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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: 0.874880087707277 |
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- name: Recall |
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type: recall |
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value: 0.878491812302188 |
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- name: F1 |
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type: f1 |
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value: 0.8766822301565504 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9253043764396183 |
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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: 0.3111 |
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- Precision: 0.8749 |
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- Recall: 0.8785 |
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- F1: 0.8767 |
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- Accuracy: 0.9253 |
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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 | 1.3060 | 0.6792 | 0.3615 | 0.4718 | 0.6966 | |
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| No log | 0.63 | 200 | 0.8842 | 0.6524 | 0.5193 | 0.5783 | 0.7737 | |
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| No log | 0.95 | 300 | 0.6795 | 0.7338 | 0.6772 | 0.7044 | 0.8336 | |
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| No log | 1.26 | 400 | 0.5604 | 0.7719 | 0.7390 | 0.7551 | 0.8629 | |
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| 1.0319 | 1.58 | 500 | 0.4862 | 0.7819 | 0.7618 | 0.7717 | 0.8730 | |
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| 1.0319 | 1.89 | 600 | 0.4365 | 0.7852 | 0.7807 | 0.7829 | 0.8795 | |
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| 1.0319 | 2.21 | 700 | 0.4182 | 0.8162 | 0.8016 | 0.8088 | 0.8897 | |
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| 1.0319 | 2.52 | 800 | 0.3886 | 0.8126 | 0.8196 | 0.8161 | 0.8936 | |
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| 1.0319 | 2.84 | 900 | 0.3637 | 0.8260 | 0.8347 | 0.8303 | 0.9004 | |
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| 0.4162 | 3.15 | 1000 | 0.3482 | 0.8532 | 0.8243 | 0.8385 | 0.9062 | |
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| 0.4162 | 3.47 | 1100 | 0.3474 | 0.8573 | 0.8248 | 0.8407 | 0.9042 | |
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| 0.4162 | 3.79 | 1200 | 0.3325 | 0.8408 | 0.8435 | 0.8421 | 0.9086 | |
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| 0.4162 | 4.1 | 1300 | 0.3262 | 0.8468 | 0.8467 | 0.8468 | 0.9095 | |
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| 0.4162 | 4.42 | 1400 | 0.3237 | 0.8511 | 0.8442 | 0.8477 | 0.9100 | |
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| 0.2764 | 4.73 | 1500 | 0.3156 | 0.8563 | 0.8456 | 0.8509 | 0.9122 | |
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| 0.2764 | 5.05 | 1600 | 0.3032 | 0.8558 | 0.8566 | 0.8562 | 0.9153 | |
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| 0.2764 | 5.36 | 1700 | 0.3120 | 0.8604 | 0.8457 | 0.8530 | 0.9142 | |
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| 0.2764 | 5.68 | 1800 | 0.2976 | 0.8608 | 0.8592 | 0.8600 | 0.9178 | |
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| 0.2764 | 5.99 | 1900 | 0.3056 | 0.8551 | 0.8676 | 0.8613 | 0.9171 | |
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| 0.212 | 6.31 | 2000 | 0.3191 | 0.8528 | 0.8599 | 0.8563 | 0.9147 | |
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| 0.212 | 6.62 | 2100 | 0.3051 | 0.8653 | 0.8635 | 0.8644 | 0.9186 | |
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| 0.212 | 6.94 | 2200 | 0.3022 | 0.8681 | 0.8632 | 0.8657 | 0.9208 | |
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| 0.212 | 7.26 | 2300 | 0.3101 | 0.8605 | 0.8643 | 0.8624 | 0.9178 | |
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| 0.212 | 7.57 | 2400 | 0.3100 | 0.8553 | 0.8693 | 0.8622 | 0.9163 | |
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| 0.1725 | 7.89 | 2500 | 0.3012 | 0.8685 | 0.8723 | 0.8704 | 0.9221 | |
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| 0.1725 | 8.2 | 2600 | 0.3135 | 0.8627 | 0.8756 | 0.8691 | 0.9187 | |
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| 0.1725 | 8.52 | 2700 | 0.3115 | 0.8768 | 0.8671 | 0.8719 | 0.9229 | |
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| 0.1725 | 8.83 | 2800 | 0.3044 | 0.8757 | 0.8708 | 0.8732 | 0.9231 | |
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| 0.1725 | 9.15 | 2900 | 0.3042 | 0.8698 | 0.8658 | 0.8678 | 0.9212 | |
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| 0.142 | 9.46 | 3000 | 0.3095 | 0.8677 | 0.8702 | 0.8690 | 0.9207 | |
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| 0.142 | 9.78 | 3100 | 0.3119 | 0.8686 | 0.8762 | 0.8724 | 0.9229 | |
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| 0.142 | 10.09 | 3200 | 0.3078 | 0.8713 | 0.8774 | 0.8743 | 0.9238 | |
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| 0.142 | 10.41 | 3300 | 0.3123 | 0.8711 | 0.8753 | 0.8732 | 0.9238 | |
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| 0.142 | 10.73 | 3400 | 0.3098 | 0.8688 | 0.8774 | 0.8731 | 0.9232 | |
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| 0.1238 | 11.04 | 3500 | 0.3120 | 0.8737 | 0.8770 | 0.8754 | 0.9247 | |
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| 0.1238 | 11.36 | 3600 | 0.3124 | 0.8760 | 0.8768 | 0.8764 | 0.9251 | |
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| 0.1238 | 11.67 | 3700 | 0.3101 | 0.8770 | 0.8759 | 0.8764 | 0.9254 | |
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| 0.1238 | 11.99 | 3800 | 0.3103 | 0.8767 | 0.8774 | 0.8770 | 0.9255 | |
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| 0.1238 | 12.3 | 3900 | 0.3122 | 0.8740 | 0.8788 | 0.8764 | 0.9251 | |
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| 0.1096 | 12.62 | 4000 | 0.3111 | 0.8749 | 0.8785 | 0.8767 | 0.9253 | |
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### Framework versions |
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- Transformers 4.23.0.dev0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.5.1 |
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- Tokenizers 0.13.0 |
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