| | --- |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: distilbert-base-uncased |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: Basic-Spam-Smish-Classifier |
| | 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. --> |
| |
|
| | # Basic-Spam-Smish-Classifier |
| |
|
| | This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0760 |
| | - Accuracy: 0.9965 |
| |
|
| | ## 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: 2e-05 |
| | - train_batch_size: 32 |
| | - eval_batch_size: 64 |
| | - seed: 42 |
| | - distributed_type: multi-GPU |
| | - num_devices: 4 |
| | - total_train_batch_size: 128 |
| | - total_eval_batch_size: 256 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 0.06 |
| | - num_epochs: 3 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | | 1.2331 | 1.0 | 63 | 0.1649 | 0.9890 | |
| | | 0.1547 | 2.0 | 126 | 0.0875 | 0.9955 | |
| | | 0.0621 | 3.0 | 189 | 0.0760 | 0.9965 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 5.2.0 |
| | - Pytorch 2.10.0+cu128 |
| | - Datasets 4.5.0 |
| | - Tokenizers 0.22.2 |
| |
|
