readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - sms_spam
  metrics:
  - accuracy
  model-index:
  - name: MiniLMv2-L12-H384-distilled-finetuned-spam-detection
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: sms_spam
        type: sms_spam
        args: plain_text
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9928263988522238
  ---
  
  <!-- 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. -->
  
  # MiniLMv2-L12-H384-distilled-finetuned-spam-detection
  
  This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large) on the sms_spam dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0938
  - Accuracy: 0.9928
  
  ## 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: 0.0001
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 33
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 6
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.4101        | 1.0   | 131  | 0.4930          | 0.9763   |
  | 0.8003        | 2.0   | 262  | 0.3999          | 0.9799   |
  | 0.377         | 3.0   | 393  | 0.3196          | 0.9828   |
  | 0.302         | 4.0   | 524  | 0.3462          | 0.9828   |
  | 0.1945        | 5.0   | 655  | 0.1094          | 0.9928   |
  | 0.1393        | 6.0   | 786  | 0.0938          | 0.9928   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.2+cu113
  - Datasets 1.18.4
  - Tokenizers 0.12.1
  