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# perioli_manifesti_v5.7

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: 0.9973
- Loss: 0.0153
- Overall Precision: 0.9793
- Overall Recall: 0.9793
- Overall F1: 0.9793
- Overall Accuracy: 0.9973
- Container id: {'precision': 0.9941176470588236, 'recall': 0.9712643678160919, 'f1': 0.9825581395348837, 'number': 174}
- Seal number: {'precision': 0.994413407821229, 'recall': 1.0, 'f1': 0.9971988795518207, 'number': 178}
- Container quantity: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 72}
- Container type: {'precision': 0.9941860465116279, 'recall': 0.9941860465116279, 'f1': 0.9941860465116279, 'number': 172}
- Tare: {'precision': 0.9712230215827338, 'recall': 0.9574468085106383, 'f1': 0.9642857142857143, 'number': 141}
- Package quantity: {'precision': 0.9651162790697675, 'recall': 0.9880952380952381, 'f1': 0.9764705882352942, 'number': 168}
- Weight: {'precision': 0.9781021897810219, 'recall': 0.9571428571428572, 'f1': 0.9675090252707581, 'number': 140}
- Others: {'precision': 0.971736204576043, 'recall': 0.9769959404600812, 'f1': 0.9743589743589745, 'number': 739}

## 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                                                                                                   |
|:-------------:|:-----:|:----:|:------:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|
| 0.1396        | 2.44  | 500  | 0.9945 | 0.0216          | 0.9652            | 0.9652         | 0.9652     | 0.9945           | {'precision': 0.9939024390243902, 'recall': 0.9367816091954023, 'f1': 0.9644970414201184, 'number': 174} | {'precision': 0.9833333333333333, 'recall': 0.9943820224719101, 'f1': 0.9888268156424581, 'number': 178} | {'precision': 0.935064935064935, 'recall': 1.0, 'f1': 0.9664429530201343, 'number': 72} | {'precision': 0.9710982658959537, 'recall': 0.9767441860465116, 'f1': 0.9739130434782609, 'number': 172} | {'precision': 0.9645390070921985, 'recall': 0.9645390070921985, 'f1': 0.9645390070921985, 'number': 141} | {'precision': 0.9636363636363636, 'recall': 0.9464285714285714, 'f1': 0.9549549549549549, 'number': 168} | {'precision': 0.9635036496350365, 'recall': 0.9428571428571428, 'f1': 0.9530685920577618, 'number': 140} | {'precision': 0.9571619812583668, 'recall': 0.9675236806495264, 'f1': 0.9623149394347242, 'number': 739} |
| 0.0166        | 4.88  | 1000 | 0.9964 | 0.0138          | 0.9737            | 0.9753         | 0.9745     | 0.9964           | {'precision': 0.9884393063583815, 'recall': 0.9827586206896551, 'f1': 0.9855907780979827, 'number': 174} | {'precision': 0.9943820224719101, 'recall': 0.9943820224719101, 'f1': 0.9943820224719101, 'number': 178} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 72}                              | {'precision': 0.9825581395348837, 'recall': 0.9825581395348837, 'f1': 0.9825581395348837, 'number': 172} | {'precision': 0.9710144927536232, 'recall': 0.950354609929078, 'f1': 0.9605734767025089, 'number': 141}  | {'precision': 0.9649122807017544, 'recall': 0.9821428571428571, 'f1': 0.9734513274336283, 'number': 168} | {'precision': 0.9496402877697842, 'recall': 0.9428571428571428, 'f1': 0.9462365591397849, 'number': 140} | {'precision': 0.967741935483871, 'recall': 0.9742895805142084, 'f1': 0.9710047201618341, 'number': 739}  |
| 0.0094        | 7.32  | 1500 | 0.9970 | 0.0136          | 0.9787            | 0.9765         | 0.9776     | 0.9970           | {'precision': 0.9940476190476191, 'recall': 0.9597701149425287, 'f1': 0.976608187134503, 'number': 174}  | {'precision': 0.994413407821229, 'recall': 1.0, 'f1': 0.9971988795518207, 'number': 178}                 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 72}                              | {'precision': 0.9941520467836257, 'recall': 0.9883720930232558, 'f1': 0.9912536443148688, 'number': 172} | {'precision': 0.9710144927536232, 'recall': 0.950354609929078, 'f1': 0.9605734767025089, 'number': 141}  | {'precision': 0.9705882352941176, 'recall': 0.9821428571428571, 'f1': 0.9763313609467456, 'number': 168} | {'precision': 0.9705882352941176, 'recall': 0.9428571428571428, 'f1': 0.9565217391304348, 'number': 140} | {'precision': 0.9705093833780161, 'recall': 0.979702300405954, 'f1': 0.9750841750841751, 'number': 739}  |
| 0.0061        | 9.76  | 2000 | 0.9973 | 0.0120          | 0.9809            | 0.9781         | 0.9795     | 0.9973           | {'precision': 0.9941860465116279, 'recall': 0.9827586206896551, 'f1': 0.9884393063583815, 'number': 174} | {'precision': 0.994413407821229, 'recall': 1.0, 'f1': 0.9971988795518207, 'number': 178}                 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 72}                              | {'precision': 0.9826589595375722, 'recall': 0.9883720930232558, 'f1': 0.9855072463768114, 'number': 172} | {'precision': 0.9640287769784173, 'recall': 0.950354609929078, 'f1': 0.9571428571428572, 'number': 141}  | {'precision': 0.9761904761904762, 'recall': 0.9761904761904762, 'f1': 0.9761904761904762, 'number': 168} | {'precision': 0.9777777777777777, 'recall': 0.9428571428571428, 'f1': 0.9600000000000001, 'number': 140} | {'precision': 0.9770580296896086, 'recall': 0.979702300405954, 'f1': 0.9783783783783784, 'number': 739}  |
| 0.0041        | 12.2  | 2500 | 0.9976 | 0.0119          | 0.9831            | 0.9798         | 0.9815     | 0.9976           | {'precision': 0.9941520467836257, 'recall': 0.9770114942528736, 'f1': 0.9855072463768115, 'number': 174} | {'precision': 0.994413407821229, 'recall': 1.0, 'f1': 0.9971988795518207, 'number': 178}                 | {'precision': 1.0, 'recall': 0.9861111111111112, 'f1': 0.993006993006993, 'number': 72} | {'precision': 0.9883720930232558, 'recall': 0.9883720930232558, 'f1': 0.9883720930232558, 'number': 172} | {'precision': 0.9782608695652174, 'recall': 0.9574468085106383, 'f1': 0.967741935483871, 'number': 141}  | {'precision': 0.9822485207100592, 'recall': 0.9880952380952381, 'f1': 0.9851632047477745, 'number': 168} | {'precision': 0.9781021897810219, 'recall': 0.9571428571428572, 'f1': 0.9675090252707581, 'number': 140} | {'precision': 0.9770580296896086, 'recall': 0.979702300405954, 'f1': 0.9783783783783784, 'number': 739}  |
| 0.0031        | 14.63 | 3000 | 0.9966 | 0.0179          | 0.9732            | 0.9787         | 0.9760     | 0.9966           | {'precision': 0.9941520467836257, 'recall': 0.9770114942528736, 'f1': 0.9855072463768115, 'number': 174} | {'precision': 0.994413407821229, 'recall': 1.0, 'f1': 0.9971988795518207, 'number': 178}                 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 72}                              | {'precision': 0.9770114942528736, 'recall': 0.9883720930232558, 'f1': 0.9826589595375722, 'number': 172} | {'precision': 0.9782608695652174, 'recall': 0.9574468085106383, 'f1': 0.967741935483871, 'number': 141}  | {'precision': 0.9540229885057471, 'recall': 0.9880952380952381, 'f1': 0.9707602339181286, 'number': 168} | {'precision': 0.9710144927536232, 'recall': 0.9571428571428572, 'f1': 0.9640287769784173, 'number': 140} | {'precision': 0.963903743315508, 'recall': 0.9756427604871448, 'f1': 0.9697377269670476, 'number': 739}  |
| 0.0022        | 17.07 | 3500 | 0.9970 | 0.0162          | 0.9759            | 0.9770         | 0.9765     | 0.9970           | {'precision': 0.9941176470588236, 'recall': 0.9712643678160919, 'f1': 0.9825581395348837, 'number': 174} | {'precision': 0.994413407821229, 'recall': 1.0, 'f1': 0.9971988795518207, 'number': 178}                 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 72}                              | {'precision': 0.9826589595375722, 'recall': 0.9883720930232558, 'f1': 0.9855072463768114, 'number': 172} | {'precision': 0.9712230215827338, 'recall': 0.9574468085106383, 'f1': 0.9642857142857143, 'number': 141} | {'precision': 0.9595375722543352, 'recall': 0.9880952380952381, 'f1': 0.9736070381231672, 'number': 168} | {'precision': 0.9779411764705882, 'recall': 0.95, 'f1': 0.963768115942029, 'number': 140}                | {'precision': 0.967741935483871, 'recall': 0.9742895805142084, 'f1': 0.9710047201618341, 'number': 739}  |
| 0.0014        | 19.51 | 4000 | 0.9973 | 0.0153          | 0.9793            | 0.9793         | 0.9793     | 0.9973           | {'precision': 0.9941176470588236, 'recall': 0.9712643678160919, 'f1': 0.9825581395348837, 'number': 174} | {'precision': 0.994413407821229, 'recall': 1.0, 'f1': 0.9971988795518207, 'number': 178}                 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 72}                              | {'precision': 0.9941860465116279, 'recall': 0.9941860465116279, 'f1': 0.9941860465116279, 'number': 172} | {'precision': 0.9712230215827338, 'recall': 0.9574468085106383, 'f1': 0.9642857142857143, 'number': 141} | {'precision': 0.9651162790697675, 'recall': 0.9880952380952381, 'f1': 0.9764705882352942, 'number': 168} | {'precision': 0.9781021897810219, 'recall': 0.9571428571428572, 'f1': 0.9675090252707581, 'number': 140} | {'precision': 0.971736204576043, 'recall': 0.9769959404600812, 'f1': 0.9743589743589745, 'number': 739}  |


### 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_v5.7", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "f1", "value": 0.9973109243697479, "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\_v5.7
========================


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: 0.9973
* Loss: 0.0153
* Overall Precision: 0.9793
* Overall Recall: 0.9793
* Overall F1: 0.9793
* Overall Accuracy: 0.9973
* Container id: {'precision': 0.9941176470588236, 'recall': 0.9712643678160919, 'f1': 0.9825581395348837, 'number': 174}
* Seal number: {'precision': 0.994413407821229, 'recall': 1.0, 'f1': 0.9971988795518207, 'number': 178}
* Container quantity: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 72}
* Container type: {'precision': 0.9941860465116279, 'recall': 0.9941860465116279, 'f1': 0.9941860465116279, 'number': 172}
* Tare: {'precision': 0.9712230215827338, 'recall': 0.9574468085106383, 'f1': 0.9642857142857143, 'number': 141}
* Package quantity: {'precision': 0.9651162790697675, 'recall': 0.9880952380952381, 'f1': 0.9764705882352942, 'number': 168}
* Weight: {'precision': 0.9781021897810219, 'recall': 0.9571428571428572, 'f1': 0.9675090252707581, 'number': 140}
* Others: {'precision': 0.971736204576043, 'recall': 0.9769959404600812, 'f1': 0.9743589743589745, 'number': 739}


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