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# perioli_vgm_v5.4

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: 0.0135
- Precision: 0.9585
- Recall: 0.9665
- F1: 0.9625
- Accuracy: 0.9980

## 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: 2500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.52  | 100  | 0.0894          | 0.5174    | 0.4351 | 0.4727 | 0.9782   |
| No log        | 1.04  | 200  | 0.0414          | 0.6754    | 0.6444 | 0.6595 | 0.9883   |
| No log        | 1.55  | 300  | 0.0413          | 0.6481    | 0.7782 | 0.7072 | 0.9874   |
| No log        | 2.07  | 400  | 0.0187          | 0.7757    | 0.8536 | 0.8127 | 0.9940   |
| 0.0743        | 2.59  | 500  | 0.0121          | 0.8427    | 0.8745 | 0.8583 | 0.9963   |
| 0.0743        | 3.11  | 600  | 0.0178          | 0.8192    | 0.8912 | 0.8537 | 0.9951   |
| 0.0743        | 3.63  | 700  | 0.0166          | 0.8353    | 0.8912 | 0.8623 | 0.9954   |
| 0.0743        | 4.15  | 800  | 0.0119          | 0.8051    | 0.9163 | 0.8571 | 0.9954   |
| 0.0743        | 4.66  | 900  | 0.0122          | 0.9224    | 0.9456 | 0.9339 | 0.9972   |
| 0.0095        | 5.18  | 1000 | 0.0149          | 0.9313    | 0.9079 | 0.9195 | 0.9971   |
| 0.0095        | 5.7   | 1100 | 0.0146          | 0.9578    | 0.9498 | 0.9538 | 0.9979   |
| 0.0095        | 6.22  | 1200 | 0.0164          | 0.9309    | 0.9582 | 0.9443 | 0.9969   |
| 0.0095        | 6.74  | 1300 | 0.0183          | 0.8814    | 0.9331 | 0.9065 | 0.9964   |
| 0.0095        | 7.25  | 1400 | 0.0132          | 0.9540    | 0.9540 | 0.9540 | 0.9979   |
| 0.0025        | 7.77  | 1500 | 0.0142          | 0.9456    | 0.9456 | 0.9456 | 0.9977   |
| 0.0025        | 8.29  | 1600 | 0.0136          | 0.9617    | 0.9456 | 0.9536 | 0.9979   |
| 0.0025        | 8.81  | 1700 | 0.0143          | 0.9494    | 0.9414 | 0.9454 | 0.9977   |
| 0.0025        | 9.33  | 1800 | 0.0144          | 0.9388    | 0.9623 | 0.9504 | 0.9977   |
| 0.0025        | 9.84  | 1900 | 0.0121          | 0.9540    | 0.9540 | 0.9540 | 0.9979   |
| 0.0012        | 10.36 | 2000 | 0.0127          | 0.9707    | 0.9707 | 0.9707 | 0.9982   |
| 0.0012        | 10.88 | 2100 | 0.0140          | 0.9465    | 0.9623 | 0.9544 | 0.9979   |
| 0.0012        | 11.4  | 2200 | 0.0129          | 0.9506    | 0.9665 | 0.9585 | 0.9982   |
| 0.0012        | 11.92 | 2300 | 0.0126          | 0.9506    | 0.9665 | 0.9585 | 0.9982   |
| 0.0012        | 12.44 | 2400 | 0.0140          | 0.9585    | 0.9665 | 0.9625 | 0.9980   |
| 0.0007        | 12.95 | 2500 | 0.0135          | 0.9585    | 0.9665 | 0.9625 | 0.9980   |


### 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"], "datasets": ["sroie"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "perioli_vgm_v5.4", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.9585062240663901, "name": "Precision"}, {"type": "recall", "value": 0.9665271966527197, "name": "Recall"}, {"type": "f1", "value": 0.9625, "name": "F1"}, {"type": "accuracy", "value": 0.9980436909031627, "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 

perioli\_vgm\_v5.4
==================


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: 0.0135
* Precision: 0.9585
* Recall: 0.9665
* F1: 0.9625
* Accuracy: 0.9980


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: 2500


### 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: 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: 2500 ### 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 #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: 2500 ### Training results ### Framework versions


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