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# passive_invoices_v4.3

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: 0.0991
- Precision: 0.8844
- Recall: 0.9062
- F1: 0.8952
- Accuracy: 0.9773

## 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: 17000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.9884        | 0.28  | 500   | 0.7877          | 0.2256    | 0.1381 | 0.1713 | 0.8026   |
| 0.544         | 0.56  | 1000  | 0.4954          | 0.4793    | 0.4391 | 0.4583 | 0.8744   |
| 0.3564        | 0.84  | 1500  | 0.3731          | 0.5562    | 0.5750 | 0.5654 | 0.8959   |
| 0.2663        | 1.11  | 2000  | 0.3121          | 0.6203    | 0.6429 | 0.6314 | 0.9142   |
| 0.1994        | 1.39  | 2500  | 0.2476          | 0.6599    | 0.6842 | 0.6719 | 0.9285   |
| 0.1602        | 1.67  | 3000  | 0.2322          | 0.6689    | 0.7264 | 0.6965 | 0.9335   |
| 0.1425        | 1.95  | 3500  | 0.1946          | 0.7202    | 0.7478 | 0.7337 | 0.9444   |
| 0.1189        | 2.23  | 4000  | 0.1734          | 0.7425    | 0.7838 | 0.7626 | 0.9513   |
| 0.1051        | 2.51  | 4500  | 0.1537          | 0.7868    | 0.7969 | 0.7919 | 0.9573   |
| 0.0998        | 2.78  | 5000  | 0.1446          | 0.7957    | 0.8153 | 0.8054 | 0.9593   |
| 0.0805        | 3.06  | 5500  | 0.1467          | 0.7875    | 0.8332 | 0.8097 | 0.9623   |
| 0.0677        | 3.34  | 6000  | 0.1265          | 0.8261    | 0.8496 | 0.8377 | 0.9674   |
| 0.073         | 3.62  | 6500  | 0.1291          | 0.8244    | 0.8477 | 0.8359 | 0.9667   |
| 0.0702        | 3.9   | 7000  | 0.1226          | 0.8188    | 0.8550 | 0.8365 | 0.9668   |
| 0.0607        | 4.18  | 7500  | 0.1271          | 0.8378    | 0.8617 | 0.8496 | 0.9685   |
| 0.0593        | 4.45  | 8000  | 0.1189          | 0.8441    | 0.8618 | 0.8528 | 0.9689   |
| 0.0562        | 4.73  | 8500  | 0.1093          | 0.8521    | 0.8835 | 0.8675 | 0.9722   |
| 0.0465        | 5.01  | 9000  | 0.1092          | 0.8462    | 0.8819 | 0.8637 | 0.9723   |
| 0.0499        | 5.29  | 9500  | 0.0978          | 0.8641    | 0.8850 | 0.8744 | 0.9746   |
| 0.0431        | 5.57  | 10000 | 0.1085          | 0.8555    | 0.8864 | 0.8707 | 0.9739   |
| 0.0398        | 5.85  | 10500 | 0.1052          | 0.8630    | 0.8885 | 0.8756 | 0.9742   |
| 0.039         | 6.12  | 11000 | 0.1049          | 0.8687    | 0.8949 | 0.8816 | 0.9753   |
| 0.0392        | 6.4   | 11500 | 0.0989          | 0.8746    | 0.9004 | 0.8873 | 0.9770   |
| 0.0389        | 6.68  | 12000 | 0.1030          | 0.8752    | 0.8993 | 0.8871 | 0.9758   |
| 0.0389        | 6.96  | 12500 | 0.0979          | 0.8793    | 0.9029 | 0.8909 | 0.9767   |
| 0.0276        | 7.24  | 13000 | 0.1008          | 0.8764    | 0.9047 | 0.8903 | 0.9766   |
| 0.0346        | 7.52  | 13500 | 0.1002          | 0.8823    | 0.9052 | 0.8936 | 0.9772   |
| 0.0352        | 7.8   | 14000 | 0.1020          | 0.8757    | 0.9018 | 0.8886 | 0.9765   |
| 0.0276        | 8.07  | 14500 | 0.0996          | 0.8864    | 0.9051 | 0.8957 | 0.9778   |
| 0.0284        | 8.35  | 15000 | 0.1006          | 0.8762    | 0.9034 | 0.8896 | 0.9766   |
| 0.0281        | 8.63  | 15500 | 0.0985          | 0.8814    | 0.9057 | 0.8934 | 0.9770   |
| 0.0295        | 8.91  | 16000 | 0.0990          | 0.8802    | 0.9058 | 0.8928 | 0.9772   |
| 0.0281        | 9.19  | 16500 | 0.0991          | 0.8823    | 0.9061 | 0.8940 | 0.9772   |
| 0.0234        | 9.47  | 17000 | 0.0991          | 0.8844    | 0.9062 | 0.8952 | 0.9773   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.1.0+cu121
- 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": "passive_invoices_v4.3", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.8844043070510594, "name": "Precision"}, {"type": "recall", "value": 0.9061854936294398, "name": "Recall"}, {"type": "f1", "value": 0.8951624244128814, "name": "F1"}, {"type": "accuracy", "value": 0.9772527591560654, "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