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hBERTv2_data_aug_qqp

This model is a fine-tuned version of gokuls/bert_12_layer_model_v2 on the GLUE QQP dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6782
  • Accuracy: 0.6318
  • F1: 0.0
  • Combined Score: 0.3159

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: 256
  • eval_batch_size: 256
  • seed: 10
  • distributed_type: multi-GPU
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Combined Score
0.6528 1.0 29671 0.6782 0.6318 0.0 0.3159
0.6407 2.0 59342 nan 0.6318 0.0 0.3159
0.0 3.0 89013 nan 0.6318 0.0 0.3159
0.0 4.0 118684 nan 0.6318 0.0 0.3159
0.0 5.0 148355 nan 0.6318 0.0 0.3159
0.0 6.0 178026 nan 0.6318 0.0 0.3159

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.14.0a0+410ce96
  • Datasets 2.10.1
  • Tokenizers 0.13.2
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Dataset used to train gokuls/hBERTv2_data_aug_qqp

Evaluation results