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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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- kantinewinkel |
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model-index: |
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- name: kantinewinkel-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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# kantinewinkel-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 kantinewinkel dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0083 |
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- Cash: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} |
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- Date: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 44} |
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- Subtotal: {'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19} |
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- Total: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} |
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- Overall Precision: 0.9886 |
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- Overall Recall: 1.0 |
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- Overall F1: 0.9943 |
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- Overall Accuracy: 0.9991 |
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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: 1000 |
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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 | Cash | Date | Subtotal | Total | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:----------------------------------------------------------:|:--------------------------------------------------------------------------:|:----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.0476 | 8.7 | 200 | 0.0083 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 44} | {'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | 0.9886 | 1.0 | 0.9943 | 0.9991 | |
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| 0.0003 | 17.39 | 400 | 0.0092 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 44} | {'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | 0.9886 | 1.0 | 0.9943 | 0.9991 | |
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| 0.0005 | 26.09 | 600 | 0.0080 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 44} | {'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | 0.9886 | 1.0 | 0.9943 | 0.9991 | |
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| 0.0 | 34.78 | 800 | 0.0082 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 44} | {'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | 0.9886 | 1.0 | 0.9943 | 0.9991 | |
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| 0.0001 | 43.48 | 1000 | 0.0081 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 44} | {'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19} | 0.9886 | 1.0 | 0.9943 | 0.9991 | |
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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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