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metadata
language:
  - en
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
model-index:
  - name: hBERTv1_data_aug_qqp
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE QQP
          type: glue
          args: qqp
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8161513727430126
          - name: F1
            type: f1
            value: 0.7679145720798077

hBERTv1_data_aug_qqp

This model is a fine-tuned version of gokuls/bert_12_layer_model_v1 on the GLUE QQP dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5769
  • Accuracy: 0.8162
  • F1: 0.7679
  • Combined Score: 0.7920

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
0.2419 1.0 29671 0.5769 0.8162 0.7679 0.7920
0.104 2.0 59342 0.6327 0.8272 0.7769 0.8020
0.0911 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