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# 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

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "lmv2-g-rai-aRx-refill-230427", "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-aRx-refill-230427
============================


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: 0.0176
* 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



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3



### 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 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3
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 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3
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 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3