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# lmv2-g-aadhaar-236doc-06-14

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: 0.0427
- Aadhaar Precision: 0.9783
- Aadhaar Recall: 1.0
- Aadhaar F1: 0.9890
- Aadhaar Number: 45
- Dob Precision: 0.9787
- Dob Recall: 1.0
- Dob F1: 0.9892
- Dob Number: 46
- Gender Precision: 1.0
- Gender Recall: 0.9787
- Gender F1: 0.9892
- Gender Number: 47
- Name Precision: 0.9574
- Name Recall: 0.9375
- Name F1: 0.9474
- Name Number: 48
- Overall Precision: 0.9785
- Overall Recall: 0.9785
- Overall F1: 0.9785
- Overall Accuracy: 0.9939

## 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: 4e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Aadhaar Precision | Aadhaar Recall | Aadhaar F1 | Aadhaar Number | Dob Precision | Dob Recall | Dob F1 | Dob Number | Gender Precision | Gender Recall | Gender F1 | Gender Number | Name Precision | Name Recall | Name F1 | Name Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-------------:|:----------:|:------:|:----------:|:----------------:|:-------------:|:---------:|:-------------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.0024        | 1.0   | 188  | 0.5819          | 0.9348            | 0.9556         | 0.9451     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9574        | 0.9783    | 47            | 0.5172         | 0.625       | 0.5660  | 48          | 0.8410            | 0.8817         | 0.8609     | 0.9744           |
| 0.4484        | 2.0   | 376  | 0.3263          | 0.8980            | 0.9778         | 0.9362     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.6842         | 0.8125      | 0.7429  | 48          | 0.8838            | 0.9409         | 0.9115     | 0.9733           |
| 0.2508        | 3.0   | 564  | 0.2230          | 0.9318            | 0.9111         | 0.9213     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.8913         | 0.8542      | 0.8723  | 48          | 0.9560            | 0.9355         | 0.9457     | 0.9811           |
| 0.165         | 4.0   | 752  | 0.1728          | 0.9362            | 0.9778         | 0.9565     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.8444         | 0.7917      | 0.8172  | 48          | 0.9457            | 0.9355         | 0.9405     | 0.9844           |
| 0.1081        | 5.0   | 940  | 0.0987          | 0.8958            | 0.9556         | 0.9247     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 1.0            | 0.9167      | 0.9565  | 48          | 0.9728            | 0.9624         | 0.9676     | 0.9928           |
| 0.0834        | 6.0   | 1128 | 0.0984          | 0.8980            | 0.9778         | 0.9362     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9574        | 0.9783    | 47            | 0.8148         | 0.9167      | 0.8627  | 48          | 0.9227            | 0.9624         | 0.9421     | 0.9833           |
| 0.0676        | 7.0   | 1316 | 0.0773          | 0.9362            | 0.9778         | 0.9565     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.9111         | 0.8542      | 0.8817  | 48          | 0.9620            | 0.9516         | 0.9568     | 0.9894           |
| 0.0572        | 8.0   | 1504 | 0.0786          | 0.8235            | 0.9333         | 0.8750     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.8936         | 0.875       | 0.8842  | 48          | 0.9263            | 0.9462         | 0.9362     | 0.9872           |
| 0.0481        | 9.0   | 1692 | 0.0576          | 0.9375            | 1.0            | 0.9677     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.9362         | 0.9167      | 0.9263  | 48          | 0.9679            | 0.9731         | 0.9705     | 0.99             |
| 0.0349        | 10.0  | 1880 | 0.0610          | 0.9574            | 1.0            | 0.9783     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.8958         | 0.8958      | 0.8958  | 48          | 0.9626            | 0.9677         | 0.9651     | 0.9894           |
| 0.0287        | 11.0  | 2068 | 0.0978          | 0.9091            | 0.8889         | 0.8989     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.9348         | 0.8958      | 0.9149  | 48          | 0.9615            | 0.9409         | 0.9511     | 0.985            |
| 0.0297        | 12.0  | 2256 | 0.0993          | 0.9375            | 1.0            | 0.9677     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.7959         | 0.8125      | 0.8041  | 48          | 0.9312            | 0.9462         | 0.9387     | 0.9833           |
| 0.0395        | 13.0  | 2444 | 0.0824          | 0.9362            | 0.9778         | 0.9565     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.875          | 0.875       | 0.875   | 48          | 0.9519            | 0.9570         | 0.9544     | 0.9872           |
| 0.0333        | 14.0  | 2632 | 0.0788          | 0.8913            | 0.9111         | 0.9011     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.9556         | 0.8958      | 0.9247  | 48          | 0.9617            | 0.9462         | 0.9539     | 0.9867           |
| 0.0356        | 15.0  | 2820 | 0.0808          | 0.84              | 0.9333         | 0.8842     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.9565         | 0.9167      | 0.9362  | 48          | 0.9468            | 0.9570         | 0.9519     | 0.9867           |
| 0.0192        | 16.0  | 3008 | 0.0955          | 0.8462            | 0.9778         | 0.9072     | 45             | 0.9787        | 1.0        | 0.9892 | 46         | 0.9583           | 0.9787        | 0.9684    | 47            | 0.9070         | 0.8125      | 0.8571  | 48          | 0.9211            | 0.9409         | 0.9309     | 0.9822           |
| 0.016         | 17.0  | 3196 | 0.0936          | 0.9130            | 0.9333         | 0.9231     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.9318         | 0.8542      | 0.8913  | 48          | 0.9615            | 0.9409         | 0.9511     | 0.9867           |
| 0.0218        | 18.0  | 3384 | 0.1009          | 0.9545            | 0.9333         | 0.9438     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.8571         | 0.875       | 0.8660  | 48          | 0.9514            | 0.9462         | 0.9488     | 0.9844           |
| 0.0165        | 19.0  | 3572 | 0.0517          | 0.9574            | 1.0            | 0.9783     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.9333         | 0.875       | 0.9032  | 48          | 0.9728            | 0.9624         | 0.9676     | 0.9906           |
| 0.0198        | 20.0  | 3760 | 0.0890          | 0.9167            | 0.9778         | 0.9462     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.9149         | 0.8958      | 0.9053  | 48          | 0.9572            | 0.9624         | 0.9598     | 0.9867           |
| 0.0077        | 21.0  | 3948 | 0.0835          | 0.9574            | 1.0            | 0.9783     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.88           | 0.9167      | 0.8980  | 48          | 0.9577            | 0.9731         | 0.9653     | 0.9872           |
| 0.0088        | 22.0  | 4136 | 0.0427          | 0.9783            | 1.0            | 0.9890     | 45             | 0.9787        | 1.0        | 0.9892 | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.9574         | 0.9375      | 0.9474  | 48          | 0.9785            | 0.9785         | 0.9785     | 0.9939           |
| 0.0078        | 23.0  | 4324 | 0.0597          | 0.9574            | 1.0            | 0.9783     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.8654         | 0.9375      | 0.9     | 48          | 0.9529            | 0.9785         | 0.9655     | 0.9889           |
| 0.0178        | 24.0  | 4512 | 0.0524          | 0.9574            | 1.0            | 0.9783     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 1.0            | 0.875       | 0.9333  | 48          | 0.9890            | 0.9624         | 0.9755     | 0.9922           |
| 0.012         | 25.0  | 4700 | 0.0637          | 0.9375            | 1.0            | 0.9677     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.8491         | 0.9375      | 0.8911  | 48          | 0.9430            | 0.9785         | 0.9604     | 0.9867           |
| 0.0135        | 26.0  | 4888 | 0.0668          | 0.9184            | 1.0            | 0.9574     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.86           | 0.8958      | 0.8776  | 48          | 0.9424            | 0.9677         | 0.9549     | 0.9867           |
| 0.0123        | 27.0  | 5076 | 0.0713          | 0.9565            | 0.9778         | 0.9670     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.9375         | 0.9375      | 0.9375  | 48          | 0.9731            | 0.9731         | 0.9731     | 0.9911           |
| 0.0074        | 28.0  | 5264 | 0.0675          | 0.9362            | 0.9778         | 0.9565     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.9            | 0.9375      | 0.9184  | 48          | 0.9577            | 0.9731         | 0.9653     | 0.99             |
| 0.0051        | 29.0  | 5452 | 0.0713          | 0.9362            | 0.9778         | 0.9565     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.9167         | 0.9167      | 0.9167  | 48          | 0.9626            | 0.9677         | 0.9651     | 0.9906           |
| 0.0027        | 30.0  | 5640 | 0.0725          | 0.9362            | 0.9778         | 0.9565     | 45             | 1.0           | 1.0        | 1.0    | 46         | 1.0              | 0.9787        | 0.9892    | 47            | 0.9167         | 0.9167      | 0.9167  | 48          | 0.9626            | 0.9677         | 0.9651     | 0.9906           |


### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "lmv2-g-aadhaar-236doc-06-14", "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-aadhaar-236doc-06-14
===========================


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: 0.0427
* Aadhaar Precision: 0.9783
* Aadhaar Recall: 1.0
* Aadhaar F1: 0.9890
* Aadhaar Number: 45
* Dob Precision: 0.9787
* Dob Recall: 1.0
* Dob F1: 0.9892
* Dob Number: 46
* Gender Precision: 1.0
* Gender Recall: 0.9787
* Gender F1: 0.9892
* Gender Number: 47
* Name Precision: 0.9574
* Name Recall: 0.9375
* Name F1: 0.9474
* Name Number: 48
* Overall Precision: 0.9785
* Overall Recall: 0.9785
* Overall F1: 0.9785
* Overall Accuracy: 0.9939


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: 4e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* num\_epochs: 30


### Training results



### Framework versions


* Transformers 4.20.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.20.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
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: 4e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.20.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: 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: 4e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* num\_epochs: 30### Training results### Framework versions


* Transformers 4.20.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1