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---
license: mit
tags:
- generated_from_trainer
datasets:
- cord
model-index:
- name: cord-repo
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cord-repo
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.
It achieves the following results on the evaluation set:
- Loss: 0.2317
- Menu.cnt: {'precision': 0.9521739130434783, 'recall': 0.9733333333333334, 'f1': 0.9626373626373628, 'number': 225}
- Menu.discountprice: {'precision': 0.6, 'recall': 0.6, 'f1': 0.6, 'number': 10}
- Menu.nm: {'precision': 0.9011406844106464, 'recall': 0.9404761904761905, 'f1': 0.920388349514563, 'number': 252}
- Menu.num: {'precision': 1.0, 'recall': 0.8181818181818182, 'f1': 0.9, 'number': 11}
- Menu.price: {'precision': 0.9565217391304348, 'recall': 0.9758064516129032, 'f1': 0.9660678642714571, 'number': 248}
- Menu.sub Cnt: {'precision': 0.875, 'recall': 0.8235294117647058, 'f1': 0.8484848484848485, 'number': 17}
- Menu.sub Nm: {'precision': 0.6666666666666666, 'recall': 0.8125, 'f1': 0.7323943661971831, 'number': 32}
- Menu.sub Price: {'precision': 1.0, 'recall': 0.7, 'f1': 0.8235294117647058, 'number': 20}
- Menu.unitprice: {'precision': 0.9253731343283582, 'recall': 0.9117647058823529, 'f1': 0.9185185185185185, 'number': 68}
- Sub Total.discount Price: {'precision': 0.8571428571428571, 'recall': 0.8571428571428571, 'f1': 0.8571428571428571, 'number': 7}
- Sub Total.etc: {'precision': 1.0, 'recall': 0.75, 'f1': 0.8571428571428571, 'number': 8}
- Sub Total.service Price: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12}
- Sub Total.subtotal Price: {'precision': 0.8205128205128205, 'recall': 0.927536231884058, 'f1': 0.870748299319728, 'number': 69}
- Sub Total.tax Price: {'precision': 1.0, 'recall': 0.9555555555555556, 'f1': 0.9772727272727273, 'number': 45}
- Total.cashprice: {'precision': 0.9393939393939394, 'recall': 0.8732394366197183, 'f1': 0.9051094890510948, 'number': 71}
- Total.changeprice: {'precision': 0.9661016949152542, 'recall': 0.95, 'f1': 0.957983193277311, 'number': 60}
- Total.creditcardprice: {'precision': 0.8235294117647058, 'recall': 0.875, 'f1': 0.8484848484848485, 'number': 16}
- Total.emoneyprice: {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2}
- Total.menuqty Cnt: {'precision': 0.71875, 'recall': 0.7666666666666667, 'f1': 0.7419354838709677, 'number': 30}
- Total.menutype Cnt: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}
- Total.total Etc: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
- Total.total Price: {'precision': 0.9019607843137255, 'recall': 0.9292929292929293, 'f1': 0.9154228855721392, 'number': 99}
- Overall Precision: 0.9125
- Overall Recall: 0.9201
- Overall F1: 0.9163
- Overall Accuracy: 0.9355
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 300
- mixed_precision_training: Native AMP
### Training results
| 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:----------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:--------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 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 |
### Framework versions
- Transformers 4.29.2
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.2
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