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gokuls/
hBERTv2_data_aug_wnli

Name: hBERTv2_data_aug_wnli
Availability: InStock

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hBERTv2_data_aug_wnli

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

Loss: 0.6873
Accuracy: 0.5634

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
0.699	1.0	218	0.6895	0.5634
0.6947	2.0	436	0.6886	0.5634
0.6935	3.0	654	0.6873	0.5634
0.6937	4.0	872	0.6921	0.5634
0.6934	5.0	1090	0.6892	0.5634
0.6932	6.0	1308	0.6911	0.5634
0.6933	7.0	1526	0.6955	0.4366
0.6931	8.0	1744	0.6908	0.5634

Framework versions

Transformers 4.26.1
Pytorch 1.14.0a0+410ce96
Datasets 2.10.1
Tokenizers 0.13.2

gokuls/hBERTv2_data_aug_wnli

hBERTv2_data_aug_wnli

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gokuls/
hBERTv2_data_aug_wnli