đ OCR-LayoutLMv3-Invoice
This model is a fine - tuned version of [microsoft/layoutlmv3 - base](https://huggingface.co/microsoft/layoutlmv3 - base) on the wild_receipt dataset, which can achieve high - precision token classification results.
đ Quick Start
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: 0.3159
- Precision: 0.8765
- Recall: 0.8812
- F1: 0.8789
- Accuracy: 0.9268
đ Documentation
Model Information
Property |
Details |
Model Type |
OCR - LayoutLMv3 - Invoice |
Tags |
generated_from_trainer |
Datasets |
wild_receipt |
Metrics |
precision, recall, f1, accuracy |
Model Results
- Model Name: OCR - LayoutLMv3 - Invoice
- Task: Token Classification
- Dataset: wild_receipt (train split)
Metric |
Value |
Precision |
0.8765398302764851 |
Recall |
0.8812439796339617 |
F1 |
0.8788856103753516 |
Accuracy |
0.92678512668641 |
Training and Evaluation Data
The model uses the wild_receipt dataset for training and evaluation. However, more detailed information about the data is yet to be provided.
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: 6000
Training Results
Training Loss |
Epoch |
Step |
Validation Loss |
Precision |
Recall |
F1 |
Accuracy |
No log |
0.16 |
100 |
1.5032 |
0.4934 |
0.1444 |
0.2234 |
0.6064 |
No log |
0.32 |
200 |
1.0282 |
0.5884 |
0.4420 |
0.5048 |
0.7385 |
No log |
0.47 |
300 |
0.7856 |
0.7448 |
0.6205 |
0.6770 |
0.8133 |
No log |
0.63 |
400 |
0.6464 |
0.7736 |
0.6689 |
0.7174 |
0.8399 |
1.1733 |
0.79 |
500 |
0.5672 |
0.7609 |
0.7303 |
0.7453 |
0.8557 |
1.1733 |
0.95 |
600 |
0.5055 |
0.7658 |
0.7652 |
0.7655 |
0.8677 |
1.1733 |
1.1 |
700 |
0.4735 |
0.7946 |
0.7848 |
0.7897 |
0.8784 |
1.1733 |
1.26 |
800 |
0.4414 |
0.7962 |
0.7946 |
0.7954 |
0.8818 |
1.1733 |
1.42 |
900 |
0.4094 |
0.8176 |
0.8064 |
0.8120 |
0.8894 |
0.5047 |
1.58 |
1000 |
0.3971 |
0.8219 |
0.8248 |
0.8234 |
0.8961 |
0.5047 |
1.74 |
1100 |
0.4082 |
0.7993 |
0.8362 |
0.8174 |
0.8927 |
0.5047 |
1.89 |
1200 |
0.3797 |
0.8240 |
0.8317 |
0.8278 |
0.8962 |
0.5047 |
2.05 |
1300 |
0.3597 |
0.8326 |
0.8331 |
0.8329 |
0.9020 |
0.5047 |
2.21 |
1400 |
0.3544 |
0.8462 |
0.8283 |
0.8371 |
0.9020 |
0.368 |
2.37 |
1500 |
0.3374 |
0.8428 |
0.8435 |
0.8432 |
0.9056 |
0.368 |
2.52 |
1600 |
0.3364 |
0.8406 |
0.8522 |
0.8464 |
0.9089 |
0.368 |
2.68 |
1700 |
0.3404 |
0.8467 |
0.8536 |
0.8501 |
0.9107 |
0.368 |
2.84 |
1800 |
0.3319 |
0.8405 |
0.8501 |
0.8453 |
0.9090 |
0.368 |
3.0 |
1900 |
0.3324 |
0.8584 |
0.8492 |
0.8538 |
0.9117 |
0.2949 |
3.15 |
2000 |
0.3204 |
0.8691 |
0.8404 |
0.8545 |
0.9119 |
0.2949 |
3.31 |
2100 |
0.3107 |
0.8599 |
0.8547 |
0.8573 |
0.9162 |
0.2949 |
3.47 |
2200 |
0.3169 |
0.8680 |
0.8489 |
0.8584 |
0.9146 |
0.2949 |
3.63 |
2300 |
0.3190 |
0.8683 |
0.8519 |
0.8600 |
0.9152 |
0.2949 |
3.79 |
2400 |
0.2975 |
0.8631 |
0.8617 |
0.8624 |
0.9182 |
0.2438 |
3.94 |
2500 |
0.3040 |
0.8566 |
0.8640 |
0.8603 |
0.9171 |
0.2438 |
4.1 |
2600 |
0.3045 |
0.8585 |
0.8642 |
0.8613 |
0.9181 |
0.2438 |
4.26 |
2700 |
0.3139 |
0.8498 |
0.8748 |
0.8621 |
0.9160 |
0.2438 |
4.42 |
2800 |
0.2985 |
0.8642 |
0.8672 |
0.8657 |
0.9214 |
0.2438 |
4.57 |
2900 |
0.3047 |
0.8688 |
0.8694 |
0.8691 |
0.9214 |
0.2028 |
4.73 |
3000 |
0.2986 |
0.8686 |
0.8695 |
0.8691 |
0.9207 |
0.2028 |
4.89 |
3100 |
0.3135 |
0.8628 |
0.8755 |
0.8691 |
0.9197 |
0.2028 |
5.05 |
3200 |
0.2927 |
0.8656 |
0.8755 |
0.8705 |
0.9217 |
0.2028 |
5.21 |
3300 |
0.2992 |
0.8724 |
0.8697 |
0.8711 |
0.9228 |
0.2028 |
5.36 |
3400 |
0.2975 |
0.8831 |
0.8639 |
0.8734 |
0.9244 |
0.1814 |
5.52 |
3500 |
0.2897 |
0.8736 |
0.8788 |
0.8762 |
0.9250 |
0.1814 |
5.68 |
3600 |
0.3118 |
0.8674 |
0.8751 |
0.8712 |
0.9216 |
0.1814 |
5.84 |
3700 |
0.2974 |
0.8735 |
0.8779 |
0.8757 |
0.9237 |
0.1814 |
5.99 |
3800 |
0.2957 |
0.8696 |
0.8815 |
0.8755 |
0.9240 |
0.1814 |
6.15 |
3900 |
0.3120 |
0.8698 |
0.8817 |
0.8757 |
0.9250 |
0.1602 |
6.31 |
4000 |
0.3080 |
0.8715 |
0.8800 |
0.8757 |
0.9238 |
0.1602 |
6.47 |
4100 |
0.3031 |
0.8767 |
0.8788 |
0.8777 |
0.9261 |
0.1602 |
6.62 |
4200 |
0.3146 |
0.8699 |
0.8784 |
0.8741 |
0.9227 |
0.1602 |
6.78 |
4300 |
0.3085 |
0.8717 |
0.8788 |
0.8752 |
0.9248 |
0.1602 |
6.94 |
4400 |
0.3023 |
0.8749 |
0.8756 |
0.8752 |
0.9250 |
0.1383 |
7.1 |
4500 |
0.3025 |
0.8860 |
0.8735 |
0.8797 |
0.9252 |
0.1383 |
7.26 |
4600 |
0.3026 |
0.8775 |
0.8810 |
0.8792 |
0.9272 |
0.1383 |
7.41 |
4700 |
0.3146 |
0.8715 |
0.8832 |
0.8773 |
0.9251 |
0.1383 |
7.57 |
4800 |
0.3113 |
0.8769 |
0.8803 |
0.8786 |
0.9275 |
0.1383 |
7.73 |
4900 |
0.3073 |
0.8797 |
0.8786 |
0.8792 |
0.9261 |
0.1306 |
7.89 |
5000 |
0.3163 |
0.8714 |
0.8828 |
0.8770 |
0.9248 |
0.1306 |
8.04 |
5100 |
0.3163 |
0.8753 |
0.8810 |
0.8781 |
0.9250 |
0.1306 |
8.2 |
5200 |
0.3132 |
0.8743 |
0.8804 |
0.8773 |
0.9257 |
0.1306 |
8.36 |
5300 |
0.3119 |
0.8735 |
0.8837 |
0.8786 |
0.9264 |
0.1306 |
8.52 |
5400 |
0.3145 |
0.8826 |
0.8779 |
0.8802 |
0.9272 |
0.1174 |
8.68 |
5500 |
0.3166 |
0.8776 |
0.8811 |
0.8794 |
0.9261 |
0.1174 |
8.83 |
5600 |
0.3146 |
0.8776 |
0.8814 |
0.8795 |
0.9260 |
0.1174 |
8.99 |
5700 |
0.3135 |
0.8763 |
0.8826 |
0.8795 |
0.9271 |
0.1174 |
9.15 |
5800 |
0.3154 |
0.8794 |
0.8818 |
0.8806 |
0.9275 |
0.1174 |
9.31 |
5900 |
0.3152 |
0.8788 |
0.8817 |
0.8802 |
0.9274 |
0.11 |
9.46 |
6000 |
0.3159 |
0.8765 |
0.8812 |
0.8789 |
0.9268 |
Framework Versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
đ License
This model is licensed under the CC - BY - NC - SA 4.0 license.