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jpassynk/distilbert-base-uncased-lora-text-classification

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face
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distilbert-base-uncased-lora-text-classification

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • —Loss: 0.9101
  • —Accuracy: {'accuracy': 0.895}

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: 0.001
  • —trainbatchsize: 4
  • —evalbatchsize: 4
  • —seed: 42
  • —optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • —lrschedulertype: linear
  • —num_epochs: 10

Training results

Training LossEpochStepValidation LossAccuracy
No log1.02500.3868{'accuracy': 0.866}
0.43032.05000.4039{'accuracy': 0.872}
0.43033.07500.6090{'accuracy': 0.882}
0.20014.010000.6951{'accuracy': 0.884}
0.20015.012500.6671{'accuracy': 0.89}
0.08556.015000.7451{'accuracy': 0.896}
0.08557.017500.8618{'accuracy': 0.894}
0.02778.020000.9029{'accuracy': 0.889}
0.02779.022500.9018{'accuracy': 0.89}
0.00610.025000.9101{'accuracy': 0.895}

Framework versions

  • —PEFT 0.17.1
  • —Transformers 4.57.0
  • —Pytorch 2.8.0+cu126
  • —Datasets 4.0.0
  • —Tokenizers 0.22.1