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

sourceHugging Faceapache-2.0updated 2y 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 the None dataset. It achieves the following results on the evaluation set:

  • —Loss: 1.1492
  • —Accuracy: {'accuracy': 0.872}

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.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
  • —lrschedulertype: linear
  • —num_epochs: 10

Training results

Training LossEpochStepValidation LossAccuracy
No log1.02500.4627{'accuracy': 0.866}
0.37962.05000.5169{'accuracy': 0.87}
0.37963.07500.6853{'accuracy': 0.871}
0.16934.010000.8606{'accuracy': 0.869}
0.16935.012500.9956{'accuracy': 0.868}
0.03576.015001.0542{'accuracy': 0.868}
0.03577.017501.0670{'accuracy': 0.868}
0.02028.020001.1235{'accuracy': 0.87}
0.02029.022501.1426{'accuracy': 0.871}
0.002210.025001.1492{'accuracy': 0.872}

Framework versions

  • —PEFT 0.14.0
  • —Transformers 4.47.1
  • —Pytorch 2.5.1+cu121
  • —Datasets 3.2.0
  • —Tokenizers 0.21.0