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bert-finetuned-mrpc

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: 0.5152
  • Accuracy: 0.8603
  • F1: 0.9032
  • Combined Score: 0.8818

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: 8
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 2
  • total_train_batch_size: 16
  • total_eval_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3.0

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Combined Score
No log 1.0 230 0.3668 0.8431 0.8881 0.8656
No log 2.0 460 0.3751 0.8578 0.9017 0.8798
0.4264 3.0 690 0.5152 0.8603 0.9032 0.8818

Framework versions

  • Transformers 4.11.0.dev0
  • Pytorch 1.8.1+cu111
  • Datasets 1.10.3.dev0
  • Tokenizers 0.10.3
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Dataset used to train sgugger/bert-finetuned-mrpc

Evaluation results