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# perioli_vgm_v5.2

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: 0.0093
- Precision: 0.9702
- Recall: 0.9540
- F1: 0.9620
- 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: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.53  | 100  | 0.0787          | 0.5595    | 0.3933 | 0.4619 | 0.9811   |
| No log        | 1.06  | 200  | 0.0450          | 0.8069    | 0.6820 | 0.7392 | 0.9909   |
| No log        | 1.6   | 300  | 0.0438          | 0.6469    | 0.8201 | 0.7232 | 0.9874   |
| No log        | 2.13  | 400  | 0.0230          | 0.8063    | 0.8536 | 0.8293 | 0.9935   |
| 0.0742        | 2.66  | 500  | 0.0177          | 0.8214    | 0.8661 | 0.8432 | 0.9946   |
| 0.0742        | 3.19  | 600  | 0.0251          | 0.8295    | 0.8954 | 0.8612 | 0.9943   |
| 0.0742        | 3.72  | 700  | 0.0167          | 0.8782    | 0.8745 | 0.8763 | 0.9961   |
| 0.0742        | 4.26  | 800  | 0.0183          | 0.8510    | 0.9079 | 0.8785 | 0.9949   |
| 0.0742        | 4.79  | 900  | 0.0100          | 0.9481    | 0.9163 | 0.9319 | 0.9969   |
| 0.0085        | 5.32  | 1000 | 0.0153          | 0.9048    | 0.9540 | 0.9287 | 0.9967   |
| 0.0085        | 5.85  | 1100 | 0.0112          | 0.9253    | 0.9331 | 0.9292 | 0.9972   |
| 0.0085        | 6.38  | 1200 | 0.0120          | 0.9184    | 0.9414 | 0.9298 | 0.9971   |
| 0.0085        | 6.91  | 1300 | 0.0072          | 0.9417    | 0.9456 | 0.9436 | 0.9980   |
| 0.0085        | 7.45  | 1400 | 0.0102          | 0.9617    | 0.9456 | 0.9536 | 0.9977   |
| 0.0029        | 7.98  | 1500 | 0.0101          | 0.9702    | 0.9540 | 0.9620 | 0.9980   |
| 0.0029        | 8.51  | 1600 | 0.0098          | 0.9580    | 0.9540 | 0.9560 | 0.9979   |
| 0.0029        | 9.04  | 1700 | 0.0075          | 0.9536    | 0.9456 | 0.9496 | 0.9980   |
| 0.0029        | 9.57  | 1800 | 0.0083          | 0.9702    | 0.9540 | 0.9620 | 0.9980   |
| 0.0029        | 10.11 | 1900 | 0.0093          | 0.9617    | 0.9456 | 0.9536 | 0.9979   |
| 0.001         | 10.64 | 2000 | 0.0093          | 0.9702    | 0.9540 | 0.9620 | 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_v5.2", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.9702127659574468, "name": "Precision"}, {"type": "recall", "value": 0.9539748953974896, "name": "Recall"}, {"type": "f1", "value": 0.9620253164556961, "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\_v5.2
==================


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: 0.0093
* Precision: 0.9702
* Recall: 0.9540
* F1: 0.9620
* 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: 2000


### 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: 2000 ### 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: 2000 ### Training results ### Framework versions


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