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