metadata

language:
  - en
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
model-index:
  - name: hBERTv2_new_pretrain_qqp
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE QQP
          type: glue
          config: qqp
          split: validation
          args: qqp
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7902547613158546
          - name: F1
            type: f1
            value: 0.7136682874122096

hBERTv2_new_pretrain_qqp

This model is a fine-tuned version of gokuls/bert_12_layer_model_v2_complete_training_new on the GLUE QQP dataset. It achieves the following results on the evaluation set:

Loss: 0.4399
Accuracy: 0.7903
F1: 0.7137
Combined Score: 0.7520

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: 4e-05
train_batch_size: 128
eval_batch_size: 128
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

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Combined Score
0.5303	1.0	2843	0.4893	0.7608	0.6250	0.6929
0.4677	2.0	5686	0.4781	0.7773	0.6831	0.7302
0.4229	3.0	8529	0.4399	0.7903	0.7137	0.7520
0.3712	4.0	11372	0.4426	0.8018	0.7163	0.7590
0.3268	5.0	14215	0.4515	0.8107	0.7348	0.7728
0.2925	6.0	17058	0.5221	0.8119	0.7227	0.7673
0.2614	7.0	19901	0.4518	0.8058	0.7527	0.7792
0.2389	8.0	22744	0.5231	0.8134	0.7601	0.7868

Framework versions

Transformers 4.29.2
Pytorch 1.14.0a0+410ce96
Datasets 2.12.0
Tokenizers 0.13.3