You can deploy this model and start earning money today!

gokuls/
hBERTv1_qnli

Name: hBERTv1_qnli
Availability: InStock

generated_from_trainer

Views

No views yet

hBERTv1_qnli

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

Loss: 0.4198
Accuracy: 0.8113

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
0.6667	1.0	410	0.5955	0.6874
0.4998	2.0	820	0.4486	0.7948
0.3985	3.0	1230	0.4198	0.8113
0.3106	4.0	1640	0.4841	0.7866
0.2286	5.0	2050	0.5340	0.7906
0.1662	6.0	2460	0.6282	0.7728
0.1237	7.0	2870	0.6678	0.7752
0.0945	8.0	3280	0.7668	0.7752

Framework versions

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

gokuls/hBERTv1_qnli

hBERTv1_qnli

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gokuls/
hBERTv1_qnli