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--- |
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license: apache-2.0 |
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lang: |
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- en |
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tags: |
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- generated_from_trainer |
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- spam |
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- spam detection |
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metrics: |
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- precision |
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- recall |
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- accuracy |
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- f1 |
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datasets: |
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- SetFit/enron_spam |
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model-index: |
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- name: bert-tiny-finetuned-enron-spam-detection |
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results: [] |
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widget: |
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- text: "aquila dave marks just got a call from someone at aquila saying they got a corporate - wide e - mail saying they shouldn ' t trade on enrononline anymore . - r" |
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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-Tiny fine-tuned on Enron Spam Detection |
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This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) (aka BERT-Tiny) on an [SetFit/enron_spam](https://huggingface.co/datasets/SetFit/enron_spam) for `Spam Dectection` downstream task. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0593 |
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- Precision: 0.9851 |
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- Recall: 0.9871 |
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- Accuracy: 0.986 |
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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: 16 |
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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: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:------:| |
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| 0.1125 | 1.0 | 1983 | 0.0797 | 0.9839 | 0.9692 | 0.9765 | 0.9765 | |
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| 0.061 | 2.0 | 3966 | 0.0618 | 0.9822 | 0.9861 | 0.984 | 0.9842 | |
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| 0.0486 | 3.0 | 5949 | 0.0593 | 0.9851 | 0.9871 | 0.986 | 0.9861 | |
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| 0.048 | 4.0 | 7932 | 0.0588 | 0.9870 | 0.9821 | 0.9845 | 0.9846 | |
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### Framework versions |
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- Transformers 4.23.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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