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# perioli_manifesti_v5.6_detailed

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: 0.9930
- Loss: 0.0252
- Overall Precision: 0.9354
- Overall Recall: 0.9497
- Overall F1: 0.9425
- Overall Accuracy: 0.9930
- Container id: {'precision': 0.9908256880733946, 'recall': 0.9818181818181818, 'f1': 0.9863013698630138, 'number': 110}
- Seal number: {'precision': 0.9380530973451328, 'recall': 0.9724770642201835, 'f1': 0.954954954954955, 'number': 109}
- Container quantity: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 53}
- Container type: {'precision': 0.9894736842105263, 'recall': 0.9791666666666666, 'f1': 0.9842931937172775, 'number': 96}
- Tare: {'precision': 0.7816091954022989, 'recall': 0.7816091954022989, 'f1': 0.781609195402299, 'number': 87}
- Package quantity: {'precision': 0.9047619047619048, 'recall': 0.9405940594059405, 'f1': 0.9223300970873787, 'number': 101}
- Weight: {'precision': 1.0, 'recall': 0.9578947368421052, 'f1': 0.978494623655914, 'number': 95}
- Others: {'precision': 0.9266247379454927, 'recall': 0.9567099567099567, 'f1': 0.9414270500532481, 'number': 462}

## 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: 3000

### 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                                                                                                   |
|:-------------:|:-----:|:----:|:------:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|
| No log        | 1.17  | 250  | 0.9856 | 0.0573          | 0.8748            | 0.9039         | 0.8891     | 0.9856           | {'precision': 0.981651376146789, 'recall': 0.9727272727272728, 'f1': 0.9771689497716896, 'number': 110}  | {'precision': 0.990909090909091, 'recall': 1.0, 'f1': 0.995433789954338, 'number': 109}                 | {'precision': 0.8412698412698413, 'recall': 1.0, 'f1': 0.9137931034482758, 'number': 53} | {'precision': 0.9175257731958762, 'recall': 0.9270833333333334, 'f1': 0.9222797927461139, 'number': 96} | {'precision': 0.7, 'recall': 0.6436781609195402, 'f1': 0.6706586826347305, 'number': 87}                | {'precision': 0.8189655172413793, 'recall': 0.9405940594059405, 'f1': 0.8755760368663594, 'number': 101} | {'precision': 0.875, 'recall': 0.7368421052631579, 'f1': 0.7999999999999999, 'number': 95}              | {'precision': 0.8626262626262626, 'recall': 0.9242424242424242, 'f1': 0.8923719958202716, 'number': 462} |
| 0.1682        | 2.34  | 500  | 0.9914 | 0.0346          | 0.9111            | 0.9479         | 0.9291     | 0.9914           | {'precision': 0.9908256880733946, 'recall': 0.9818181818181818, 'f1': 0.9863013698630138, 'number': 110} | {'precision': 0.9732142857142857, 'recall': 1.0, 'f1': 0.9864253393665159, 'number': 109}               | {'precision': 0.9464285714285714, 'recall': 1.0, 'f1': 0.9724770642201834, 'number': 53} | {'precision': 0.9387755102040817, 'recall': 0.9583333333333334, 'f1': 0.9484536082474228, 'number': 96} | {'precision': 0.8, 'recall': 0.7816091954022989, 'f1': 0.7906976744186047, 'number': 87}                | {'precision': 0.8761061946902655, 'recall': 0.9801980198019802, 'f1': 0.9252336448598131, 'number': 101} | {'precision': 0.9680851063829787, 'recall': 0.9578947368421052, 'f1': 0.962962962962963, 'number': 95}  | {'precision': 0.8859470468431772, 'recall': 0.9415584415584416, 'f1': 0.912906610703043, 'number': 462}  |
| 0.1682        | 3.5   | 750  | 0.9923 | 0.0324          | 0.9202            | 0.9533         | 0.9365     | 0.9923           | {'precision': 0.9908256880733946, 'recall': 0.9818181818181818, 'f1': 0.9863013698630138, 'number': 110} | {'precision': 0.9819819819819819, 'recall': 1.0, 'f1': 0.9909090909090909, 'number': 109}               | {'precision': 0.9464285714285714, 'recall': 1.0, 'f1': 0.9724770642201834, 'number': 53} | {'precision': 0.9690721649484536, 'recall': 0.9791666666666666, 'f1': 0.9740932642487047, 'number': 96} | {'precision': 0.8023255813953488, 'recall': 0.7931034482758621, 'f1': 0.7976878612716762, 'number': 87} | {'precision': 0.8672566371681416, 'recall': 0.9702970297029703, 'f1': 0.9158878504672897, 'number': 101} | {'precision': 0.989010989010989, 'recall': 0.9473684210526315, 'f1': 0.967741935483871, 'number': 95}   | {'precision': 0.8979591836734694, 'recall': 0.9523809523809523, 'f1': 0.9243697478991597, 'number': 462} |
| 0.0266        | 4.67  | 1000 | 0.9911 | 0.0361          | 0.9004            | 0.9506         | 0.9248     | 0.9911           | {'precision': 0.9908256880733946, 'recall': 0.9818181818181818, 'f1': 0.9863013698630138, 'number': 110} | {'precision': 0.9732142857142857, 'recall': 1.0, 'f1': 0.9864253393665159, 'number': 109}               | {'precision': 0.8412698412698413, 'recall': 1.0, 'f1': 0.9137931034482758, 'number': 53} | {'precision': 0.92, 'recall': 0.9583333333333334, 'f1': 0.9387755102040817, 'number': 96}               | {'precision': 0.8, 'recall': 0.8275862068965517, 'f1': 0.8135593220338982, 'number': 87}                | {'precision': 0.8584070796460177, 'recall': 0.9603960396039604, 'f1': 0.9065420560747663, 'number': 101} | {'precision': 0.989247311827957, 'recall': 0.968421052631579, 'f1': 0.9787234042553192, 'number': 95}   | {'precision': 0.8787878787878788, 'recall': 0.9415584415584416, 'f1': 0.9090909090909091, 'number': 462} |
| 0.0266        | 5.84  | 1250 | 0.9916 | 0.0336          | 0.9131            | 0.9443         | 0.9284     | 0.9916           | {'precision': 0.9908256880733946, 'recall': 0.9818181818181818, 'f1': 0.9863013698630138, 'number': 110} | {'precision': 0.9732142857142857, 'recall': 1.0, 'f1': 0.9864253393665159, 'number': 109}               | {'precision': 0.9814814814814815, 'recall': 1.0, 'f1': 0.9906542056074767, 'number': 53} | {'precision': 0.9489795918367347, 'recall': 0.96875, 'f1': 0.9587628865979382, 'number': 96}            | {'precision': 0.797752808988764, 'recall': 0.8160919540229885, 'f1': 0.8068181818181818, 'number': 87}  | {'precision': 0.8771929824561403, 'recall': 0.9900990099009901, 'f1': 0.9302325581395348, 'number': 101} | {'precision': 0.9215686274509803, 'recall': 0.9894736842105263, 'f1': 0.9543147208121827, 'number': 95} | {'precision': 0.8942917547568711, 'recall': 0.9155844155844156, 'f1': 0.9048128342245989, 'number': 462} |
| 0.0161        | 7.01  | 1500 | 0.9944 | 0.0240          | 0.9339            | 0.9650         | 0.9492     | 0.9944           | {'precision': 0.9908256880733946, 'recall': 0.9818181818181818, 'f1': 0.9863013698630138, 'number': 110} | {'precision': 0.9819819819819819, 'recall': 1.0, 'f1': 0.9909090909090909, 'number': 109}               | {'precision': 0.9814814814814815, 'recall': 1.0, 'f1': 0.9906542056074767, 'number': 53} | {'precision': 0.9690721649484536, 'recall': 0.9791666666666666, 'f1': 0.9740932642487047, 'number': 96} | {'precision': 0.8089887640449438, 'recall': 0.8275862068965517, 'f1': 0.8181818181818181, 'number': 87} | {'precision': 0.9174311926605505, 'recall': 0.9900990099009901, 'f1': 0.9523809523809524, 'number': 101} | {'precision': 0.9893617021276596, 'recall': 0.9789473684210527, 'f1': 0.9841269841269842, 'number': 95} | {'precision': 0.9137577002053389, 'recall': 0.9632034632034632, 'f1': 0.9378292939936775, 'number': 462} |
| 0.0161        | 8.18  | 1750 | 0.9940 | 0.0255          | 0.9314            | 0.9641         | 0.9475     | 0.9940           | {'precision': 0.9908256880733946, 'recall': 0.9818181818181818, 'f1': 0.9863013698630138, 'number': 110} | {'precision': 0.9819819819819819, 'recall': 1.0, 'f1': 0.9909090909090909, 'number': 109}               | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 53}                               | {'precision': 0.9791666666666666, 'recall': 0.9791666666666666, 'f1': 0.9791666666666666, 'number': 96} | {'precision': 0.8089887640449438, 'recall': 0.8275862068965517, 'f1': 0.8181818181818181, 'number': 87} | {'precision': 0.8849557522123894, 'recall': 0.9900990099009901, 'f1': 0.9345794392523366, 'number': 101} | {'precision': 1.0, 'recall': 0.9789473684210527, 'f1': 0.9893617021276596, 'number': 95}                | {'precision': 0.9098360655737705, 'recall': 0.961038961038961, 'f1': 0.9347368421052632, 'number': 462}  |
| 0.0107        | 9.35  | 2000 | 0.9930 | 0.0263          | 0.9254            | 0.9470         | 0.9361     | 0.9930           | {'precision': 0.9908256880733946, 'recall': 0.9818181818181818, 'f1': 0.9863013698630138, 'number': 110} | {'precision': 0.9385964912280702, 'recall': 0.981651376146789, 'f1': 0.9596412556053812, 'number': 109} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 53}                               | {'precision': 0.968421052631579, 'recall': 0.9583333333333334, 'f1': 0.9633507853403142, 'number': 96}  | {'precision': 0.7954545454545454, 'recall': 0.8045977011494253, 'f1': 0.7999999999999999, 'number': 87} | {'precision': 0.8785046728971962, 'recall': 0.9306930693069307, 'f1': 0.9038461538461539, 'number': 101} | {'precision': 0.989010989010989, 'recall': 0.9473684210526315, 'f1': 0.967741935483871, 'number': 95}   | {'precision': 0.9128630705394191, 'recall': 0.9523809523809523, 'f1': 0.9322033898305083, 'number': 462} |
| 0.0107        | 10.51 | 2250 | 0.9934 | 0.0212          | 0.9370            | 0.9488         | 0.9429     | 0.9934           | {'precision': 0.9908256880733946, 'recall': 0.9818181818181818, 'f1': 0.9863013698630138, 'number': 110} | {'precision': 0.9380530973451328, 'recall': 0.9724770642201835, 'f1': 0.954954954954955, 'number': 109} | {'precision': 1.0, 'recall': 0.9811320754716981, 'f1': 0.9904761904761905, 'number': 53} | {'precision': 0.9789473684210527, 'recall': 0.96875, 'f1': 0.9738219895287958, 'number': 96}            | {'precision': 0.8045977011494253, 'recall': 0.8045977011494253, 'f1': 0.8045977011494253, 'number': 87} | {'precision': 0.9215686274509803, 'recall': 0.9306930693069307, 'f1': 0.9261083743842364, 'number': 101} | {'precision': 1.0, 'recall': 0.9578947368421052, 'f1': 0.978494623655914, 'number': 95}                 | {'precision': 0.9246861924686193, 'recall': 0.9567099567099567, 'f1': 0.9404255319148936, 'number': 462} |
| 0.0064        | 11.68 | 2500 | 0.9928 | 0.0293          | 0.9300            | 0.9551         | 0.9424     | 0.9928           | {'precision': 0.9908256880733946, 'recall': 0.9818181818181818, 'f1': 0.9863013698630138, 'number': 110} | {'precision': 0.9380530973451328, 'recall': 0.9724770642201835, 'f1': 0.954954954954955, 'number': 109} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 53}                               | {'precision': 0.9894736842105263, 'recall': 0.9791666666666666, 'f1': 0.9842931937172775, 'number': 96} | {'precision': 0.8089887640449438, 'recall': 0.8275862068965517, 'f1': 0.8181818181818181, 'number': 87} | {'precision': 0.8962264150943396, 'recall': 0.9405940594059405, 'f1': 0.9178743961352657, 'number': 101} | {'precision': 0.9893617021276596, 'recall': 0.9789473684210527, 'f1': 0.9841269841269842, 'number': 95} | {'precision': 0.9132231404958677, 'recall': 0.9567099567099567, 'f1': 0.9344608879492601, 'number': 462} |
| 0.0064        | 12.85 | 2750 | 0.9930 | 0.0252          | 0.9362            | 0.9497         | 0.9429     | 0.9930           | {'precision': 0.9908256880733946, 'recall': 0.9818181818181818, 'f1': 0.9863013698630138, 'number': 110} | {'precision': 0.9380530973451328, 'recall': 0.9724770642201835, 'f1': 0.954954954954955, 'number': 109} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 53}                               | {'precision': 0.9894736842105263, 'recall': 0.9791666666666666, 'f1': 0.9842931937172775, 'number': 96} | {'precision': 0.7727272727272727, 'recall': 0.7816091954022989, 'f1': 0.777142857142857, 'number': 87}  | {'precision': 0.9134615384615384, 'recall': 0.9405940594059405, 'f1': 0.926829268292683, 'number': 101}  | {'precision': 0.989010989010989, 'recall': 0.9473684210526315, 'f1': 0.967741935483871, 'number': 95}   | {'precision': 0.930672268907563, 'recall': 0.9588744588744589, 'f1': 0.9445628997867804, 'number': 462}  |
| 0.0057        | 14.02 | 3000 | 0.9930 | 0.0252          | 0.9354            | 0.9497         | 0.9425     | 0.9930           | {'precision': 0.9908256880733946, 'recall': 0.9818181818181818, 'f1': 0.9863013698630138, 'number': 110} | {'precision': 0.9380530973451328, 'recall': 0.9724770642201835, 'f1': 0.954954954954955, 'number': 109} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 53}                               | {'precision': 0.9894736842105263, 'recall': 0.9791666666666666, 'f1': 0.9842931937172775, 'number': 96} | {'precision': 0.7816091954022989, 'recall': 0.7816091954022989, 'f1': 0.781609195402299, 'number': 87}  | {'precision': 0.9047619047619048, 'recall': 0.9405940594059405, 'f1': 0.9223300970873787, 'number': 101} | {'precision': 1.0, 'recall': 0.9578947368421052, 'f1': 0.978494623655914, 'number': 95}                 | {'precision': 0.9266247379454927, 'recall': 0.9567099567099567, 'f1': 0.9414270500532481, 'number': 462} |


### 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_v5.6_detailed", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "f1", "value": 0.9930313588850174, "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\_v5.6\_detailed
==================================


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: 0.9930
* Loss: 0.0252
* Overall Precision: 0.9354
* Overall Recall: 0.9497
* Overall F1: 0.9425
* Overall Accuracy: 0.9930
* Container id: {'precision': 0.9908256880733946, 'recall': 0.9818181818181818, 'f1': 0.9863013698630138, 'number': 110}
* Seal number: {'precision': 0.9380530973451328, 'recall': 0.9724770642201835, 'f1': 0.954954954954955, 'number': 109}
* Container quantity: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 53}
* Container type: {'precision': 0.9894736842105263, 'recall': 0.9791666666666666, 'f1': 0.9842931937172775, 'number': 96}
* Tare: {'precision': 0.7816091954022989, 'recall': 0.7816091954022989, 'f1': 0.781609195402299, 'number': 87}
* Package quantity: {'precision': 0.9047619047619048, 'recall': 0.9405940594059405, 'f1': 0.9223300970873787, 'number': 101}
* Weight: {'precision': 1.0, 'recall': 0.9578947368421052, 'f1': 0.978494623655914, 'number': 95}
* Others: {'precision': 0.9266247379454927, 'recall': 0.9567099567099567, 'f1': 0.9414270500532481, 'number': 462}


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: 3000


### 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: 3000 ### 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: 3000 ### Training results ### Framework versions


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