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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: distilbert-base-uncased-finetuned-mrpc
    results: []

distilbert-base-uncased-finetuned-mrpc

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: 0.5682
  • Accuracy: 0.7164
  • F1: 0.2022

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
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
0.5789 1.0 635 0.5764 0.7055 0.0
0.543 2.0 1270 0.5682 0.7164 0.2022
0.4661 3.0 1905 0.6358 0.7164 0.2812
0.2338 4.0 2540 0.9347 0.6844 0.4670
0.1538 5.0 3175 1.3556 0.6758 0.4740
0.1067 6.0 3810 1.6163 0.7016 0.3322
0.0928 7.0 4445 2.0786 0.6984 0.3609
0.0438 8.0 5080 2.1976 0.6945 0.4309
0.0312 9.0 5715 2.1931 0.6969 0.4209
0.0311 10.0 6350 2.4030 0.6883 0.4158
0.0281 11.0 6985 2.3715 0.7148 0.3739
0.0166 12.0 7620 2.6843 0.6984 0.3390
0.0167 13.0 8255 2.7291 0.6922 0.3604
0.0181 14.0 8890 2.7929 0.6906 0.3851
0.0147 15.0 9525 2.8976 0.7117 0.3303
0.0103 16.0 10160 3.0229 0.6859 0.3964
0.0047 17.0 10795 3.0616 0.6836 0.3817
0.0136 18.0 11430 3.0513 0.6875 0.3730
0.005 19.0 12065 3.0634 0.6930 0.3732
0.0042 20.0 12700 3.0611 0.7 0.3642

Framework versions

  • Transformers 4.28.1
  • Pytorch 2.0.1+cu117
  • Datasets 1.18.4
  • Tokenizers 0.12.1