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# vgm_model_0.2

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0477
- Precision: 0.8
- Recall: 0.7304
- F1: 0.7636
- Accuracy: 0.9935

## 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.33  | 100  | 0.0826          | 0.1538    | 0.0348 | 0.0567 | 0.9783   |
| No log        | 2.67  | 200  | 0.0633          | 0.4907    | 0.4609 | 0.4753 | 0.9859   |
| No log        | 4.0   | 300  | 0.0433          | 0.7358    | 0.6783 | 0.7059 | 0.9927   |
| No log        | 5.33  | 400  | 0.0412          | 0.76      | 0.6609 | 0.7070 | 0.9916   |
| 0.0937        | 6.67  | 500  | 0.0390          | 0.6885    | 0.7304 | 0.7089 | 0.9919   |
| 0.0937        | 8.0   | 600  | 0.0400          | 0.7177    | 0.7739 | 0.7448 | 0.9914   |
| 0.0937        | 9.33  | 700  | 0.0457          | 0.7619    | 0.6957 | 0.7273 | 0.9924   |
| 0.0937        | 10.67 | 800  | 0.0370          | 0.7154    | 0.8087 | 0.7592 | 0.9922   |
| 0.0937        | 12.0  | 900  | 0.0369          | 0.7759    | 0.7826 | 0.7792 | 0.9945   |
| 0.0105        | 13.33 | 1000 | 0.0373          | 0.7672    | 0.7739 | 0.7706 | 0.9940   |
| 0.0105        | 14.67 | 1100 | 0.0419          | 0.8190    | 0.7478 | 0.7818 | 0.9940   |
| 0.0105        | 16.0  | 1200 | 0.0396          | 0.8018    | 0.7739 | 0.7876 | 0.9945   |
| 0.0105        | 17.33 | 1300 | 0.0428          | 0.7568    | 0.7304 | 0.7434 | 0.9940   |
| 0.0105        | 18.67 | 1400 | 0.0450          | 0.7522    | 0.7391 | 0.7456 | 0.9940   |
| 0.003         | 20.0  | 1500 | 0.0397          | 0.7541    | 0.8    | 0.7764 | 0.9937   |
| 0.003         | 21.33 | 1600 | 0.0415          | 0.8349    | 0.7913 | 0.8125 | 0.9948   |
| 0.003         | 22.67 | 1700 | 0.0427          | 0.7739    | 0.7739 | 0.7739 | 0.9945   |
| 0.003         | 24.0  | 1800 | 0.0455          | 0.7727    | 0.7391 | 0.7556 | 0.9935   |
| 0.003         | 25.33 | 1900 | 0.0464          | 0.7830    | 0.7217 | 0.7511 | 0.9932   |
| 0.0016        | 26.67 | 2000 | 0.0477          | 0.8       | 0.7304 | 0.7636 | 0.9935   |


### 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"], "datasets": ["sroie"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "vgm_model_0.2", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.8, "name": "Precision"}, {"type": "recall", "value": 0.7304347826086957, "name": "Recall"}, {"type": "f1", "value": 0.7636363636363637, "name": "F1"}, {"type": "accuracy", "value": 0.993474288697468, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard layoutlmv3 token-classification generated_from_trainer dataset:sroie license:cc-by-nc-sa-4.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

vgm\_model\_0.2
===============


This model is a fine-tuned version of microsoft/layoutlmv3-base on the sroie dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0477
* Precision: 0.8
* Recall: 0.7304
* F1: 0.7636
* Accuracy: 0.9935


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: 1e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2000


### 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: 1e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2000 ### 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 #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2000 ### 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 #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2000### Training results### Framework versions


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