metadata

language:
  - en
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
model-index:
  - name: hBERTv2_new_pretrain_48_KD_w_init_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: 0.7335641850111303
          - name: F1
            type: f1
            value: 0.6479278337037521

hBERTv2_new_pretrain_48_KD_w_init_qqp

This model is a fine-tuned version of gokuls/bert_12_layer_model_v2_complete_training_new_48_KD_wt_init on the GLUE QQP dataset. It achieves the following results on the evaluation set:

Loss: 0.5377
Accuracy: 0.7336
F1: 0.6479
Combined Score: 0.6907

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
0.5751	1.0	2843	0.5381	0.7260	0.6367	0.6813
0.522	2.0	5686	0.5377	0.7336	0.6479	0.6907
0.5274	3.0	8529	0.5656	0.7333	0.6147	0.6740
0.5435	4.0	11372	0.5561	0.7424	0.6314	0.6869
0.5405	5.0	14215	0.5772	0.7359	0.5543	0.6451
0.5468	6.0	17058	0.5563	0.7355	0.5944	0.6649
0.5732	7.0	19901	0.5929	0.7148	0.5017	0.6082

Framework versions

Transformers 4.30.2
Pytorch 1.14.0a0+410ce96
Datasets 2.13.0
Tokenizers 0.13.3