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# perioli_manifesti_v5.1

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: 0.0301
- Precision: 0.9215
- Recall: 0.9497
- F1: 0.9354
- Accuracy: 0.9922

## 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: 2700

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.48  | 100  | 0.1283          | 0.7806    | 0.8086 | 0.7944 | 0.9734   |
| No log        | 0.96  | 200  | 0.0539          | 0.8832    | 0.9102 | 0.8965 | 0.9865   |
| No log        | 1.44  | 300  | 0.0451          | 0.9001    | 0.9389 | 0.9191 | 0.9893   |
| No log        | 1.92  | 400  | 0.0395          | 0.9058    | 0.9326 | 0.9190 | 0.9890   |
| 0.1374        | 2.4   | 500  | 0.0312          | 0.9269    | 0.9461 | 0.9364 | 0.9926   |
| 0.1374        | 2.88  | 600  | 0.0306          | 0.9230    | 0.9587 | 0.9405 | 0.9925   |
| 0.1374        | 3.37  | 700  | 0.0260          | 0.9356    | 0.9659 | 0.9505 | 0.9939   |
| 0.1374        | 3.85  | 800  | 0.0331          | 0.9244    | 0.9668 | 0.9451 | 0.9928   |
| 0.1374        | 4.33  | 900  | 0.0296          | 0.9099    | 0.9524 | 0.9306 | 0.9909   |
| 0.0233        | 4.81  | 1000 | 0.0235          | 0.9379    | 0.9632 | 0.9504 | 0.9943   |
| 0.0233        | 5.29  | 1100 | 0.0200          | 0.9407    | 0.9695 | 0.9549 | 0.9948   |
| 0.0233        | 5.77  | 1200 | 0.0263          | 0.9233    | 0.9632 | 0.9428 | 0.9929   |
| 0.0233        | 6.25  | 1300 | 0.0267          | 0.9291    | 0.9650 | 0.9467 | 0.9933   |
| 0.0233        | 6.73  | 1400 | 0.0243          | 0.9348    | 0.9668 | 0.9505 | 0.9941   |
| 0.0147        | 7.21  | 1500 | 0.0260          | 0.9280    | 0.9614 | 0.9444 | 0.9934   |
| 0.0147        | 7.69  | 1600 | 0.0256          | 0.9311    | 0.9596 | 0.9451 | 0.9934   |
| 0.0147        | 8.17  | 1700 | 0.0246          | 0.9291    | 0.9542 | 0.9415 | 0.9930   |
| 0.0147        | 8.65  | 1800 | 0.0242          | 0.9364    | 0.9650 | 0.9504 | 0.9941   |
| 0.0147        | 9.13  | 1900 | 0.0266          | 0.9268    | 0.9560 | 0.9412 | 0.9928   |
| 0.0107        | 9.62  | 2000 | 0.0300          | 0.9296    | 0.9605 | 0.9448 | 0.9932   |
| 0.0107        | 10.1  | 2100 | 0.0277          | 0.9365    | 0.9677 | 0.9518 | 0.9941   |
| 0.0107        | 10.58 | 2200 | 0.0292          | 0.9324    | 0.9668 | 0.9493 | 0.9937   |
| 0.0107        | 11.06 | 2300 | 0.0247          | 0.9284    | 0.9560 | 0.9420 | 0.9930   |
| 0.0107        | 11.54 | 2400 | 0.0298          | 0.9191    | 0.9488 | 0.9337 | 0.9921   |
| 0.0067        | 12.02 | 2500 | 0.0303          | 0.9215    | 0.9497 | 0.9354 | 0.9922   |
| 0.0067        | 12.5  | 2600 | 0.0305          | 0.9234    | 0.9533 | 0.9381 | 0.9925   |
| 0.0067        | 12.98 | 2700 | 0.0301          | 0.9215    | 0.9497 | 0.9354 | 0.9922   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+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_manifesti_v5.1", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.9215344376634699, "name": "Precision"}, {"type": "recall", "value": 0.949685534591195, "name": "Recall"}, {"type": "f1", "value": 0.9353982300884957, "name": "F1"}, {"type": "accuracy", "value": 0.9921951219512195, "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\_manifesti\_v5.1
========================


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: 0.0301
* Precision: 0.9215
* Recall: 0.9497
* F1: 0.9354
* Accuracy: 0.9922


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: 2700


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+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: 2700 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+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: 2700 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.2.2
* Tokenizers 0.13.3