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BW7898/spam_message_classification

sourceHugging Faceapache-2.0updated 3y agoView on Hugging Face
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Model Card

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spammessageclassification

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • —Loss: 0.0878
  • —Accuracy: 0.9884

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: 5e-05
  • —trainbatchsize: 64
  • —evalbatchsize: 64
  • —seed: 42
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: linear
  • —num_epochs: 10

Training results

Training LossEpochStepValidation LossAccuracy
No log1.02970.05590.9816
0.09662.05940.08330.9745
0.09663.08910.07230.9819
0.01614.011880.08130.9859
0.01615.014850.07990.9852
0.00296.017820.07830.9873
0.00137.020790.08660.9882
0.00138.023760.08620.9884
0.00029.026730.09330.9873
0.000210.029700.08780.9884

Framework versions

  • —Transformers 4.32.0
  • —Pytorch 2.0.1+cu118
  • —Datasets 2.14.4
  • —Tokenizers 0.13.3