readme: ---
  license: apache-2.0
  lang:
  - en
  tags:
  - generated_from_trainer
  - spam
  - spam detection
  metrics:
  - precision
  - recall
  - accuracy
  - f1
  datasets:
  - SetFit/enron_spam
  model-index:
  - name: bert-tiny-finetuned-enron-spam-detection
    results: []
    
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  ---
  
  <!-- 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. -->
  
  # BERT-Tiny fine-tuned on Enron Spam Detection
  
  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.
  
  
  It achieves the following results on the evaluation set:
  - Loss: 0.0593
  - Precision: 0.9851
  - Recall: 0.9871
  - Accuracy: 0.986
  - F1: 0.9861
  
  ## 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: 16
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:------:|
  | 0.1125        | 1.0   | 1983 | 0.0797          | 0.9839    | 0.9692 | 0.9765   | 0.9765 |
  | 0.061         | 2.0   | 3966 | 0.0618          | 0.9822    | 0.9861 | 0.984    | 0.9842 |
  | 0.0486        | 3.0   | 5949 | 0.0593          | 0.9851    | 0.9871 | 0.986    | 0.9861 |
  | 0.048         | 4.0   | 7932 | 0.0588          | 0.9870    | 0.9821 | 0.9845   | 0.9846 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  