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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: google-bert/bert-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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model-index: |
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- name: spam-detection_m1 |
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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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# spam-detection_m1 |
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This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an [spam-detection](https://huggingface.co/datasets/vishnun0027/spam-detection) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0202 |
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- Accuracy: 0.9967 |
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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: 32 |
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- eval_batch_size: 32 |
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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: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 256 | 0.1144 | 0.9919 | |
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| 0.22 | 2.0 | 512 | 0.0483 | 0.9923 | |
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| 0.22 | 3.0 | 768 | 0.0321 | 0.9949 | |
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| 0.0361 | 4.0 | 1024 | 0.0275 | 0.9949 | |
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| 0.0361 | 5.0 | 1280 | 0.0245 | 0.9952 | |
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| 0.0233 | 6.0 | 1536 | 0.0232 | 0.9960 | |
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| 0.0233 | 7.0 | 1792 | 0.0220 | 0.9967 | |
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| 0.0171 | 8.0 | 2048 | 0.0209 | 0.9967 | |
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| 0.0171 | 9.0 | 2304 | 0.0211 | 0.9967 | |
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| 0.0148 | 10.0 | 2560 | 0.0202 | 0.9967 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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