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# perioli_manifesti_v5.8

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:
- F1: 0.9952
- Loss: 0.0315
- Overall Precision: 0.9623
- Overall Recall: 0.9713
- Overall F1: 0.9668
- Overall Accuracy: 0.9952
- Container id: {'precision': 0.9936708860759493, 'recall': 0.98125, 'f1': 0.9874213836477987, 'number': 160}
- Seal number: {'precision': 0.9820359281437125, 'recall': 0.9879518072289156, 'f1': 0.984984984984985, 'number': 166}
- Container quantity: {'precision': 0.978021978021978, 'recall': 0.9888888888888889, 'f1': 0.9834254143646408, 'number': 90}
- Container type: {'precision': 0.974025974025974, 'recall': 0.9803921568627451, 'f1': 0.977198697068404, 'number': 153}
- Tare: {'precision': 0.967741935483871, 'recall': 0.9917355371900827, 'f1': 0.979591836734694, 'number': 121}
- Package quantity: {'precision': 0.9195402298850575, 'recall': 0.9696969696969697, 'f1': 0.943952802359882, 'number': 165}
- Weight: {'precision': 0.96875, 'recall': 0.9763779527559056, 'f1': 0.9725490196078432, 'number': 127}
- Others: {'precision': 0.9547945205479452, 'recall': 0.9574175824175825, 'f1': 0.9561042524005487, 'number': 728}

## 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: 4000

### Training results

| Training Loss | Epoch | Step | F1     | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Container id                                                                                  | Seal number                                                                                              | Container quantity                                                                                      | Container type                                                                                           | Tare                                                                                                     | Package quantity                                                                                         | Weight                                                                                                   | Others                                                                                                   |
|:-------------:|:-----:|:----:|:------:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:---------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|
| 0.1271        | 2.34  | 500  | 0.9951 | 0.0246          | 0.9605            | 0.9667         | 0.9636     | 0.9951           | {'precision': 0.98125, 'recall': 0.98125, 'f1': 0.98125, 'number': 160}                       | {'precision': 0.9939024390243902, 'recall': 0.9819277108433735, 'f1': 0.9878787878787879, 'number': 166} | {'precision': 1.0, 'recall': 0.9888888888888889, 'f1': 0.9944134078212291, 'number': 90}                | {'precision': 0.9738562091503268, 'recall': 0.9738562091503268, 'f1': 0.9738562091503268, 'number': 153} | {'precision': 0.9516129032258065, 'recall': 0.9752066115702479, 'f1': 0.963265306122449, 'number': 121}  | {'precision': 0.930635838150289, 'recall': 0.9757575757575757, 'f1': 0.9526627218934911, 'number': 165}  | {'precision': 0.952755905511811, 'recall': 0.952755905511811, 'f1': 0.952755905511811, 'number': 127}    | {'precision': 0.9507523939808481, 'recall': 0.9546703296703297, 'f1': 0.9527073337902673, 'number': 728} |
| 0.016         | 4.67  | 1000 | 0.9953 | 0.0255          | 0.9604            | 0.9643         | 0.9624     | 0.9953           | {'precision': 0.9874213836477987, 'recall': 0.98125, 'f1': 0.9843260188087775, 'number': 160} | {'precision': 0.9939393939393939, 'recall': 0.9879518072289156, 'f1': 0.9909365558912386, 'number': 166} | {'precision': 0.9888888888888889, 'recall': 0.9888888888888889, 'f1': 0.9888888888888889, 'number': 90} | {'precision': 0.9671052631578947, 'recall': 0.9607843137254902, 'f1': 0.9639344262295081, 'number': 153} | {'precision': 0.9669421487603306, 'recall': 0.9669421487603306, 'f1': 0.9669421487603306, 'number': 121} | {'precision': 0.9137931034482759, 'recall': 0.9636363636363636, 'f1': 0.9380530973451328, 'number': 165} | {'precision': 0.976, 'recall': 0.9606299212598425, 'f1': 0.9682539682539683, 'number': 127}              | {'precision': 0.9493844049247606, 'recall': 0.9532967032967034, 'f1': 0.9513365318711446, 'number': 728} |
| 0.0093        | 7.01  | 1500 | 0.9960 | 0.0232          | 0.9668            | 0.9708         | 0.9688     | 0.9960           | {'precision': 0.9874213836477987, 'recall': 0.98125, 'f1': 0.9843260188087775, 'number': 160} | {'precision': 0.9939393939393939, 'recall': 0.9879518072289156, 'f1': 0.9909365558912386, 'number': 166} | {'precision': 1.0, 'recall': 0.9888888888888889, 'f1': 0.9944134078212291, 'number': 90}                | {'precision': 0.9803921568627451, 'recall': 0.9803921568627451, 'f1': 0.9803921568627451, 'number': 153} | {'precision': 0.9596774193548387, 'recall': 0.9834710743801653, 'f1': 0.9714285714285714, 'number': 121} | {'precision': 0.9352941176470588, 'recall': 0.9636363636363636, 'f1': 0.9492537313432835, 'number': 165} | {'precision': 0.9612403100775194, 'recall': 0.9763779527559056, 'f1': 0.9687500000000001, 'number': 127} | {'precision': 0.9587912087912088, 'recall': 0.9587912087912088, 'f1': 0.9587912087912088, 'number': 728} |
| 0.0053        | 9.35  | 2000 | 0.9950 | 0.0296          | 0.9578            | 0.9696         | 0.9637     | 0.9950           | {'precision': 0.96875, 'recall': 0.96875, 'f1': 0.96875, 'number': 160}                       | {'precision': 0.9939759036144579, 'recall': 0.9939759036144579, 'f1': 0.9939759036144579, 'number': 166} | {'precision': 1.0, 'recall': 0.9888888888888889, 'f1': 0.9944134078212291, 'number': 90}                | {'precision': 0.9803921568627451, 'recall': 0.9803921568627451, 'f1': 0.9803921568627451, 'number': 153} | {'precision': 0.967741935483871, 'recall': 0.9917355371900827, 'f1': 0.979591836734694, 'number': 121}   | {'precision': 0.9090909090909091, 'recall': 0.9696969696969697, 'f1': 0.93841642228739, 'number': 165}   | {'precision': 0.96875, 'recall': 0.9763779527559056, 'f1': 0.9725490196078432, 'number': 127}            | {'precision': 0.9455782312925171, 'recall': 0.9546703296703297, 'f1': 0.9501025290498976, 'number': 728} |
| 0.0046        | 11.68 | 2500 | 0.9955 | 0.0291          | 0.9668            | 0.9719         | 0.9694     | 0.9955           | {'precision': 0.9874213836477987, 'recall': 0.98125, 'f1': 0.9843260188087775, 'number': 160} | {'precision': 0.9939393939393939, 'recall': 0.9879518072289156, 'f1': 0.9909365558912386, 'number': 166} | {'precision': 1.0, 'recall': 0.9888888888888889, 'f1': 0.9944134078212291, 'number': 90}                | {'precision': 0.9803921568627451, 'recall': 0.9803921568627451, 'f1': 0.9803921568627451, 'number': 153} | {'precision': 0.9758064516129032, 'recall': 1.0, 'f1': 0.9877551020408163, 'number': 121}                | {'precision': 0.9190751445086706, 'recall': 0.9636363636363636, 'f1': 0.9408284023668639, 'number': 165} | {'precision': 0.9763779527559056, 'recall': 0.9763779527559056, 'f1': 0.9763779527559056, 'number': 127} | {'precision': 0.9574759945130316, 'recall': 0.9587912087912088, 'f1': 0.9581331503088538, 'number': 728} |
| 0.0028        | 14.02 | 3000 | 0.9948 | 0.0322          | 0.9578            | 0.9678         | 0.9628     | 0.9948           | {'precision': 0.9936708860759493, 'recall': 0.98125, 'f1': 0.9874213836477987, 'number': 160} | {'precision': 0.9879518072289156, 'recall': 0.9879518072289156, 'f1': 0.9879518072289156, 'number': 166} | {'precision': 0.978021978021978, 'recall': 0.9888888888888889, 'f1': 0.9834254143646408, 'number': 90}  | {'precision': 0.974025974025974, 'recall': 0.9803921568627451, 'f1': 0.977198697068404, 'number': 153}   | {'precision': 0.975609756097561, 'recall': 0.9917355371900827, 'f1': 0.9836065573770492, 'number': 121}  | {'precision': 0.8977272727272727, 'recall': 0.9575757575757575, 'f1': 0.9266862170087976, 'number': 165} | {'precision': 0.9609375, 'recall': 0.968503937007874, 'f1': 0.9647058823529412, 'number': 127}           | {'precision': 0.9480874316939891, 'recall': 0.9532967032967034, 'f1': 0.9506849315068493, 'number': 728} |
| 0.0017        | 16.36 | 3500 | 0.9955 | 0.0296          | 0.9652            | 0.9719         | 0.9685     | 0.9955           | {'precision': 0.9936708860759493, 'recall': 0.98125, 'f1': 0.9874213836477987, 'number': 160} | {'precision': 0.9820359281437125, 'recall': 0.9879518072289156, 'f1': 0.984984984984985, 'number': 166}  | {'precision': 0.978021978021978, 'recall': 0.9888888888888889, 'f1': 0.9834254143646408, 'number': 90}  | {'precision': 0.974025974025974, 'recall': 0.9803921568627451, 'f1': 0.977198697068404, 'number': 153}   | {'precision': 0.9836065573770492, 'recall': 0.9917355371900827, 'f1': 0.9876543209876544, 'number': 121} | {'precision': 0.930635838150289, 'recall': 0.9757575757575757, 'f1': 0.9526627218934911, 'number': 165}  | {'precision': 0.9682539682539683, 'recall': 0.9606299212598425, 'f1': 0.9644268774703557, 'number': 127} | {'precision': 0.9562243502051984, 'recall': 0.9601648351648352, 'f1': 0.9581905414667581, 'number': 728} |
| 0.0011        | 18.69 | 4000 | 0.9952 | 0.0315          | 0.9623            | 0.9713         | 0.9668     | 0.9952           | {'precision': 0.9936708860759493, 'recall': 0.98125, 'f1': 0.9874213836477987, 'number': 160} | {'precision': 0.9820359281437125, 'recall': 0.9879518072289156, 'f1': 0.984984984984985, 'number': 166}  | {'precision': 0.978021978021978, 'recall': 0.9888888888888889, 'f1': 0.9834254143646408, 'number': 90}  | {'precision': 0.974025974025974, 'recall': 0.9803921568627451, 'f1': 0.977198697068404, 'number': 153}   | {'precision': 0.967741935483871, 'recall': 0.9917355371900827, 'f1': 0.979591836734694, 'number': 121}   | {'precision': 0.9195402298850575, 'recall': 0.9696969696969697, 'f1': 0.943952802359882, 'number': 165}  | {'precision': 0.96875, 'recall': 0.9763779527559056, 'f1': 0.9725490196078432, 'number': 127}            | {'precision': 0.9547945205479452, 'recall': 0.9574175824175825, 'f1': 0.9561042524005487, 'number': 728} |


### 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": ["f1"], "model-index": [{"name": "perioli_manifesti_v5.8", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "f1", "value": 0.9951893095768374, "name": "F1"}]}]}]}
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\_v5.8
========================


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


* F1: 0.9952
* Loss: 0.0315
* Overall Precision: 0.9623
* Overall Recall: 0.9713
* Overall F1: 0.9668
* Overall Accuracy: 0.9952
* Container id: {'precision': 0.9936708860759493, 'recall': 0.98125, 'f1': 0.9874213836477987, 'number': 160}
* Seal number: {'precision': 0.9820359281437125, 'recall': 0.9879518072289156, 'f1': 0.984984984984985, 'number': 166}
* Container quantity: {'precision': 0.978021978021978, 'recall': 0.9888888888888889, 'f1': 0.9834254143646408, 'number': 90}
* Container type: {'precision': 0.974025974025974, 'recall': 0.9803921568627451, 'f1': 0.977198697068404, 'number': 153}
* Tare: {'precision': 0.967741935483871, 'recall': 0.9917355371900827, 'f1': 0.979591836734694, 'number': 121}
* Package quantity: {'precision': 0.9195402298850575, 'recall': 0.9696969696969697, 'f1': 0.943952802359882, 'number': 165}
* Weight: {'precision': 0.96875, 'recall': 0.9763779527559056, 'f1': 0.9725490196078432, 'number': 127}
* Others: {'precision': 0.9547945205479452, 'recall': 0.9574175824175825, 'f1': 0.9561042524005487, 'number': 728}


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: 4000


### 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: 4000 ### 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: 4000 ### Training results ### Framework versions


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