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--- |
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library_name: transformers |
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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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model-index: |
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- name: bert-practice-classifier |
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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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# bert-practice-classifier |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7264 |
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- Accuracy: 0.375 |
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- Auc: 0.133 |
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- Precision: 0.333 |
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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: 0.0002 |
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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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:---------:| |
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| 0.6963 | 1.0 | 4 | 0.7382 | 0.375 | 0.133 | 0.375 | |
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| 0.6877 | 2.0 | 8 | 0.7270 | 0.375 | 0.133 | 0.375 | |
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| 0.6984 | 3.0 | 12 | 0.7126 | 0.25 | 0.067 | 0.2 | |
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| 0.6871 | 4.0 | 16 | 0.7091 | 0.375 | 0.133 | 0.0 | |
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| 0.6912 | 5.0 | 20 | 0.7012 | 0.5 | 0.133 | 0.0 | |
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| 0.6867 | 6.0 | 24 | 0.7062 | 0.5 | 0.133 | 0.0 | |
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| 0.6862 | 7.0 | 28 | 0.7095 | 0.375 | 0.133 | 0.0 | |
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| 0.6639 | 8.0 | 32 | 0.7177 | 0.25 | 0.133 | 0.0 | |
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| 0.67 | 9.0 | 36 | 0.7239 | 0.125 | 0.133 | 0.0 | |
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| 0.6597 | 10.0 | 40 | 0.7264 | 0.375 | 0.133 | 0.333 | |
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### Framework versions |
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- Transformers 4.50.0 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
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