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This model is a fine-tuned version of bert-base-cased on the GLUE MRPC dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6566
  • Accuracy: 0.8554
  • F1: 0.8974
  • Combined Score: 0.8764

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
  • 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: 3.0

Training results

Framework versions

  • Transformers 4.13.0.dev0
  • Pytorch 1.10.0+cu102
  • Datasets 1.15.2.dev0
  • Tokenizers 0.10.3
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Dataset used to train sgugger/glue-mrpc

Evaluation results