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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: lmv2-g-rai-aRx-refill-230427
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lmv2-g-rai-aRx-refill-230427
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0176
- Dob Key Precision: 0.6846
- Dob Key Recall: 0.7007
- Dob Key F1: 0.6926
- Dob Key Number: 598
- Dob Value Precision: 0.9934
- Dob Value Recall: 0.9983
- Dob Value F1: 0.9958
- Dob Value Number: 599
- Patient Name Key Precision: 0.6852
- Patient Name Key Recall: 0.7089
- Patient Name Key F1: 0.6968
- Patient Name Key Number: 608
- Patient Name Value Precision: 0.9581
- Patient Name Value Recall: 0.9738
- Patient Name Value F1: 0.9659
- Patient Name Value Number: 611
- Provider Name Key Precision: 0.7875
- Provider Name Key Recall: 0.7903
- Provider Name Key F1: 0.7889
- Provider Name Key Number: 558
- Provider Name Value Precision: 0.9786
- Provider Name Value Recall: 0.9751
- Provider Name Value F1: 0.9768
- Provider Name Value Number: 562
- Overall Precision: 0.8460
- Overall Recall: 0.8575
- Overall F1: 0.8517
- Overall Accuracy: 0.9936
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| 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 | 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 | Provider Name Key Precision | Provider Name Key Recall | Provider Name Key F1 | Provider Name Key Number | Provider Name Value Precision | Provider Name Value Recall | Provider Name Value F1 | Provider Name Value Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-------------------:|:----------------:|:------------:|:----------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------------:|:-------------------------:|:---------------------:|:-------------------------:|:---------------------------:|:------------------------:|:--------------------:|:------------------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.5109 | 1.0 | 691 | 0.0829 | 0.6804 | 0.6906 | 0.6855 | 598 | 0.9469 | 0.9833 | 0.9648 | 599 | 0.6607 | 0.6628 | 0.6617 | 608 | 0.9331 | 0.9362 | 0.9346 | 611 | 0.5872 | 0.6577 | 0.6205 | 558 | 0.9442 | 0.9626 | 0.9533 | 562 | 0.7904 | 0.8159 | 0.8030 | 0.9921 |
| 0.0554 | 2.0 | 1382 | 0.0391 | 0.6838 | 0.6906 | 0.6872 | 598 | 0.9950 | 0.9983 | 0.9967 | 599 | 0.6546 | 0.6859 | 0.6699 | 608 | 0.9357 | 0.9525 | 0.9440 | 611 | 0.7431 | 0.7724 | 0.7575 | 558 | 0.9231 | 0.9609 | 0.9416 | 562 | 0.8214 | 0.8430 | 0.8321 | 0.9926 |
| 0.0298 | 3.0 | 2073 | 0.0255 | 0.6922 | 0.6957 | 0.6939 | 598 | 0.9934 | 0.9983 | 0.9958 | 599 | 0.6505 | 0.6859 | 0.6677 | 608 | 0.9560 | 0.9591 | 0.9575 | 611 | 0.7718 | 0.7760 | 0.7739 | 558 | 0.9459 | 0.9644 | 0.9551 | 562 | 0.8332 | 0.8462 | 0.8396 | 0.9931 |
| 0.0213 | 4.0 | 2764 | 0.0208 | 0.6264 | 0.6756 | 0.6500 | 598 | 0.9934 | 0.9983 | 0.9958 | 599 | 0.6762 | 0.6974 | 0.6866 | 608 | 0.9645 | 0.9787 | 0.9716 | 611 | 0.7334 | 0.7545 | 0.7438 | 558 | 0.9579 | 0.9715 | 0.9647 | 562 | 0.8222 | 0.8459 | 0.8338 | 0.9928 |
| 0.0177 | 5.0 | 3455 | 0.0185 | 0.6672 | 0.6973 | 0.6819 | 598 | 0.9934 | 0.9983 | 0.9958 | 599 | 0.6856 | 0.6957 | 0.6906 | 608 | 0.9686 | 0.9591 | 0.9638 | 611 | 0.7778 | 0.7778 | 0.7778 | 558 | 0.9444 | 0.9680 | 0.9561 | 562 | 0.8378 | 0.8490 | 0.8434 | 0.9933 |
| 0.0157 | 6.0 | 4146 | 0.0176 | 0.6846 | 0.7007 | 0.6926 | 598 | 0.9934 | 0.9983 | 0.9958 | 599 | 0.6852 | 0.7089 | 0.6968 | 608 | 0.9581 | 0.9738 | 0.9659 | 611 | 0.7875 | 0.7903 | 0.7889 | 558 | 0.9786 | 0.9751 | 0.9768 | 562 | 0.8460 | 0.8575 | 0.8517 | 0.9936 |
| 0.0144 | 7.0 | 4837 | 0.0176 | 0.6228 | 0.6656 | 0.6435 | 598 | 0.9950 | 0.9983 | 0.9967 | 599 | 0.6667 | 0.6974 | 0.6817 | 608 | 0.9677 | 0.9804 | 0.9740 | 611 | 0.7718 | 0.7760 | 0.7739 | 558 | 0.9658 | 0.9555 | 0.9606 | 562 | 0.8275 | 0.8453 | 0.8363 | 0.9931 |
| 0.0134 | 8.0 | 5528 | 0.0172 | 0.6551 | 0.6923 | 0.6732 | 598 | 0.9933 | 0.9967 | 0.995 | 599 | 0.6900 | 0.7138 | 0.7017 | 608 | 0.9741 | 0.9853 | 0.9797 | 611 | 0.7715 | 0.7867 | 0.7791 | 558 | 0.9713 | 0.9644 | 0.9679 | 562 | 0.8395 | 0.8563 | 0.8478 | 0.9937 |
| 0.0125 | 9.0 | 6219 | 0.0167 | 0.6571 | 0.6890 | 0.6727 | 598 | 0.9934 | 0.9983 | 0.9958 | 599 | 0.7 | 0.7138 | 0.7068 | 608 | 0.9676 | 0.9771 | 0.9723 | 611 | 0.7786 | 0.7814 | 0.7800 | 558 | 0.9715 | 0.9698 | 0.9706 | 562 | 0.8425 | 0.8546 | 0.8485 | 0.9936 |
| 0.0118 | 10.0 | 6910 | 0.0170 | 0.6683 | 0.6839 | 0.6760 | 598 | 0.9934 | 0.9983 | 0.9958 | 599 | 0.6949 | 0.7155 | 0.7050 | 608 | 0.9804 | 0.9820 | 0.9812 | 611 | 0.7583 | 0.7760 | 0.7671 | 558 | 0.9751 | 0.9751 | 0.9751 | 562 | 0.8432 | 0.8549 | 0.8490 | 0.9936 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.2.2
- Tokenizers 0.13.3
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