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--- |
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license: mit |
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base_model: microsoft/layoutlm-base-uncased |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: layoutlm-cord |
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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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# layoutlm-cord |
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This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- eval_loss: 0.1624 |
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- eval_enu.cnt: {'precision': 0.9861111111111112, 'recall': 0.9681818181818181, 'f1': 0.9770642201834862, 'number': 220} |
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- eval_enu.discountprice: {'precision': 0.6666666666666666, 'recall': 0.6, 'f1': 0.631578947368421, 'number': 10} |
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- eval_enu.etc: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} |
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- eval_enu.itemsubtotal: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} |
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- eval_enu.nm: {'precision': 0.9525691699604744, 'recall': 0.9601593625498008, 'f1': 0.9563492063492064, 'number': 251} |
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- eval_enu.num: {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} |
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- eval_enu.price: {'precision': 0.9568627450980393, 'recall': 0.991869918699187, 'f1': 0.9740518962075848, 'number': 246} |
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- eval_enu.sub.cnt: {'precision': 0.85, 'recall': 1.0, 'f1': 0.9189189189189189, 'number': 17} |
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- eval_enu.sub.nm: {'precision': 0.8285714285714286, 'recall': 0.9354838709677419, 'f1': 0.8787878787878788, 'number': 31} |
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- eval_enu.sub.price: {'precision': 1.0, 'recall': 0.95, 'f1': 0.9743589743589743, 'number': 20} |
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- eval_enu.unitprice: {'precision': 0.984375, 'recall': 0.9402985074626866, 'f1': 0.9618320610687023, 'number': 67} |
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- eval_otal.cashprice: {'precision': 0.9558823529411765, 'recall': 0.9558823529411765, 'f1': 0.9558823529411765, 'number': 68} |
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- eval_otal.changeprice: {'precision': 0.9655172413793104, 'recall': 1.0, 'f1': 0.9824561403508771, 'number': 56} |
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- eval_otal.creditcardprice: {'precision': 0.7647058823529411, 'recall': 0.8125, 'f1': 0.787878787878788, 'number': 16} |
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- eval_otal.emoneyprice: {'precision': 0.3333333333333333, 'recall': 0.5, 'f1': 0.4, 'number': 2} |
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- eval_otal.menuqty_cnt: {'precision': 0.9333333333333333, 'recall': 0.9655172413793104, 'f1': 0.9491525423728815, 'number': 29} |
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- eval_otal.menutype_cnt: {'precision': 1.0, 'recall': 0.7142857142857143, 'f1': 0.8333333333333333, 'number': 7} |
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- eval_otal.total_etc: {'precision': 0.5, 'recall': 0.3333333333333333, 'f1': 0.4, 'number': 3} |
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- eval_otal.total_price: {'precision': 0.9583333333333334, 'recall': 0.968421052631579, 'f1': 0.9633507853403142, 'number': 95} |
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- eval_ub_total.discount_price: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 7} |
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- eval_ub_total.etc: {'precision': 0.875, 'recall': 0.7777777777777778, 'f1': 0.823529411764706, 'number': 9} |
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- eval_ub_total.service_price: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12} |
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- eval_ub_total.subtotal_price: {'precision': 0.9545454545454546, 'recall': 0.9692307692307692, 'f1': 0.9618320610687022, 'number': 65} |
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- eval_ub_total.tax_price: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 43} |
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- eval_overall_precision: 0.9522 |
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- eval_overall_recall: 0.9544 |
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- eval_overall_f1: 0.9533 |
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- eval_overall_accuracy: 0.9707 |
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- eval_runtime: 3.0438 |
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- eval_samples_per_second: 32.853 |
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- eval_steps_per_second: 4.271 |
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- epoch: 1.0 |
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- step: 50 |
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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-06 |
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- train_batch_size: 16 |
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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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- num_epochs: 15 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.36.0 |
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- Pytorch 2.0.0 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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