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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cord |
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model-index: |
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- name: cord-repo |
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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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# cord-repo |
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This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the cord dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2317 |
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- Menu.cnt: {'precision': 0.9521739130434783, 'recall': 0.9733333333333334, 'f1': 0.9626373626373628, 'number': 225} |
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- Menu.discountprice: {'precision': 0.6, 'recall': 0.6, 'f1': 0.6, 'number': 10} |
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- Menu.nm: {'precision': 0.9011406844106464, 'recall': 0.9404761904761905, 'f1': 0.920388349514563, 'number': 252} |
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- Menu.num: {'precision': 1.0, 'recall': 0.8181818181818182, 'f1': 0.9, 'number': 11} |
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- Menu.price: {'precision': 0.9565217391304348, 'recall': 0.9758064516129032, 'f1': 0.9660678642714571, 'number': 248} |
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- Menu.sub Cnt: {'precision': 0.875, 'recall': 0.8235294117647058, 'f1': 0.8484848484848485, 'number': 17} |
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- Menu.sub Nm: {'precision': 0.6666666666666666, 'recall': 0.8125, 'f1': 0.7323943661971831, 'number': 32} |
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- Menu.sub Price: {'precision': 1.0, 'recall': 0.7, 'f1': 0.8235294117647058, 'number': 20} |
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- Menu.unitprice: {'precision': 0.9253731343283582, 'recall': 0.9117647058823529, 'f1': 0.9185185185185185, 'number': 68} |
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- Sub Total.discount Price: {'precision': 0.8571428571428571, 'recall': 0.8571428571428571, 'f1': 0.8571428571428571, 'number': 7} |
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- Sub Total.etc: {'precision': 1.0, 'recall': 0.75, 'f1': 0.8571428571428571, 'number': 8} |
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- Sub Total.service Price: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12} |
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- Sub Total.subtotal Price: {'precision': 0.8205128205128205, 'recall': 0.927536231884058, 'f1': 0.870748299319728, 'number': 69} |
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- Sub Total.tax Price: {'precision': 1.0, 'recall': 0.9555555555555556, 'f1': 0.9772727272727273, 'number': 45} |
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- Total.cashprice: {'precision': 0.9393939393939394, 'recall': 0.8732394366197183, 'f1': 0.9051094890510948, 'number': 71} |
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- Total.changeprice: {'precision': 0.9661016949152542, 'recall': 0.95, 'f1': 0.957983193277311, 'number': 60} |
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- Total.creditcardprice: {'precision': 0.8235294117647058, 'recall': 0.875, 'f1': 0.8484848484848485, 'number': 16} |
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- Total.emoneyprice: {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2} |
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- Total.menuqty Cnt: {'precision': 0.71875, 'recall': 0.7666666666666667, 'f1': 0.7419354838709677, 'number': 30} |
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- Total.menutype Cnt: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} |
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- Total.total Etc: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} |
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- Total.total Price: {'precision': 0.9019607843137255, 'recall': 0.9292929292929293, 'f1': 0.9154228855721392, 'number': 99} |
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- Overall Precision: 0.9125 |
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- Overall Recall: 0.9201 |
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- Overall F1: 0.9163 |
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- Overall Accuracy: 0.9355 |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 300 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Menu.cnt | Menu.discountprice | Menu.nm | Menu.num | Menu.price | Menu.sub Cnt | Menu.sub Nm | Menu.sub Price | Menu.unitprice | Sub Total.discount Price | Sub Total.etc | Sub Total.service Price | Sub Total.subtotal Price | Sub Total.tax Price | Total.cashprice | Total.changeprice | Total.creditcardprice | Total.emoneyprice | Total.menuqty Cnt | Total.menutype Cnt | Total.total Etc | Total.total Price | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:----------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:--------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.6711 | 2.0 | 200 | 0.2317 | {'precision': 0.9521739130434783, 'recall': 0.9733333333333334, 'f1': 0.9626373626373628, 'number': 225} | {'precision': 0.6, 'recall': 0.6, 'f1': 0.6, 'number': 10} | {'precision': 0.9011406844106464, 'recall': 0.9404761904761905, 'f1': 0.920388349514563, 'number': 252} | {'precision': 1.0, 'recall': 0.8181818181818182, 'f1': 0.9, 'number': 11} | {'precision': 0.9565217391304348, 'recall': 0.9758064516129032, 'f1': 0.9660678642714571, 'number': 248} | {'precision': 0.875, 'recall': 0.8235294117647058, 'f1': 0.8484848484848485, 'number': 17} | {'precision': 0.6666666666666666, 'recall': 0.8125, 'f1': 0.7323943661971831, 'number': 32} | {'precision': 1.0, 'recall': 0.7, 'f1': 0.8235294117647058, 'number': 20} | {'precision': 0.9253731343283582, 'recall': 0.9117647058823529, 'f1': 0.9185185185185185, 'number': 68} | {'precision': 0.8571428571428571, 'recall': 0.8571428571428571, 'f1': 0.8571428571428571, 'number': 7} | {'precision': 1.0, 'recall': 0.75, 'f1': 0.8571428571428571, 'number': 8} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12} | {'precision': 0.8205128205128205, 'recall': 0.927536231884058, 'f1': 0.870748299319728, 'number': 69} | {'precision': 1.0, 'recall': 0.9555555555555556, 'f1': 0.9772727272727273, 'number': 45} | {'precision': 0.9393939393939394, 'recall': 0.8732394366197183, 'f1': 0.9051094890510948, 'number': 71} | {'precision': 0.9661016949152542, 'recall': 0.95, 'f1': 0.957983193277311, 'number': 60} | {'precision': 0.8235294117647058, 'recall': 0.875, 'f1': 0.8484848484848485, 'number': 16} | {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2} | {'precision': 0.71875, 'recall': 0.7666666666666667, 'f1': 0.7419354838709677, 'number': 30} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.9019607843137255, 'recall': 0.9292929292929293, 'f1': 0.9154228855721392, 'number': 99} | 0.9125 | 0.9201 | 0.9163 | 0.9355 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 1.13.1 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.2 |
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