 | 0.9643 | 1.0 | 0.9818 | 27 | 0.9630 | 0.9286 | 0.9455 | 28 | 0.9630 | 0.9630 | 0.9630 | 27 | 0.8966 | 1.0 | 0.9455 | 26 | 0.9459 | 0.9722 | 0.9589 | 0.9800 |
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+ | 0.0291 | 16.0 | 1824 | 0.0930 | 0.9643 | 1.0 | 0.9818 | 27 | 0.9643 | 0.9643 | 0.9643 | 28 | 0.9643 | 1.0 | 0.9818 | 27 | 0.8667 | 1.0 | 0.9286 | 26 | 0.9386 | 0.9907 | 0.9640 | 0.9900 |
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+ | 0.0267 | 17.0 | 1938 | 0.0993 | 0.9643 | 1.0 | 0.9818 | 27 | 0.9286 | 0.9286 | 0.9286 | 28 | 0.9286 | 0.9630 | 0.9455 | 27 | 0.9286 | 1.0 | 0.9630 | 26 | 0.9375 | 0.9722 | 0.9545 | 0.9844 |
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+ | 0.023 | 18.0 | 2052 | 0.1240 | 0.9643 | 1.0 | 0.9818 | 27 | 0.7941 | 0.9643 | 0.8710 | 28 | 0.9643 | 1.0 | 0.9818 | 27 | 0.8387 | 1.0 | 0.9123 | 26 | 0.8843 | 0.9907 | 0.9345 | 0.9800 |
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+ | 0.0379 | 19.0 | 2166 | 0.1154 | 0.9643 | 1.0 | 0.9818 | 27 | 1.0 | 0.9286 | 0.9630 | 28 | 0.9286 | 0.9630 | 0.9455 | 27 | 0.9286 | 1.0 | 0.9630 | 26 | 0.9545 | 0.9722 | 0.9633 | 0.9833 |
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+ | 0.0199 | 20.0 | 2280 | 0.1143 | 0.9643 | 1.0 | 0.9818 | 27 | 1.0 | 0.9286 | 0.9630 | 28 | 0.8966 | 0.9630 | 0.9286 | 27 | 0.8667 | 1.0 | 0.9286 | 26 | 0.9292 | 0.9722 | 0.9502 | 0.9844 |
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+ | 0.0256 | 21.0 | 2394 | 0.1175 | 0.9643 | 1.0 | 0.9818 | 27 | 0.8667 | 0.9286 | 0.8966 | 28 | 0.9286 | 0.9630 | 0.9455 | 27 | 0.9286 | 1.0 | 0.9630 | 26 | 0.9211 | 0.9722 | 0.9459 | 0.9811 |
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+ | 0.0388 | 22.0 | 2508 | 0.0964 | 0.9643 | 1.0 | 0.9818 | 27 | 0.8966 | 0.9286 | 0.9123 | 28 | 0.9310 | 1.0 | 0.9643 | 27 | 0.8966 | 1.0 | 0.9455 | 26 | 0.9217 | 0.9815 | 0.9507 | 0.9855 |
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+ | 0.0334 | 23.0 | 2622 | 0.1186 | 0.9643 | 1.0 | 0.9818 | 27 | 1.0 | 0.9286 | 0.9630 | 28 | 1.0 | 0.9630 | 0.9811 | 27 | 0.8966 | 1.0 | 0.9455 | 26 | 0.9633 | 0.9722 | 0.9677 | 0.9833 |
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+ | 0.0134 | 24.0 | 2736 | 0.1193 | 0.9643 | 1.0 | 0.9818 | 27 | 0.9630 | 0.9286 | 0.9455 | 28 | 1.0 | 0.9630 | 0.9811 | 27 | 0.9286 | 1.0 | 0.9630 | 26 | 0.9633 | 0.9722 | 0.9677 | 0.9822 |
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+ | 0.0157 | 25.0 | 2850 | 0.1078 | 1.0 | 1.0 | 1.0 | 27 | 0.9259 | 0.8929 | 0.9091 | 28 | 0.9286 | 0.9630 | 0.9455 | 27 | 0.8966 | 1.0 | 0.9455 | 26 | 0.9369 | 0.9630 | 0.9498 | 0.9833 |
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+ | 0.0157 | 26.0 | 2964 | 0.0758 | 1.0 | 1.0 | 1.0 | 27 | 0.8929 | 0.8929 | 0.8929 | 28 | 1.0 | 1.0 | 1.0 | 27 | 0.8966 | 1.0 | 0.9455 | 26 | 0.9459 | 0.9722 | 0.9589 | 0.9911 |
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+ | 0.0096 | 27.0 | 3078 | 0.0766 | 1.0 | 1.0 | 1.0 | 27 | 0.8929 | 0.8929 | 0.8929 | 28 | 1.0 | 1.0 | 1.0 | 27 | 0.8966 | 1.0 | 0.9455 | 26 | 0.9459 | 0.9722 | 0.9589 | 0.9889 |
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+ | 0.0135 | 28.0 | 3192 | 0.0443 | 1.0 | 1.0 | 1.0 | 27 | 1.0 | 0.9643 | 0.9818 | 28 | 0.9630 | 0.9630 | 0.9630 | 27 | 1.0 | 1.0 | 1.0 | 26 | 0.9907 | 0.9815 | 0.9860 | 0.9978 |
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+ | 0.012 | 29.0 | 3306 | 0.1153 | 0.9643 | 1.0 | 0.9818 | 27 | 0.8966 | 0.9286 | 0.9123 | 28 | 0.8667 | 0.9630 | 0.9123 | 27 | 0.8966 | 1.0 | 0.9455 | 26 | 0.9052 | 0.9722 | 0.9375 | 0.9822 |
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+ | 0.0069 | 30.0 | 3420 | 0.1373 | 0.9643 | 1.0 | 0.9818 | 27 | 0.8966 | 0.9286 | 0.9123 | 28 | 0.9286 | 0.9630 | 0.9455 | 27 | 0.8966 | 1.0 | 0.9455 | 26 | 0.9211 | 0.9722 | 0.9459 | 0.9777 |
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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