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# 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

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "lmv2-g-rai-auth-02-14", "results": []}]}
transformers pytorch tensorboard layoutlmv2 token-classification generated_from_trainer license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

lmv2-g-rai-auth-02-14
=====================


This model is a fine-tuned version of 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



### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.2.2
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.2.2
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.2.2
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.2.2
* Tokenizers 0.13.2