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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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- f1 |
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model-index: |
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- name: scam-alert-bert |
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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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# scam-alert-bert |
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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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- Loss: 0.1215 |
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- Accuracy: 0.9861 |
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- F1: 0.9861 |
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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: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 2 |
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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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- num_epochs: 6 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:| |
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| No log | 0.1577 | 100 | 0.0723 | 0.9841 | 0.9841 | |
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| No log | 0.3155 | 200 | 0.0520 | 0.9900 | 0.9899 | |
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| No log | 0.4732 | 300 | 0.0825 | 0.9821 | 0.9823 | |
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| No log | 0.6309 | 400 | 0.0722 | 0.9861 | 0.9857 | |
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| No log | 0.7886 | 500 | 0.0521 | 0.9861 | 0.9859 | |
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| No log | 0.9464 | 600 | 0.0905 | 0.9761 | 0.9765 | |
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| No log | 1.1041 | 700 | 0.0675 | 0.9821 | 0.9822 | |
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| No log | 1.2618 | 800 | 0.0661 | 0.9900 | 0.9899 | |
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| No log | 1.4196 | 900 | 0.0695 | 0.9861 | 0.9861 | |
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| No log | 1.5773 | 1000 | 0.0780 | 0.9880 | 0.9880 | |
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| No log | 1.7350 | 1100 | 0.0877 | 0.9861 | 0.9858 | |
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| No log | 1.8927 | 1200 | 0.0714 | 0.9880 | 0.9880 | |
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| No log | 2.0505 | 1300 | 0.0856 | 0.9841 | 0.9841 | |
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| No log | 2.2082 | 1400 | 0.0930 | 0.9841 | 0.9842 | |
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| No log | 2.3659 | 1500 | 0.0886 | 0.9861 | 0.9860 | |
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| No log | 2.5237 | 1600 | 0.0982 | 0.9861 | 0.9861 | |
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| No log | 2.6814 | 1700 | 0.0854 | 0.9861 | 0.9860 | |
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| No log | 2.8391 | 1800 | 0.0949 | 0.9861 | 0.9860 | |
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| No log | 2.9968 | 1900 | 0.0935 | 0.9880 | 0.9880 | |
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| No log | 3.1546 | 2000 | 0.1004 | 0.9861 | 0.9860 | |
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| No log | 3.3123 | 2100 | 0.1223 | 0.9801 | 0.9803 | |
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| No log | 3.4700 | 2200 | 0.1146 | 0.9861 | 0.9861 | |
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| No log | 3.6278 | 2300 | 0.1148 | 0.9861 | 0.9861 | |
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| No log | 3.7855 | 2400 | 0.1074 | 0.9861 | 0.9860 | |
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| No log | 3.9432 | 2500 | 0.1082 | 0.9861 | 0.9860 | |
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| 0.0249 | 4.1009 | 2600 | 0.1272 | 0.9841 | 0.9842 | |
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| 0.0249 | 4.2587 | 2700 | 0.1264 | 0.9841 | 0.9842 | |
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| 0.0249 | 4.4164 | 2800 | 0.1161 | 0.9861 | 0.9861 | |
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| 0.0249 | 4.5741 | 2900 | 0.1242 | 0.9861 | 0.9861 | |
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| 0.0249 | 4.7319 | 3000 | 0.1227 | 0.9861 | 0.9861 | |
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| 0.0249 | 4.8896 | 3100 | 0.1223 | 0.9861 | 0.9861 | |
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| 0.0249 | 5.0473 | 3200 | 0.1207 | 0.9861 | 0.9861 | |
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| 0.0249 | 5.2050 | 3300 | 0.1210 | 0.9861 | 0.9861 | |
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| 0.0249 | 5.3628 | 3400 | 0.1214 | 0.9861 | 0.9861 | |
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| 0.0249 | 5.5205 | 3500 | 0.1214 | 0.9861 | 0.9861 | |
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| 0.0249 | 5.6782 | 3600 | 0.1215 | 0.9861 | 0.9861 | |
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| 0.0249 | 5.8360 | 3700 | 0.1215 | 0.9861 | 0.9861 | |
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| 0.0249 | 5.9937 | 3800 | 0.1215 | 0.9861 | 0.9861 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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