|
--- |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- wild_receipt |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: layoutlmv3-finetuned-wildreceipt |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: wild_receipt |
|
type: wild_receipt |
|
args: WildReceipt |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.877212237618329 |
|
- name: Recall |
|
type: recall |
|
value: 0.8798678959680749 |
|
- name: F1 |
|
type: f1 |
|
value: 0.8785380599065679 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9249204782274871 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# layoutlmv3-finetuned-wildreceipt |
|
|
|
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the wild_receipt dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3108 |
|
- Precision: 0.8772 |
|
- Recall: 0.8799 |
|
- F1: 0.8785 |
|
- Accuracy: 0.9249 |
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## Intended uses & limitations |
|
|
|
More information needed |
|
|
|
## Training and evaluation data |
|
|
|
The WildReceipt dataset consists of 1740 receipt images, and contains 25 key information categories, and a total of about 69000 text boxes. 1268 and 472 images are used for training and testing respectively to train the LayoutLMv3 model for Key Information Extraction. |
|
|
|
## Training procedure |
|
|
|
The training code: https://github.com/Theivaprakasham/layoutlmv3/blob/main/training_codes/LayoutLMv3_training_WildReceipts_dataset.ipynb |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 1e-05 |
|
- train_batch_size: 4 |
|
- eval_batch_size: 4 |
|
- 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 | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| No log | 0.32 | 100 | 1.3143 | 0.6709 | 0.2679 | 0.3829 | 0.6700 | |
|
| No log | 0.63 | 200 | 0.8814 | 0.6478 | 0.5195 | 0.5766 | 0.7786 | |
|
| No log | 0.95 | 300 | 0.6568 | 0.7205 | 0.6491 | 0.6829 | 0.8303 | |
|
| No log | 1.26 | 400 | 0.5618 | 0.7544 | 0.7072 | 0.7300 | 0.8519 | |
|
| 1.0284 | 1.58 | 500 | 0.5003 | 0.7802 | 0.7566 | 0.7682 | 0.8687 | |
|
| 1.0284 | 1.89 | 600 | 0.4454 | 0.7941 | 0.7679 | 0.7807 | 0.8748 | |
|
| 1.0284 | 2.21 | 700 | 0.4314 | 0.8142 | 0.7928 | 0.8033 | 0.8852 | |
|
| 1.0284 | 2.52 | 800 | 0.3870 | 0.8172 | 0.8200 | 0.8186 | 0.8953 | |
|
| 1.0284 | 2.84 | 900 | 0.3629 | 0.8288 | 0.8369 | 0.8329 | 0.9025 | |
|
| 0.4167 | 3.15 | 1000 | 0.3537 | 0.8540 | 0.8200 | 0.8366 | 0.9052 | |
|
| 0.4167 | 3.47 | 1100 | 0.3383 | 0.8438 | 0.8285 | 0.8361 | 0.9063 | |
|
| 0.4167 | 3.79 | 1200 | 0.3403 | 0.8297 | 0.8493 | 0.8394 | 0.9062 | |
|
| 0.4167 | 4.1 | 1300 | 0.3271 | 0.8428 | 0.8545 | 0.8487 | 0.9110 | |
|
| 0.4167 | 4.42 | 1400 | 0.3182 | 0.8491 | 0.8518 | 0.8504 | 0.9131 | |
|
| 0.2766 | 4.73 | 1500 | 0.3111 | 0.8491 | 0.8539 | 0.8515 | 0.9129 | |
|
| 0.2766 | 5.05 | 1600 | 0.3177 | 0.8397 | 0.8620 | 0.8507 | 0.9124 | |
|
| 0.2766 | 5.36 | 1700 | 0.3091 | 0.8676 | 0.8548 | 0.8612 | 0.9191 | |
|
| 0.2766 | 5.68 | 1800 | 0.3080 | 0.8508 | 0.8645 | 0.8576 | 0.9162 | |
|
| 0.2766 | 5.99 | 1900 | 0.3059 | 0.8492 | 0.8662 | 0.8576 | 0.9163 | |
|
| 0.2114 | 6.31 | 2000 | 0.3184 | 0.8536 | 0.8657 | 0.8596 | 0.9147 | |
|
| 0.2114 | 6.62 | 2100 | 0.3161 | 0.8583 | 0.8713 | 0.8648 | 0.9184 | |
|
| 0.2114 | 6.94 | 2200 | 0.3055 | 0.8707 | 0.8682 | 0.8694 | 0.9220 | |
|
| 0.2114 | 7.26 | 2300 | 0.3004 | 0.8689 | 0.8745 | 0.8717 | 0.9219 | |
|
| 0.2114 | 7.57 | 2400 | 0.3111 | 0.8701 | 0.8720 | 0.8711 | 0.9211 | |
|
| 0.174 | 7.89 | 2500 | 0.3130 | 0.8599 | 0.8741 | 0.8669 | 0.9198 | |
|
| 0.174 | 8.2 | 2600 | 0.3034 | 0.8661 | 0.8748 | 0.8704 | 0.9219 | |
|
| 0.174 | 8.52 | 2700 | 0.3005 | 0.8799 | 0.8673 | 0.8736 | 0.9225 | |
|
| 0.174 | 8.83 | 2800 | 0.3043 | 0.8687 | 0.8804 | 0.8745 | 0.9240 | |
|
| 0.174 | 9.15 | 2900 | 0.3121 | 0.8776 | 0.8704 | 0.8740 | 0.9242 | |
|
| 0.1412 | 9.46 | 3000 | 0.3131 | 0.8631 | 0.8755 | 0.8692 | 0.9204 | |
|
| 0.1412 | 9.78 | 3100 | 0.3067 | 0.8715 | 0.8773 | 0.8744 | 0.9233 | |
|
| 0.1412 | 10.09 | 3200 | 0.3021 | 0.8751 | 0.8812 | 0.8782 | 0.9248 | |
|
| 0.1412 | 10.41 | 3300 | 0.3092 | 0.8651 | 0.8808 | 0.8729 | 0.9228 | |
|
| 0.1412 | 10.73 | 3400 | 0.3084 | 0.8776 | 0.8749 | 0.8762 | 0.9237 | |
|
| 0.1254 | 11.04 | 3500 | 0.3156 | 0.8738 | 0.8785 | 0.8761 | 0.9237 | |
|
| 0.1254 | 11.36 | 3600 | 0.3131 | 0.8723 | 0.8818 | 0.8770 | 0.9244 | |
|
| 0.1254 | 11.67 | 3700 | 0.3108 | 0.8778 | 0.8781 | 0.8780 | 0.9250 | |
|
| 0.1254 | 11.99 | 3800 | 0.3097 | 0.8778 | 0.8771 | 0.8775 | 0.9239 | |
|
| 0.1254 | 12.3 | 3900 | 0.3115 | 0.8785 | 0.8801 | 0.8793 | 0.9251 | |
|
| 0.111 | 12.62 | 4000 | 0.3108 | 0.8772 | 0.8799 | 0.8785 | 0.9249 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.20.0.dev0 |
|
- Pytorch 1.11.0+cu113 |
|
- Datasets 2.2.2 |
|
- Tokenizers 0.12.1 |
|
|
