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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: lmv2-g-rai-auth-02-14
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-auth-02-14
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: 0.0368
- Dob Key Precision: 0.5057
- Dob Key Recall: 0.5205
- Dob Key F1: 0.5130
- Dob Key Number: 171
- Dob Value Precision: 0.8071
- Dob Value Recall: 0.9191
- Dob Value F1: 0.8595
- Dob Value Number: 173
- Patient Name Key Precision: 0.6923
- Patient Name Key Recall: 0.7219
- Patient Name Key F1: 0.7068
- Patient Name Key Number: 187
- Patient Name Value Precision: 0.9235
- Patient Name Value Recall: 0.9628
- Patient Name Value F1: 0.9427
- Patient Name Value Number: 188
- Provider Name Key Precision: 0.6930
- Provider Name Key Recall: 0.7065
- Provider Name Key F1: 0.6997
- Provider Name Key Number: 460
- Provider Name Value Precision: 0.9353
- Provider Name Value Recall: 0.9476
- Provider Name Value F1: 0.9414
- Provider Name Value Number: 458
- Overall Precision: 0.7796
- Overall Recall: 0.8082
- Overall F1: 0.7936
- Overall Accuracy: 0.9944
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-------------------:|:----------------:|:------------:|:----------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------------:|:-------------------------:|:---------------------:|:-------------------------:|:---------------------------:|:------------------------:|:--------------------:|:------------------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.1221 | 1.0 | 241 | 0.4373 | 0.0 | 0.0 | 0.0 | 171 | 0.0 | 0.0 | 0.0 | 173 | 0.0 | 0.0 | 0.0 | 187 | 0.0 | 0.0 | 0.0 | 188 | 0.0 | 0.0 | 0.0 | 460 | 0.0 | 0.0 | 0.0 | 458 | 0.0 | 0.0 | 0.0 | 0.9696 |
| 0.258 | 2.0 | 482 | 0.1408 | 0.0385 | 0.0351 | 0.0367 | 171 | 0.9778 | 0.2543 | 0.4037 | 173 | 0.0385 | 0.0053 | 0.0094 | 187 | 0.1739 | 0.0426 | 0.0684 | 188 | 0.0286 | 0.0043 | 0.0075 | 460 | 0.6628 | 0.7424 | 0.7003 | 458 | 0.4685 | 0.2450 | 0.3217 | 0.9782 |
| 0.1066 | 3.0 | 723 | 0.0774 | 0.4011 | 0.4386 | 0.4190 | 171 | 0.8404 | 0.9133 | 0.8753 | 173 | 0.5097 | 0.5615 | 0.5344 | 187 | 0.4804 | 0.7181 | 0.5757 | 188 | 0.5108 | 0.5674 | 0.5376 | 460 | 0.8841 | 0.9323 | 0.9075 | 458 | 0.6255 | 0.7092 | 0.6648 | 0.9920 |
| 0.0685 | 4.0 | 964 | 0.0585 | 0.4229 | 0.4327 | 0.4277 | 171 | 0.8495 | 0.9133 | 0.8802 | 173 | 0.5479 | 0.5508 | 0.5493 | 187 | 0.9005 | 0.9628 | 0.9306 | 188 | 0.6362 | 0.6957 | 0.6646 | 460 | 0.9315 | 0.9498 | 0.9405 | 458 | 0.7390 | 0.7764 | 0.7572 | 0.9938 |
| 0.0532 | 5.0 | 1205 | 0.0486 | 0.4432 | 0.4561 | 0.4496 | 171 | 0.8634 | 0.9133 | 0.8876 | 173 | 0.6862 | 0.6898 | 0.688 | 187 | 0.905 | 0.9628 | 0.9330 | 188 | 0.7106 | 0.7152 | 0.7129 | 460 | 0.9375 | 0.9498 | 0.9436 | 458 | 0.7826 | 0.8002 | 0.7913 | 0.9943 |
| 0.0453 | 6.0 | 1446 | 0.0429 | 0.4277 | 0.4327 | 0.4302 | 171 | 0.8971 | 0.9075 | 0.9023 | 173 | 0.6806 | 0.6952 | 0.6878 | 187 | 0.8835 | 0.9681 | 0.9239 | 188 | 0.7181 | 0.7087 | 0.7133 | 460 | 0.9332 | 0.9454 | 0.9393 | 458 | 0.7829 | 0.7954 | 0.7891 | 0.9943 |
| 0.0392 | 7.0 | 1687 | 0.0392 | 0.4432 | 0.4561 | 0.4496 | 171 | 0.8177 | 0.9075 | 0.8603 | 173 | 0.6875 | 0.7059 | 0.6966 | 187 | 0.9333 | 0.9681 | 0.9504 | 188 | 0.7045 | 0.7152 | 0.7098 | 460 | 0.9353 | 0.9476 | 0.9414 | 458 | 0.7782 | 0.8015 | 0.7896 | 0.9944 |
| 0.0351 | 8.0 | 1928 | 0.0368 | 0.5057 | 0.5205 | 0.5130 | 171 | 0.8071 | 0.9191 | 0.8595 | 173 | 0.6923 | 0.7219 | 0.7068 | 187 | 0.9235 | 0.9628 | 0.9427 | 188 | 0.6930 | 0.7065 | 0.6997 | 460 | 0.9353 | 0.9476 | 0.9414 | 458 | 0.7796 | 0.8082 | 0.7936 | 0.9944 |
| 0.0326 | 9.0 | 2169 | 0.0354 | 0.4375 | 0.4503 | 0.4438 | 171 | 0.8438 | 0.9364 | 0.8877 | 173 | 0.6943 | 0.7166 | 0.7053 | 187 | 0.9235 | 0.9628 | 0.9427 | 188 | 0.7063 | 0.7109 | 0.7086 | 460 | 0.9353 | 0.9476 | 0.9414 | 458 | 0.7809 | 0.8033 | 0.7919 | 0.9944 |
| 0.0313 | 10.0 | 2410 | 0.0350 | 0.4886 | 0.5029 | 0.4957 | 171 | 0.8777 | 0.9538 | 0.9141 | 173 | 0.6959 | 0.7219 | 0.7087 | 187 | 0.9188 | 0.9628 | 0.9403 | 188 | 0.6674 | 0.7022 | 0.6843 | 460 | 0.9333 | 0.9476 | 0.9404 | 458 | 0.7770 | 0.8088 | 0.7926 | 0.9944 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.2.2
- Tokenizers 0.13.2
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