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hBERTv2_data_aug_mnli

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

  • Loss: 1.0988
  • Accuracy: 0.3182

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
1.0988 1.0 31440 1.0988 0.3182
1.0985 2.0 62880 1.0992 0.3182
1.0985 3.0 94320 1.0991 0.3182
1.0985 4.0 125760 1.0991 0.3182
1.0985 5.0 157200 1.0988 0.3182
1.0985 6.0 188640 1.0988 0.3182
1.0985 7.0 220080 1.0988 0.3182
1.0985 8.0 251520 1.0988 0.3182
1.0985 9.0 282960 1.0988 0.3182
1.0985 10.0 314400 1.0988 0.3182
1.0985 11.0 345840 1.0988 0.3182
1.0985 12.0 377280 1.0988 0.3182
1.0985 13.0 408720 1.0988 0.3182
1.0985 14.0 440160 1.0988 0.3182
1.0985 15.0 471600 1.0988 0.3182

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_mnli

Evaluation results