update model card README.md
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README.md
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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-rai_1-995-doc-10-18
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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-rai_1-995-doc-10-18
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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: 0.0163
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- Dob Key Precision: 0.7638
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- Dob Key Recall: 0.7638
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- Dob Key F1: 0.7638
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- Dob Key Number: 127
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- Dob Value Precision: 0.9767
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- Dob Value Recall: 0.9767
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- Dob Value F1: 0.9767
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- Dob Value Number: 129
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- Doctor Name Key Precision: 0.6970
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- Doctor Name Key Recall: 0.6866
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- Doctor Name Key F1: 0.6917
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- Doctor Name Key Number: 67
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- Doctor Name Value Precision: 0.9275
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- Doctor Name Value Recall: 0.9143
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- Doctor Name Value F1: 0.9209
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- Doctor Name Value Number: 70
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- Patient Name Key Precision: 0.7055
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- Patient Name Key Recall: 0.7357
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- Patient Name Key F1: 0.7203
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- Patient Name Key Number: 140
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- Patient Name Value Precision: 0.9724
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- Patient Name Value Recall: 0.9792
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- Patient Name Value F1: 0.9758
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- Patient Name Value Number: 144
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- Overall Precision: 0.8460
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- Overall Recall: 0.8523
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- Overall F1: 0.8492
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- Overall Accuracy: 0.9958
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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 | Dob Key Precision | Dob Key Recall | Dob Key F1 | Dob Key Number | Dob Value Precision | Dob Value Recall | Dob Value F1 | Dob Value Number | Doctor Name Key Precision | Doctor Name Key Recall | Doctor Name Key F1 | Doctor Name Key Number | Doctor Name Value Precision | Doctor Name Value Recall | Doctor Name Value F1 | Doctor Name Value Number | Patient Name Key Precision | Patient Name Key Recall | Patient Name Key F1 | Patient Name Key Number | Patient Name Value Precision | Patient Name Value Recall | Patient Name Value F1 | Patient Name Value Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-------------------------:|:----------------------:|:------------------:|:----------------------:|:---------------------------:|:------------------------:|:--------------------:|:------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------------:|:-------------------------:|:---------------------:|:-------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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| 0.5034 | 1.0 | 796 | 0.0841 | 0.7143 | 0.7480 | 0.7308 | 127 | 0.7881 | 0.9225 | 0.85 | 129 | 0.0 | 0.0 | 0.0 | 67 | 0.0 | 0.0 | 0.0 | 70 | 0.5988 | 0.7143 | 0.6515 | 140 | 0.4908 | 0.9236 | 0.6410 | 144 | 0.5944 | 0.6603 | 0.6256 | 0.9887 |
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| 0.0579 | 2.0 | 1592 | 0.0365 | 0.7231 | 0.7402 | 0.7315 | 127 | 0.9766 | 0.9690 | 0.9728 | 129 | 0.6462 | 0.6269 | 0.6364 | 67 | 0.9296 | 0.9429 | 0.9362 | 70 | 0.7103 | 0.7357 | 0.7228 | 140 | 0.9392 | 0.9653 | 0.9521 | 144 | 0.8282 | 0.8405 | 0.8343 | 0.9954 |
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| 0.0317 | 3.0 | 2388 | 0.0297 | 0.7578 | 0.7638 | 0.7608 | 127 | 0.9767 | 0.9767 | 0.9767 | 129 | 0.7077 | 0.6866 | 0.6970 | 67 | 0.8676 | 0.8429 | 0.8551 | 70 | 0.6474 | 0.7214 | 0.6824 | 140 | 0.8993 | 0.9306 | 0.9147 | 144 | 0.8101 | 0.8316 | 0.8207 | 0.9943 |
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| 0.0233 | 4.0 | 3184 | 0.0195 | 0.7638 | 0.7638 | 0.7638 | 127 | 0.9403 | 0.9767 | 0.9582 | 129 | 0.7015 | 0.7015 | 0.7015 | 67 | 0.9718 | 0.9857 | 0.9787 | 70 | 0.6164 | 0.7 | 0.6555 | 140 | 0.9724 | 0.9792 | 0.9758 | 144 | 0.8222 | 0.8538 | 0.8377 | 0.9958 |
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| 0.0189 | 5.0 | 3980 | 0.0188 | 0.7462 | 0.7638 | 0.7549 | 127 | 0.9545 | 0.9767 | 0.9655 | 129 | 0.5606 | 0.5522 | 0.5564 | 67 | 0.9565 | 0.9429 | 0.9496 | 70 | 0.6228 | 0.7429 | 0.6775 | 140 | 0.9724 | 0.9792 | 0.9758 | 144 | 0.8054 | 0.8434 | 0.8240 | 0.9955 |
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| 0.0174 | 6.0 | 4776 | 0.0167 | 0.7638 | 0.7638 | 0.7638 | 127 | 0.9767 | 0.9767 | 0.9767 | 129 | 0.5970 | 0.5970 | 0.5970 | 67 | 0.9714 | 0.9714 | 0.9714 | 70 | 0.6478 | 0.7357 | 0.6890 | 140 | 0.9724 | 0.9792 | 0.9758 | 144 | 0.8250 | 0.8493 | 0.8370 | 0.9956 |
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| 0.0162 | 7.0 | 5572 | 0.0185 | 0.7578 | 0.7638 | 0.7608 | 127 | 0.9767 | 0.9767 | 0.9767 | 129 | 0.4272 | 0.6567 | 0.5176 | 67 | 0.9677 | 0.8571 | 0.9091 | 70 | 0.7007 | 0.7357 | 0.7178 | 140 | 0.9724 | 0.9792 | 0.9758 | 144 | 0.7997 | 0.8434 | 0.8210 | 0.9954 |
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| 0.0153 | 8.0 | 6368 | 0.0170 | 0.7638 | 0.7638 | 0.7638 | 127 | 0.9767 | 0.9767 | 0.9767 | 129 | 0.5758 | 0.5672 | 0.5714 | 67 | 0.9571 | 0.9571 | 0.9571 | 70 | 0.7305 | 0.7357 | 0.7331 | 140 | 0.9724 | 0.9792 | 0.9758 | 144 | 0.8437 | 0.8449 | 0.8443 | 0.9957 |
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| 0.0142 | 9.0 | 7164 | 0.0163 | 0.7638 | 0.7638 | 0.7638 | 127 | 0.9767 | 0.9767 | 0.9767 | 129 | 0.6970 | 0.6866 | 0.6917 | 67 | 0.9275 | 0.9143 | 0.9209 | 70 | 0.7055 | 0.7357 | 0.7203 | 140 | 0.9724 | 0.9792 | 0.9758 | 144 | 0.8460 | 0.8523 | 0.8492 | 0.9958 |
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| 0.0136 | 10.0 | 7960 | 0.0177 | 0.7405 | 0.7638 | 0.7519 | 127 | 0.9767 | 0.9767 | 0.9767 | 129 | 0.6094 | 0.5821 | 0.5954 | 67 | 0.8358 | 0.8 | 0.8175 | 70 | 0.6541 | 0.7429 | 0.6957 | 140 | 0.9589 | 0.9722 | 0.9655 | 144 | 0.8075 | 0.8301 | 0.8186 | 0.9953 |
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| 0.0131 | 11.0 | 8756 | 0.0202 | 0.7402 | 0.7402 | 0.7402 | 127 | 0.9767 | 0.9767 | 0.9767 | 129 | 0.5968 | 0.5522 | 0.5736 | 67 | 0.9403 | 0.9 | 0.9197 | 70 | 0.7305 | 0.7357 | 0.7331 | 140 | 0.9655 | 0.9722 | 0.9689 | 144 | 0.8390 | 0.8316 | 0.8353 | 0.9954 |
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| 0.0134 | 12.0 | 9552 | 0.0195 | 0.7239 | 0.7638 | 0.7433 | 127 | 0.9237 | 0.8450 | 0.8826 | 129 | 0.5846 | 0.5672 | 0.5758 | 67 | 0.9041 | 0.9429 | 0.9231 | 70 | 0.7305 | 0.7357 | 0.7331 | 140 | 0.9722 | 0.9722 | 0.9722 | 144 | 0.8193 | 0.8168 | 0.8180 | 0.9949 |
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| 0.0127 | 13.0 | 10348 | 0.0169 | 0.7638 | 0.7638 | 0.7638 | 127 | 0.9767 | 0.9767 | 0.9767 | 129 | 0.7077 | 0.6866 | 0.6970 | 67 | 0.9403 | 0.9 | 0.9197 | 70 | 0.6211 | 0.7143 | 0.6645 | 140 | 0.9724 | 0.9792 | 0.9758 | 144 | 0.8256 | 0.8464 | 0.8359 | 0.9957 |
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| 0.0119 | 14.0 | 11144 | 0.0174 | 0.7638 | 0.7638 | 0.7638 | 127 | 0.9767 | 0.9767 | 0.9767 | 129 | 0.5821 | 0.5821 | 0.5821 | 67 | 0.9437 | 0.9571 | 0.9504 | 70 | 0.6897 | 0.7143 | 0.7018 | 140 | 0.9338 | 0.9792 | 0.9559 | 144 | 0.8261 | 0.8419 | 0.8339 | 0.9955 |
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| 0.013 | 15.0 | 11940 | 0.0174 | 0.6953 | 0.7008 | 0.6980 | 127 | 0.9767 | 0.9767 | 0.9767 | 129 | 0.6164 | 0.6716 | 0.6429 | 67 | 0.9706 | 0.9429 | 0.9565 | 70 | 0.6667 | 0.7143 | 0.6897 | 140 | 0.9583 | 0.9583 | 0.9583 | 144 | 0.8150 | 0.8331 | 0.8240 | 0.9950 |
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| 0.0133 | 16.0 | 12736 | 0.0195 | 0.7008 | 0.7008 | 0.7008 | 127 | 0.9767 | 0.9767 | 0.9767 | 129 | 0.5823 | 0.6866 | 0.6301 | 67 | 0.9054 | 0.9571 | 0.9306 | 70 | 0.6174 | 0.6571 | 0.6367 | 140 | 0.9161 | 0.9097 | 0.9129 | 144 | 0.7860 | 0.8139 | 0.7997 | 0.9946 |
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| 0.0154 | 17.0 | 13532 | 0.0239 | 0.6885 | 0.6614 | 0.6747 | 127 | 0.8623 | 0.9225 | 0.8914 | 129 | 0.5057 | 0.6567 | 0.5714 | 67 | 0.9403 | 0.9 | 0.9197 | 70 | 0.3727 | 0.5857 | 0.4556 | 140 | 0.9655 | 0.9722 | 0.9689 | 144 | 0.6829 | 0.7858 | 0.7308 | 0.9935 |
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| 0.0163 | 18.0 | 14328 | 0.0437 | 0.6607 | 0.5827 | 0.6192 | 127 | 0.5736 | 0.8760 | 0.6933 | 129 | 0.4177 | 0.4925 | 0.4521 | 67 | 0.8243 | 0.8714 | 0.8472 | 70 | 0.4845 | 0.5571 | 0.5183 | 140 | 0.5990 | 0.7986 | 0.6845 | 144 | 0.5816 | 0.7001 | 0.6354 | 0.9887 |
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| 0.0109 | 19.0 | 15124 | 0.0220 | 0.7578 | 0.7638 | 0.7608 | 127 | 0.9767 | 0.9767 | 0.9767 | 129 | 0.7097 | 0.6567 | 0.6822 | 67 | 0.9403 | 0.9 | 0.9197 | 70 | 0.6776 | 0.7357 | 0.7055 | 140 | 0.9724 | 0.9792 | 0.9758 | 144 | 0.8404 | 0.8479 | 0.8441 | 0.9955 |
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| 0.0104 | 20.0 | 15920 | 0.0184 | 0.6093 | 0.7244 | 0.6619 | 127 | 0.976 | 0.9457 | 0.9606 | 129 | 0.6133 | 0.6866 | 0.6479 | 67 | 0.9437 | 0.9571 | 0.9504 | 70 | 0.6013 | 0.6571 | 0.6280 | 140 | 0.9724 | 0.9792 | 0.9758 | 144 | 0.7778 | 0.8272 | 0.8017 | 0.9950 |
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| 0.0086 | 21.0 | 16716 | 0.0232 | 0.3889 | 0.4409 | 0.4133 | 127 | 0.9767 | 0.9767 | 0.9767 | 129 | 0.5270 | 0.5821 | 0.5532 | 67 | 0.9444 | 0.9714 | 0.9577 | 70 | 0.5245 | 0.5357 | 0.5300 | 140 | 0.9724 | 0.9792 | 0.9758 | 144 | 0.7143 | 0.7459 | 0.7298 | 0.9930 |
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| 0.0085 | 22.0 | 17512 | 0.0197 | 0.7480 | 0.7480 | 0.7480 | 127 | 0.9767 | 0.9767 | 0.9767 | 129 | 0.6471 | 0.6567 | 0.6519 | 67 | 0.9189 | 0.9714 | 0.9444 | 70 | 0.6149 | 0.65 | 0.6319 | 140 | 0.9658 | 0.9792 | 0.9724 | 144 | 0.8165 | 0.8346 | 0.8254 | 0.9951 |
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| 0.0083 | 23.0 | 18308 | 0.0220 | 0.7328 | 0.7559 | 0.7442 | 127 | 0.9692 | 0.9767 | 0.9730 | 129 | 0.6081 | 0.6716 | 0.6383 | 67 | 0.9571 | 0.9571 | 0.9571 | 70 | 0.6479 | 0.6571 | 0.6525 | 140 | 0.9592 | 0.9792 | 0.9691 | 144 | 0.8170 | 0.8375 | 0.8271 | 0.9952 |
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| 0.0084 | 24.0 | 19104 | 0.0226 | 0.6418 | 0.6772 | 0.6590 | 127 | 0.9767 | 0.9767 | 0.9767 | 129 | 0.5 | 0.7164 | 0.5890 | 67 | 0.8919 | 0.9429 | 0.9167 | 70 | 0.5034 | 0.5286 | 0.5157 | 140 | 0.9724 | 0.9792 | 0.9758 | 144 | 0.7462 | 0.7991 | 0.7718 | 0.9942 |
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| 0.0067 | 25.0 | 19900 | 0.0257 | 0.6691 | 0.7165 | 0.6920 | 127 | 0.9692 | 0.9767 | 0.9730 | 129 | 0.6267 | 0.7015 | 0.6620 | 67 | 0.9143 | 0.9143 | 0.9143 | 70 | 0.6828 | 0.7071 | 0.6947 | 140 | 0.94 | 0.9792 | 0.9592 | 144 | 0.8045 | 0.8390 | 0.8214 | 0.9949 |
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| 0.0071 | 26.0 | 20696 | 0.0241 | 0.5828 | 0.6929 | 0.6331 | 127 | 0.9692 | 0.9767 | 0.9730 | 129 | 0.6029 | 0.6119 | 0.6074 | 67 | 0.8889 | 0.9143 | 0.9014 | 70 | 0.5563 | 0.5643 | 0.5603 | 140 | 0.9658 | 0.9792 | 0.9724 | 144 | 0.7602 | 0.7962 | 0.7778 | 0.9943 |
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| 0.0072 | 27.0 | 21492 | 0.0222 | 0.6850 | 0.6850 | 0.6850 | 127 | 0.9767 | 0.9767 | 0.9767 | 129 | 0.5714 | 0.6567 | 0.6111 | 67 | 0.9178 | 0.9571 | 0.9371 | 70 | 0.6370 | 0.6643 | 0.6503 | 140 | 0.9592 | 0.9792 | 0.9691 | 144 | 0.7983 | 0.8242 | 0.8110 | 0.9948 |
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| 0.0057 | 28.0 | 22288 | 0.0259 | 0.5909 | 0.6142 | 0.6023 | 127 | 0.9767 | 0.9767 | 0.9767 | 129 | 0.6714 | 0.7015 | 0.6861 | 67 | 0.9275 | 0.9143 | 0.9209 | 70 | 0.5734 | 0.5857 | 0.5795 | 140 | 0.9724 | 0.9792 | 0.9758 | 144 | 0.7820 | 0.7947 | 0.7883 | 0.9943 |
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| 0.0054 | 29.0 | 23084 | 0.0299 | 0.6418 | 0.6772 | 0.6590 | 127 | 0.9618 | 0.9767 | 0.9692 | 129 | 0.6216 | 0.6866 | 0.6525 | 67 | 0.8873 | 0.9 | 0.8936 | 70 | 0.5306 | 0.5571 | 0.5436 | 140 | 0.9655 | 0.9722 | 0.9689 | 144 | 0.7678 | 0.7962 | 0.7817 | 0.9937 |
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| 0.0066 | 30.0 | 23880 | 0.0254 | 0.5532 | 0.6142 | 0.5821 | 127 | 0.9259 | 0.9690 | 0.9470 | 129 | 0.5938 | 0.5672 | 0.5802 | 67 | 0.9130 | 0.9 | 0.9065 | 70 | 0.6738 | 0.6786 | 0.6762 | 140 | 0.9592 | 0.9792 | 0.9691 | 144 | 0.7747 | 0.7976 | 0.7860 | 0.9943 |
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### Framework versions
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- Transformers 4.24.0.dev0
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- Pytorch 1.12.1+cu113
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- Datasets 2.2.2
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- Tokenizers 0.13.1
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