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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: my_awesome_model |
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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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# misdirection_classification |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Accuracy: 0.6937 |
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- Precision: 0.6959 |
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- Recall: 0.6937 |
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- F1: 0.6829 |
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- Roc Auc: 0.6835 |
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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: 4.890081827045594e-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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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: 9 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:| |
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| 0.6496 | 1.0 | 28 | 0.6713 | 0.6216 | 0.6683 | 0.6216 | 0.5502 | 0.6567 | |
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| 0.4718 | 2.0 | 56 | 0.7125 | 0.6486 | 0.6463 | 0.6486 | 0.6348 | 0.6667 | |
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| 0.2081 | 3.0 | 84 | 0.9041 | 0.6937 | 0.6959 | 0.6937 | 0.6829 | 0.6835 | |
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| 0.1064 | 4.0 | 112 | 1.1360 | 0.6667 | 0.6720 | 0.6667 | 0.6474 | 0.6928 | |
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| 0.0202 | 5.0 | 140 | 1.1922 | 0.6577 | 0.6543 | 0.6577 | 0.6540 | 0.6928 | |
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| 0.1239 | 6.0 | 168 | 1.4047 | 0.6667 | 0.6690 | 0.6667 | 0.6506 | 0.6753 | |
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| 0.074 | 7.0 | 196 | 1.3902 | 0.6486 | 0.6448 | 0.6486 | 0.6441 | 0.6683 | |
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| 0.0738 | 8.0 | 224 | 1.4045 | 0.6486 | 0.6478 | 0.6486 | 0.6482 | 0.6650 | |
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| 0.0458 | 9.0 | 252 | 1.4082 | 0.6306 | 0.6316 | 0.6306 | 0.6311 | 0.6634 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Tokenizers 0.19.1 |
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