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# perioli_vgm_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: 0.9962
- Loss: 0.0286
- Overall Precision: 0.9224
- Overall Recall: 0.9421
- Overall F1: 0.9321
- Overall Accuracy: 0.9962
- Authorized person: {'precision': 0.8148148148148148, 'recall': 0.9166666666666666, 'f1': 0.8627450980392156, 'number': 24}
- Container type: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5}
- Container id: {'precision': 0.9333333333333333, 'recall': 1.0, 'f1': 0.9655172413793104, 'number': 28}
- Booking number: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22}
- Vgm: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 26}
- Signer name: {'precision': 0.9285714285714286, 'recall': 0.8125, 'f1': 0.8666666666666666, 'number': 16}
- Shipper: {'precision': 0.76, 'recall': 0.8260869565217391, 'f1': 0.7916666666666667, 'number': 23}
- Others: {'precision': 0.9354838709677419, 'recall': 0.9456521739130435, 'f1': 0.9405405405405404, 'number': 184}

## 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: 5000

### Training results

| Training Loss | Epoch | Step | F1     | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Authorized person                                                                                       | Container type                                                                          | Container id                                                                             | Booking number                                                                                          | Vgm                                                                                                     | Signer name                                                                                 | Shipper                                                                                                 | Others                                                                                                   |
|:-------------:|:-----:|:----:|:------:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|
| 0.0888        | 2.22  | 500  | 0.9929 | 0.0246          | 0.8349            | 0.8171         | 0.8259     | 0.9929           | {'precision': 0.76, 'recall': 0.7916666666666666, 'f1': 0.7755102040816326, 'number': 24}               | {'precision': 0.5714285714285714, 'recall': 0.8, 'f1': 0.6666666666666666, 'number': 5} | {'precision': 0.9032258064516129, 'recall': 1.0, 'f1': 0.9491525423728813, 'number': 28} | {'precision': 1.0, 'recall': 0.9545454545454546, 'f1': 0.9767441860465117, 'number': 22}                | {'precision': 0.7916666666666666, 'recall': 0.7307692307692307, 'f1': 0.76, 'number': 26}               | {'precision': 0.8333333333333334, 'recall': 0.625, 'f1': 0.7142857142857143, 'number': 16}  | {'precision': 0.75, 'recall': 0.782608695652174, 'f1': 0.7659574468085107, 'number': 23}                | {'precision': 0.8418079096045198, 'recall': 0.8097826086956522, 'f1': 0.8254847645429363, 'number': 184} |
| 0.0141        | 4.44  | 1000 | 0.9945 | 0.0267          | 0.8813            | 0.9055         | 0.8932     | 0.9945           | {'precision': 0.65625, 'recall': 0.875, 'f1': 0.75, 'number': 24}                                       | {'precision': 0.8, 'recall': 0.8, 'f1': 0.8000000000000002, 'number': 5}                | {'precision': 0.875, 'recall': 1.0, 'f1': 0.9333333333333333, 'number': 28}              | {'precision': 0.9545454545454546, 'recall': 0.9545454545454546, 'f1': 0.9545454545454546, 'number': 22} | {'precision': 0.8620689655172413, 'recall': 0.9615384615384616, 'f1': 0.9090909090909091, 'number': 26} | {'precision': 1.0, 'recall': 0.8125, 'f1': 0.896551724137931, 'number': 16}                 | {'precision': 0.8947368421052632, 'recall': 0.7391304347826086, 'f1': 0.8095238095238095, 'number': 23} | {'precision': 0.9081081081081082, 'recall': 0.9130434782608695, 'f1': 0.9105691056910569, 'number': 184} |
| 0.004         | 6.67  | 1500 | 0.9953 | 0.0211          | 0.9069            | 0.9207         | 0.9138     | 0.9953           | {'precision': 0.8148148148148148, 'recall': 0.9166666666666666, 'f1': 0.8627450980392156, 'number': 24} | {'precision': 0.8, 'recall': 0.8, 'f1': 0.8000000000000002, 'number': 5}                | {'precision': 0.9032258064516129, 'recall': 1.0, 'f1': 0.9491525423728813, 'number': 28} | {'precision': 1.0, 'recall': 0.9545454545454546, 'f1': 0.9767441860465117, 'number': 22}                | {'precision': 0.9259259259259259, 'recall': 0.9615384615384616, 'f1': 0.9433962264150944, 'number': 26} | {'precision': 0.9230769230769231, 'recall': 0.75, 'f1': 0.8275862068965517, 'number': 16}   | {'precision': 0.8260869565217391, 'recall': 0.8260869565217391, 'f1': 0.8260869565217391, 'number': 23} | {'precision': 0.9193548387096774, 'recall': 0.9293478260869565, 'f1': 0.9243243243243242, 'number': 184} |
| 0.0023        | 8.89  | 2000 | 0.9953 | 0.0244          | 0.9055            | 0.9055         | 0.9055     | 0.9953           | {'precision': 0.8148148148148148, 'recall': 0.9166666666666666, 'f1': 0.8627450980392156, 'number': 24} | {'precision': 0.8, 'recall': 0.8, 'f1': 0.8000000000000002, 'number': 5}                | {'precision': 0.9333333333333333, 'recall': 1.0, 'f1': 0.9655172413793104, 'number': 28} | {'precision': 1.0, 'recall': 0.9545454545454546, 'f1': 0.9767441860465117, 'number': 22}                | {'precision': 0.8518518518518519, 'recall': 0.8846153846153846, 'f1': 0.8679245283018868, 'number': 26} | {'precision': 0.9230769230769231, 'recall': 0.75, 'f1': 0.8275862068965517, 'number': 16}   | {'precision': 0.8571428571428571, 'recall': 0.782608695652174, 'f1': 0.8181818181818182, 'number': 23}  | {'precision': 0.9184782608695652, 'recall': 0.9184782608695652, 'f1': 0.9184782608695652, 'number': 184} |
| 0.0016        | 11.11 | 2500 | 0.9964 | 0.0261          | 0.9247            | 0.9360         | 0.9303     | 0.9964           | {'precision': 0.7586206896551724, 'recall': 0.9166666666666666, 'f1': 0.830188679245283, 'number': 24}  | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5}                               | {'precision': 0.9032258064516129, 'recall': 1.0, 'f1': 0.9491525423728813, 'number': 28} | {'precision': 1.0, 'recall': 0.9545454545454546, 'f1': 0.9767441860465117, 'number': 22}                | {'precision': 1.0, 'recall': 0.9230769230769231, 'f1': 0.9600000000000001, 'number': 26}                | {'precision': 0.9285714285714286, 'recall': 0.8125, 'f1': 0.8666666666666666, 'number': 16} | {'precision': 0.8333333333333334, 'recall': 0.8695652173913043, 'f1': 0.851063829787234, 'number': 23}  | {'precision': 0.9456521739130435, 'recall': 0.9456521739130435, 'f1': 0.9456521739130435, 'number': 184} |
| 0.0009        | 13.33 | 3000 | 0.9959 | 0.0257          | 0.9157            | 0.9268         | 0.9212     | 0.9959           | {'precision': 0.8076923076923077, 'recall': 0.875, 'f1': 0.8400000000000001, 'number': 24}              | {'precision': 0.8, 'recall': 0.8, 'f1': 0.8000000000000002, 'number': 5}                | {'precision': 0.9032258064516129, 'recall': 1.0, 'f1': 0.9491525423728813, 'number': 28} | {'precision': 1.0, 'recall': 0.9545454545454546, 'f1': 0.9767441860465117, 'number': 22}                | {'precision': 1.0, 'recall': 0.9230769230769231, 'f1': 0.9600000000000001, 'number': 26}                | {'precision': 0.9285714285714286, 'recall': 0.8125, 'f1': 0.8666666666666666, 'number': 16} | {'precision': 0.7777777777777778, 'recall': 0.9130434782608695, 'f1': 0.84, 'number': 23}               | {'precision': 0.9347826086956522, 'recall': 0.9347826086956522, 'f1': 0.9347826086956522, 'number': 184} |
| 0.0008        | 15.56 | 3500 | 0.9957 | 0.0286          | 0.9290            | 0.9177         | 0.9233     | 0.9957           | {'precision': 0.84, 'recall': 0.875, 'f1': 0.8571428571428572, 'number': 24}                            | {'precision': 0.8, 'recall': 0.8, 'f1': 0.8000000000000002, 'number': 5}                | {'precision': 0.9333333333333333, 'recall': 1.0, 'f1': 0.9655172413793104, 'number': 28} | {'precision': 0.9565217391304348, 'recall': 1.0, 'f1': 0.9777777777777777, 'number': 22}                | {'precision': 0.9615384615384616, 'recall': 0.9615384615384616, 'f1': 0.9615384615384616, 'number': 26} | {'precision': 0.9230769230769231, 'recall': 0.75, 'f1': 0.8275862068965517, 'number': 16}   | {'precision': 0.9047619047619048, 'recall': 0.8260869565217391, 'f1': 0.8636363636363636, 'number': 23} | {'precision': 0.9392265193370166, 'recall': 0.9239130434782609, 'f1': 0.9315068493150687, 'number': 184} |
| 0.0002        | 17.78 | 4000 | 0.9962 | 0.0292          | 0.9286            | 0.9512         | 0.9398     | 0.9962           | {'precision': 0.7857142857142857, 'recall': 0.9166666666666666, 'f1': 0.8461538461538461, 'number': 24} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5}                               | {'precision': 0.9333333333333333, 'recall': 1.0, 'f1': 0.9655172413793104, 'number': 28} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 26}                                              | {'precision': 0.8571428571428571, 'recall': 0.75, 'f1': 0.7999999999999999, 'number': 16}   | {'precision': 0.8333333333333334, 'recall': 0.8695652173913043, 'f1': 0.851063829787234, 'number': 23}  | {'precision': 0.946524064171123, 'recall': 0.9619565217391305, 'f1': 0.954177897574124, 'number': 184}   |
| 0.0002        | 20.0  | 4500 | 0.9963 | 0.0294          | 0.9174            | 0.9482         | 0.9325     | 0.9963           | {'precision': 0.8461538461538461, 'recall': 0.9166666666666666, 'f1': 0.8799999999999999, 'number': 24} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5}                               | {'precision': 0.9333333333333333, 'recall': 1.0, 'f1': 0.9655172413793104, 'number': 28} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22}                                              | {'precision': 0.9629629629629629, 'recall': 1.0, 'f1': 0.9811320754716981, 'number': 26}                | {'precision': 0.8571428571428571, 'recall': 0.75, 'f1': 0.7999999999999999, 'number': 16}   | {'precision': 0.8076923076923077, 'recall': 0.9130434782608695, 'f1': 0.8571428571428572, 'number': 23} | {'precision': 0.9259259259259259, 'recall': 0.9510869565217391, 'f1': 0.9383378016085792, 'number': 184} |
| 0.0002        | 22.22 | 5000 | 0.9962 | 0.0286          | 0.9224            | 0.9421         | 0.9321     | 0.9962           | {'precision': 0.8148148148148148, 'recall': 0.9166666666666666, 'f1': 0.8627450980392156, 'number': 24} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5}                               | {'precision': 0.9333333333333333, 'recall': 1.0, 'f1': 0.9655172413793104, 'number': 28} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22}                                              | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 26}                                              | {'precision': 0.9285714285714286, 'recall': 0.8125, 'f1': 0.8666666666666666, 'number': 16} | {'precision': 0.76, 'recall': 0.8260869565217391, 'f1': 0.7916666666666667, 'number': 23}               | {'precision': 0.9354838709677419, 'recall': 0.9456521739130435, 'f1': 0.9405405405405404, 'number': 184} |


### 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_vgm_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": 0.9961944512644243, "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\_vgm\_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: 0.9962
* Loss: 0.0286
* Overall Precision: 0.9224
* Overall Recall: 0.9421
* Overall F1: 0.9321
* Overall Accuracy: 0.9962
* Authorized person: {'precision': 0.8148148148148148, 'recall': 0.9166666666666666, 'f1': 0.8627450980392156, 'number': 24}
* Container type: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5}
* Container id: {'precision': 0.9333333333333333, 'recall': 1.0, 'f1': 0.9655172413793104, 'number': 28}
* Booking number: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 22}
* Vgm: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 26}
* Signer name: {'precision': 0.9285714285714286, 'recall': 0.8125, 'f1': 0.8666666666666666, 'number': 16}
* Shipper: {'precision': 0.76, 'recall': 0.8260869565217391, 'f1': 0.7916666666666667, 'number': 23}
* Others: {'precision': 0.9354838709677419, 'recall': 0.9456521739130435, 'f1': 0.9405405405405404, 'number': 184}


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: 5000


### 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: 5000 ### 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: 5000 ### Training results ### Framework versions


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