CoolFace
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autoevaluate/glue-mrpc

sourceHugging Faceapache-2.0updated 4y agoView on Hugging Face
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Model Card

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glue-mrpc

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

  • Loss: 0.3654
  • Accuracy: 0.8554
  • F1: 0.8998

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: 2e-05
  • trainbatchsize: 16
  • evalbatchsize: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lrschedulertype: linear
  • num_epochs: 5

Training results

Training LossEpochStepValidation LossAccuracyF1
No log1.02300.40390.80390.8611
No log2.04600.36540.85540.8998
0.43683.06900.41460.84070.8885
0.43684.09200.57560.84560.8941
0.17445.011500.55230.84560.8916

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.11.0
  • Datasets 2.3.2
  • Tokenizers 0.11.6