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layoutlmv3-finetuned-wildreceipt

Token Classification
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layoutlmv3
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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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+
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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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+
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+ # layoutlmv3-finetuned-wildreceipt
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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