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--- |
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license: cc-by-nc-sa-4.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- wildreceipt |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: layoutlmv3-finetuned-wildreceipt |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: wildreceipt |
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type: wildreceipt |
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config: WildReceipt |
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split: train |
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args: WildReceipt |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.877962408063198 |
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- name: Recall |
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type: recall |
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value: 0.8870235310306867 |
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- name: F1 |
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type: f1 |
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value: 0.8824697104524608 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9265109136777449 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# layoutlmv3-finetuned-wildreceipt |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the wildreceipt dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3129 |
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- Precision: 0.8780 |
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- Recall: 0.8870 |
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- F1: 0.8825 |
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- Accuracy: 0.9265 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- training_steps: 4000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 0.32 | 100 | 1.2240 | 0.6077 | 0.3766 | 0.4650 | 0.7011 | |
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| No log | 0.63 | 200 | 0.8417 | 0.6440 | 0.5089 | 0.5685 | 0.7743 | |
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| No log | 0.95 | 300 | 0.6466 | 0.7243 | 0.6583 | 0.6897 | 0.8311 | |
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| No log | 1.26 | 400 | 0.5516 | 0.7533 | 0.7158 | 0.7341 | 0.8537 | |
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| 0.9961 | 1.58 | 500 | 0.4845 | 0.7835 | 0.7557 | 0.7693 | 0.8699 | |
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| 0.9961 | 1.89 | 600 | 0.4506 | 0.7809 | 0.7930 | 0.7869 | 0.8770 | |
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| 0.9961 | 2.21 | 700 | 0.4230 | 0.8101 | 0.8107 | 0.8104 | 0.8886 | |
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| 0.9961 | 2.52 | 800 | 0.3797 | 0.8211 | 0.8296 | 0.8253 | 0.8983 | |
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| 0.9961 | 2.84 | 900 | 0.3576 | 0.8289 | 0.8411 | 0.8349 | 0.9016 | |
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| 0.4076 | 3.15 | 1000 | 0.3430 | 0.8394 | 0.8371 | 0.8382 | 0.9055 | |
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| 0.4076 | 3.47 | 1100 | 0.3354 | 0.8531 | 0.8405 | 0.8467 | 0.9071 | |
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| 0.4076 | 3.79 | 1200 | 0.3331 | 0.8371 | 0.8504 | 0.8437 | 0.9076 | |
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| 0.4076 | 4.1 | 1300 | 0.3184 | 0.8445 | 0.8609 | 0.8526 | 0.9118 | |
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| 0.4076 | 4.42 | 1400 | 0.3087 | 0.8617 | 0.8580 | 0.8598 | 0.9150 | |
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| 0.2673 | 4.73 | 1500 | 0.3013 | 0.8613 | 0.8657 | 0.8635 | 0.9177 | |
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| 0.2673 | 5.05 | 1600 | 0.2971 | 0.8630 | 0.8689 | 0.8659 | 0.9181 | |
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| 0.2673 | 5.36 | 1700 | 0.3075 | 0.8675 | 0.8639 | 0.8657 | 0.9177 | |
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| 0.2673 | 5.68 | 1800 | 0.2989 | 0.8551 | 0.8764 | 0.8656 | 0.9193 | |
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| 0.2673 | 5.99 | 1900 | 0.3011 | 0.8572 | 0.8762 | 0.8666 | 0.9194 | |
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| 0.2026 | 6.31 | 2000 | 0.3107 | 0.8595 | 0.8722 | 0.8658 | 0.9181 | |
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| 0.2026 | 6.62 | 2100 | 0.3050 | 0.8678 | 0.8800 | 0.8739 | 0.9220 | |
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| 0.2026 | 6.94 | 2200 | 0.2971 | 0.8722 | 0.8789 | 0.8755 | 0.9237 | |
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| 0.2026 | 7.26 | 2300 | 0.3057 | 0.8666 | 0.8785 | 0.8725 | 0.9209 | |
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| 0.2026 | 7.57 | 2400 | 0.3172 | 0.8593 | 0.8773 | 0.8682 | 0.9184 | |
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| 0.1647 | 7.89 | 2500 | 0.3018 | 0.8695 | 0.8823 | 0.8759 | 0.9228 | |
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| 0.1647 | 8.2 | 2600 | 0.3001 | 0.8760 | 0.8795 | 0.8777 | 0.9256 | |
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| 0.1647 | 8.52 | 2700 | 0.3068 | 0.8758 | 0.8745 | 0.8752 | 0.9235 | |
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| 0.1647 | 8.83 | 2800 | 0.3007 | 0.8779 | 0.8779 | 0.8779 | 0.9248 | |
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| 0.1647 | 9.15 | 2900 | 0.3063 | 0.8740 | 0.8763 | 0.8751 | 0.9228 | |
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| 0.1342 | 9.46 | 3000 | 0.3096 | 0.8675 | 0.8834 | 0.8754 | 0.9235 | |
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| 0.1342 | 9.78 | 3100 | 0.3052 | 0.8736 | 0.8848 | 0.8792 | 0.9249 | |
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| 0.1342 | 10.09 | 3200 | 0.3120 | 0.8727 | 0.8885 | 0.8805 | 0.9252 | |
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| 0.1342 | 10.41 | 3300 | 0.3146 | 0.8718 | 0.8843 | 0.8780 | 0.9243 | |
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| 0.1342 | 10.73 | 3400 | 0.3124 | 0.8720 | 0.8880 | 0.8799 | 0.9253 | |
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| 0.117 | 11.04 | 3500 | 0.3088 | 0.8761 | 0.8817 | 0.8789 | 0.9252 | |
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| 0.117 | 11.36 | 3600 | 0.3082 | 0.8782 | 0.8834 | 0.8808 | 0.9257 | |
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| 0.117 | 11.67 | 3700 | 0.3129 | 0.8767 | 0.8847 | 0.8807 | 0.9256 | |
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| 0.117 | 11.99 | 3800 | 0.3116 | 0.8792 | 0.8847 | 0.8820 | 0.9265 | |
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| 0.117 | 12.3 | 3900 | 0.3142 | 0.8768 | 0.8874 | 0.8821 | 0.9261 | |
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| 0.1022 | 12.62 | 4000 | 0.3129 | 0.8780 | 0.8870 | 0.8825 | 0.9265 | |
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### Framework versions |
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- Transformers 4.22.0.dev0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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