
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# passive_invoices_v4.5

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: 0.1004
- Precision: 0.8825
- Recall: 0.9070
- F1: 0.8946
- Accuracy: 0.9774

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.048         | 0.28  | 500   | 0.8345          | 0.2080    | 0.1260 | 0.1569 | 0.7926   |
| 0.5402        | 0.55  | 1000  | 0.5633          | 0.3904    | 0.3482 | 0.3681 | 0.8506   |
| 0.3693        | 0.83  | 1500  | 0.3848          | 0.5414    | 0.5351 | 0.5382 | 0.8962   |
| 0.2674        | 1.11  | 2000  | 0.2884          | 0.6377    | 0.6156 | 0.6265 | 0.9230   |
| 0.1972        | 1.39  | 2500  | 0.2416          | 0.6746    | 0.6682 | 0.6714 | 0.9325   |
| 0.1698        | 1.66  | 3000  | 0.2125          | 0.7083    | 0.7268 | 0.7174 | 0.9414   |
| 0.1439        | 1.94  | 3500  | 0.1951          | 0.7029    | 0.7567 | 0.7288 | 0.9442   |
| 0.1161        | 2.22  | 4000  | 0.1795          | 0.7193    | 0.7692 | 0.7434 | 0.9490   |
| 0.1008        | 2.49  | 4500  | 0.1666          | 0.7638    | 0.7941 | 0.7787 | 0.9548   |
| 0.0941        | 2.77  | 5000  | 0.1460          | 0.7912    | 0.8139 | 0.8024 | 0.9598   |
| 0.0882        | 3.05  | 5500  | 0.1525          | 0.7834    | 0.8294 | 0.8057 | 0.9592   |
| 0.067         | 3.33  | 6000  | 0.1309          | 0.8008    | 0.8295 | 0.8149 | 0.9645   |
| 0.0706        | 3.6   | 6500  | 0.1268          | 0.8243    | 0.8559 | 0.8398 | 0.9679   |
| 0.0707        | 3.88  | 7000  | 0.1280          | 0.8434    | 0.8564 | 0.8498 | 0.9675   |
| 0.0607        | 4.16  | 7500  | 0.1226          | 0.8328    | 0.8630 | 0.8476 | 0.9688   |
| 0.0531        | 4.43  | 8000  | 0.1125          | 0.8413    | 0.8654 | 0.8532 | 0.9711   |
| 0.053         | 4.71  | 8500  | 0.1129          | 0.8486    | 0.8725 | 0.8604 | 0.9715   |
| 0.0533        | 4.99  | 9000  | 0.1244          | 0.8389    | 0.8760 | 0.8571 | 0.9685   |
| 0.0419        | 5.27  | 9500  | 0.1122          | 0.8587    | 0.8851 | 0.8717 | 0.9723   |
| 0.0441        | 5.54  | 10000 | 0.1161          | 0.8682    | 0.8769 | 0.8726 | 0.9729   |
| 0.0494        | 5.82  | 10500 | 0.1042          | 0.8714    | 0.8887 | 0.8800 | 0.9750   |
| 0.0387        | 6.1   | 11000 | 0.1001          | 0.8668    | 0.8934 | 0.8799 | 0.9762   |
| 0.0367        | 6.37  | 11500 | 0.1055          | 0.8674    | 0.8893 | 0.8782 | 0.9747   |
| 0.0406        | 6.65  | 12000 | 0.0961          | 0.8757    | 0.8996 | 0.8875 | 0.9770   |
| 0.0316        | 6.93  | 12500 | 0.1047          | 0.8738    | 0.8970 | 0.8852 | 0.9757   |
| 0.0284        | 7.21  | 13000 | 0.1065          | 0.8731    | 0.8985 | 0.8856 | 0.9759   |
| 0.0335        | 7.48  | 13500 | 0.1071          | 0.8724    | 0.8979 | 0.8850 | 0.9750   |
| 0.0361        | 7.76  | 14000 | 0.1068          | 0.8754    | 0.9022 | 0.8886 | 0.9761   |
| 0.0282        | 8.04  | 14500 | 0.1027          | 0.8736    | 0.9035 | 0.8883 | 0.9769   |
| 0.026         | 8.31  | 15000 | 0.1045          | 0.8802    | 0.9036 | 0.8917 | 0.9767   |
| 0.0275        | 8.59  | 15500 | 0.1058          | 0.8812    | 0.9055 | 0.8932 | 0.9771   |
| 0.0327        | 8.87  | 16000 | 0.1006          | 0.8809    | 0.9052 | 0.8929 | 0.9771   |
| 0.0248        | 9.15  | 16500 | 0.1009          | 0.8801    | 0.9059 | 0.8928 | 0.9772   |
| 0.025         | 9.42  | 17000 | 0.1004          | 0.8825    | 0.9070 | 0.8946 | 0.9774   |


### 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_v4.5", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.8825104717434594, "name": "Precision"}, {"type": "recall", "value": 0.9069865913902612, "name": "Recall"}, {"type": "f1", "value": 0.8945811436327568, "name": "F1"}, {"type": "accuracy", "value": 0.9774165913093141, "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