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gokuls/
hBERTv1_wnli

Name: hBERTv1_wnli
Availability: InStock

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hBERTv1_wnli

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

Loss: 0.6877
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
0.7359	1.0	3	0.7194	0.4366
0.6989	2.0	6	0.6899	0.5634
0.7031	3.0	9	0.7028	0.4366
0.7012	4.0	12	0.6889	0.5634
0.697	5.0	15	0.6894	0.5634
0.6971	6.0	18	0.7015	0.4366
0.7	7.0	21	0.6882	0.5634
0.6928	8.0	24	0.6890	0.5634
0.6932	9.0	27	0.6897	0.5634
0.6954	10.0	30	0.6956	0.4366
0.6962	11.0	33	0.6913	0.5634
0.6956	12.0	36	0.6877	0.5634
0.6973	13.0	39	0.6926	0.5070
0.6978	14.0	42	0.6933	0.4930
0.6945	15.0	45	0.6883	0.5634
0.6974	16.0	48	0.6881	0.5634
0.6936	17.0	51	0.6925	0.5211

Framework versions

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

gokuls/hBERTv1_wnli

hBERTv1_wnli

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gokuls/
hBERTv1_wnli