| --- |
| license: apache-2.0 |
| base_model: distilbert/distilbert-base-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: distilbert-scam-classifier-v1.1 |
| 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. --> |
|
|
| # distilbert-scam-classifier-v1.1 |
|
|
| This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0022 |
| - Accuracy: {'accuracy': 1.0} |
| - Precision: {'precision': 1.0} |
| - Recall: {'recall': 1.0} |
| - F1: {'f1': 1.0} |
|
|
| ## 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: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 8 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------------------:|:---------------------------------:|:------------------:|:--------------------------:| |
| | No log | 1.0 | 40 | 0.0981 | {'accuracy': 0.9875} | {'precision': 0.9878048780487806} | {'recall': 0.9875} | {'f1': 0.9874980465697764} | |
| | No log | 2.0 | 80 | 0.0588 | {'accuracy': 0.9875} | {'precision': 0.9878048780487806} | {'recall': 0.9875} | {'f1': 0.9874980465697764} | |
| | No log | 3.0 | 120 | 0.0035 | {'accuracy': 1.0} | {'precision': 1.0} | {'recall': 1.0} | {'f1': 1.0} | |
| | No log | 4.0 | 160 | 0.0033 | {'accuracy': 1.0} | {'precision': 1.0} | {'recall': 1.0} | {'f1': 1.0} | |
| | No log | 5.0 | 200 | 0.0029 | {'accuracy': 1.0} | {'precision': 1.0} | {'recall': 1.0} | {'f1': 1.0} | |
| | No log | 6.0 | 240 | 0.0028 | {'accuracy': 1.0} | {'precision': 1.0} | {'recall': 1.0} | {'f1': 1.0} | |
| | No log | 7.0 | 280 | 0.0025 | {'accuracy': 1.0} | {'precision': 1.0} | {'recall': 1.0} | {'f1': 1.0} | |
| | No log | 8.0 | 320 | 0.0022 | {'accuracy': 1.0} | {'precision': 1.0} | {'recall': 1.0} | {'f1': 1.0} | |
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|
| ### Framework versions |
|
|
| - Transformers 4.40.1 |
| - Pytorch 2.2.1+cu121 |
| - Datasets 2.19.0 |
| - Tokenizers 0.19.1 |
|
|
