README.md

metadata

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
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: sms_spam
          type: sms_spam
          config: plain_text
          split: train
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9919268030139935
            verified: true
          - name: Precision
            type: precision
            value: 0.9915966386554622
            verified: true
          - name: Recall
            type: recall
            value: 0.9477911646586346
            verified: true
          - name: AUC
            type: auc
            value: 0.9765156891636706
            verified: true
          - name: F1
            type: f1
            value: 0.9691991786447638
            verified: true
          - name: loss
            type: loss
            value: 0.06180405616760254
            verified: true

MiniLMv2-L12-H384-distilled-finetuned-spam-detection

This model is a fine-tuned version of 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