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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: microsoft/layoutlmv3-base |
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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: test |
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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: 0.8738394320043692 |
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- name: Recall |
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type: recall |
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value: 0.88093599449415 |
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- name: F1 |
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type: f1 |
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value: 0.8773733634930428 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9245552383044147 |
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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: 0.3068 |
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- Precision: 0.8738 |
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- Recall: 0.8809 |
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- F1: 0.8774 |
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- Accuracy: 0.9246 |
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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 | 1.3498 | 0.6130 | 0.3126 | 0.4140 | 0.6742 | |
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| No log | 0.63 | 200 | 0.8939 | 0.6665 | 0.5317 | 0.5915 | 0.7815 | |
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| No log | 0.95 | 300 | 0.7159 | 0.7311 | 0.6425 | 0.6840 | 0.8161 | |
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| No log | 1.26 | 400 | 0.5901 | 0.7554 | 0.6690 | 0.7095 | 0.8405 | |
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| 1.0677 | 1.58 | 500 | 0.5263 | 0.7632 | 0.7232 | 0.7427 | 0.8578 | |
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| 1.0677 | 1.89 | 600 | 0.4759 | 0.7871 | 0.7777 | 0.7824 | 0.8774 | |
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| 1.0677 | 2.21 | 700 | 0.4299 | 0.8054 | 0.8070 | 0.8062 | 0.8890 | |
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| 1.0677 | 2.52 | 800 | 0.4165 | 0.8064 | 0.8311 | 0.8185 | 0.8937 | |
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| 1.0677 | 2.84 | 900 | 0.3845 | 0.8344 | 0.8300 | 0.8322 | 0.9005 | |
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| 0.4267 | 3.15 | 1000 | 0.3540 | 0.8433 | 0.8318 | 0.8375 | 0.9056 | |
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| 0.4267 | 3.47 | 1100 | 0.3429 | 0.8362 | 0.8540 | 0.8450 | 0.9086 | |
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| 0.4267 | 3.79 | 1200 | 0.3274 | 0.8451 | 0.8545 | 0.8498 | 0.9105 | |
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| 0.4267 | 4.1 | 1300 | 0.3433 | 0.8397 | 0.8535 | 0.8466 | 0.9092 | |
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| 0.4267 | 4.42 | 1400 | 0.3181 | 0.8514 | 0.8604 | 0.8559 | 0.9154 | |
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| 0.2869 | 4.73 | 1500 | 0.3191 | 0.8472 | 0.8637 | 0.8554 | 0.9129 | |
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| 0.2869 | 5.05 | 1600 | 0.3128 | 0.8613 | 0.8658 | 0.8635 | 0.9182 | |
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| 0.2869 | 5.36 | 1700 | 0.3121 | 0.8622 | 0.8695 | 0.8658 | 0.9182 | |
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| 0.2869 | 5.68 | 1800 | 0.3230 | 0.8473 | 0.8661 | 0.8566 | 0.9140 | |
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| 0.2869 | 5.99 | 1900 | 0.2986 | 0.8729 | 0.8633 | 0.8681 | 0.9209 | |
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| 0.2134 | 6.31 | 2000 | 0.3032 | 0.8555 | 0.8694 | 0.8624 | 0.9169 | |
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| 0.2134 | 6.62 | 2100 | 0.3056 | 0.8705 | 0.8710 | 0.8708 | 0.9220 | |
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| 0.2134 | 6.94 | 2200 | 0.3122 | 0.8630 | 0.8790 | 0.8709 | 0.9217 | |
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| 0.2134 | 7.26 | 2300 | 0.3047 | 0.8692 | 0.8778 | 0.8734 | 0.9215 | |
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| 0.2134 | 7.57 | 2400 | 0.3103 | 0.8701 | 0.8780 | 0.8741 | 0.9225 | |
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| 0.1661 | 7.89 | 2500 | 0.3080 | 0.8712 | 0.8787 | 0.8749 | 0.9226 | |
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| 0.1661 | 8.2 | 2600 | 0.3011 | 0.8653 | 0.8834 | 0.8743 | 0.9236 | |
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| 0.1661 | 8.52 | 2700 | 0.3034 | 0.8735 | 0.8798 | 0.8766 | 0.9247 | |
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| 0.1661 | 8.83 | 2800 | 0.3054 | 0.8698 | 0.8793 | 0.8745 | 0.9238 | |
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| 0.1661 | 9.15 | 2900 | 0.3105 | 0.8697 | 0.8812 | 0.8754 | 0.9237 | |
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| 0.1415 | 9.46 | 3000 | 0.3068 | 0.8738 | 0.8809 | 0.8774 | 0.9246 | |
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| 0.1415 | 9.78 | 3100 | 0.3086 | 0.8730 | 0.8793 | 0.8761 | 0.9229 | |
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| 0.1415 | 10.09 | 3200 | 0.3013 | 0.8755 | 0.8830 | 0.8792 | 0.9256 | |
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| 0.1415 | 10.41 | 3300 | 0.3107 | 0.8692 | 0.8815 | 0.8753 | 0.9241 | |
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| 0.1415 | 10.73 | 3400 | 0.3073 | 0.8759 | 0.8794 | 0.8777 | 0.9261 | |
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| 0.1239 | 11.04 | 3500 | 0.3109 | 0.8727 | 0.8819 | 0.8773 | 0.9253 | |
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| 0.1239 | 11.36 | 3600 | 0.3124 | 0.8723 | 0.8790 | 0.8756 | 0.9243 | |
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| 0.1239 | 11.67 | 3700 | 0.3171 | 0.8724 | 0.8805 | 0.8764 | 0.9241 | |
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| 0.1239 | 11.99 | 3800 | 0.3081 | 0.8739 | 0.8804 | 0.8771 | 0.9254 | |
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| 0.1239 | 12.3 | 3900 | 0.3095 | 0.8735 | 0.8798 | 0.8766 | 0.9254 | |
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| 0.1106 | 12.62 | 4000 | 0.3094 | 0.8740 | 0.8796 | 0.8768 | 0.9254 | |
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### Framework versions |
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- Transformers 4.32.0.dev0 |
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- Pytorch 2.0.0 |
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- Datasets 2.14.3 |
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- Tokenizers 0.13.3 |
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