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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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model-index: |
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- name: layoutlmv3-triplet |
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results: [] |
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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-triplet |
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0158 |
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- Aption: {'precision': 0.9251446070091868, 'recall': 0.9238871899422358, 'f1': 0.9245154709282557, 'number': 2943} |
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- Ootnote: {'precision': 0.9455411844792376, 'recall': 0.9442556084296397, 'f1': 0.9448979591836736, 'number': 2942} |
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- Overall Precision: 0.9353 |
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- Overall Recall: 0.9341 |
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- Overall F1: 0.9347 |
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- Overall Accuracy: 0.9982 |
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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: 3e-05 |
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- train_batch_size: 8 |
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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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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Aption | Ootnote | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.0152 | 1.0 | 8507 | 0.0147 | {'precision': 0.863031914893617, 'recall': 0.8820931022765885, 'f1': 0.8724584103512015, 'number': 2943} | {'precision': 0.902593085106383, 'recall': 0.9228416043507818, 'f1': 0.9126050420168068, 'number': 2942} | 0.8828 | 0.9025 | 0.8925 | 0.9969 | |
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| 0.0067 | 2.0 | 17014 | 0.0128 | {'precision': 0.9206239168110919, 'recall': 0.9024804621134896, 'f1': 0.911461908030199, 'number': 2943} | {'precision': 0.9459084604715673, 'recall': 0.9272603670972128, 'f1': 0.936491589426708, 'number': 2942} | 0.9333 | 0.9149 | 0.9240 | 0.9979 | |
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| 0.0049 | 3.0 | 25521 | 0.0153 | {'precision': 0.9005291005291005, 'recall': 0.8674821610601428, 'f1': 0.8836967808930426, 'number': 2943} | {'precision': 0.9435426958362738, 'recall': 0.9089055064581917, 'f1': 0.9259002770083102, 'number': 2942} | 0.9220 | 0.8882 | 0.9048 | 0.9971 | |
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| 0.0037 | 4.0 | 34028 | 0.0110 | {'precision': 0.9221803222488858, 'recall': 0.9140332993544003, 'f1': 0.9180887372013652, 'number': 2943} | {'precision': 0.946159122085048, 'recall': 0.9377974167233175, 'f1': 0.9419597132127007, 'number': 2942} | 0.9342 | 0.9259 | 0.9300 | 0.9981 | |
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| 0.0025 | 5.0 | 42535 | 0.0110 | {'precision': 0.9253680246490927, 'recall': 0.9184505606523955, 'f1': 0.9218963165075034, 'number': 2943} | {'precision': 0.9455665867853474, 'recall': 0.938817131203263, 'f1': 0.9421797714480641, 'number': 2942} | 0.9355 | 0.9286 | 0.9320 | 0.9981 | |
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| 0.0021 | 6.0 | 51042 | 0.0137 | {'precision': 0.9104477611940298, 'recall': 0.9119945633707102, 'f1': 0.911220505856391, 'number': 2943} | {'precision': 0.9331523583305056, 'recall': 0.9347382732834806, 'f1': 0.9339446425539141, 'number': 2942} | 0.9218 | 0.9234 | 0.9226 | 0.9978 | |
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| 0.0012 | 7.0 | 59549 | 0.0133 | {'precision': 0.9154399178363574, 'recall': 0.90859667006456, 'f1': 0.912005457025921, 'number': 2943} | {'precision': 0.9397260273972603, 'recall': 0.9326988443235894, 'f1': 0.9361992494029341, 'number': 2942} | 0.9276 | 0.9206 | 0.9241 | 0.9981 | |
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| 0.0013 | 8.0 | 68056 | 0.0194 | {'precision': 0.9192886456908345, 'recall': 0.9133537206931702, 'f1': 0.9163115732060677, 'number': 2943} | {'precision': 0.9442353746151214, 'recall': 0.938137321549966, 'f1': 0.9411764705882352, 'number': 2942} | 0.9318 | 0.9257 | 0.9287 | 0.9979 | |
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| 0.0007 | 9.0 | 76563 | 0.0143 | {'precision': 0.9239945466939332, 'recall': 0.9211688752973156, 'f1': 0.9225795473881231, 'number': 2943} | {'precision': 0.9457892942379816, 'recall': 0.9428959891230455, 'f1': 0.9443404255319149, 'number': 2942} | 0.9349 | 0.9320 | 0.9335 | 0.9982 | |
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| 0.0004 | 10.0 | 85070 | 0.0158 | {'precision': 0.9251446070091868, 'recall': 0.9238871899422358, 'f1': 0.9245154709282557, 'number': 2943} | {'precision': 0.9455411844792376, 'recall': 0.9442556084296397, 'f1': 0.9448979591836736, 'number': 2942} | 0.9353 | 0.9341 | 0.9347 | 0.9982 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.12.1 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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