This model is a fine-tuned version of google/bert_uncased_L-12_H-128_A-2 on the massive dataset. It achieves the following results on the evaluation set:

Loss: 1.4592
Accuracy: 0.7452

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: 64
eval_batch_size: 64
seed: 33
distributed_type: multi-GPU
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.7918	1.0	180	3.4894	0.3025
3.2847	2.0	360	3.0212	0.3748
2.884	3.0	540	2.6641	0.4889
2.568	4.0	720	2.3904	0.5888
2.3179	5.0	900	2.1799	0.6439
2.1182	6.0	1080	2.0118	0.6714
1.9499	7.0	1260	1.8764	0.7004
1.816	8.0	1440	1.7611	0.7103
1.7109	9.0	1620	1.6734	0.7231
1.6164	10.0	1800	1.6047	0.7309
1.5424	11.0	1980	1.5481	0.7324
1.4871	12.0	2160	1.5112	0.7393
1.4401	13.0	2340	1.4808	0.7437
1.4179	14.0	2520	1.4667	0.7432
1.4025	15.0	2700	1.4592	0.7452

Framework versions

Transformers 4.34.0
Pytorch 1.14.0a0+410ce96
Datasets 2.14.5
Tokenizers 0.14.1

gokuls/bert_uncased_L-12_H-128_A-2_massive

bert_uncased_L-12_H-128_A-2_massive

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gokuls/
bert_uncased_L-12_H-128_A-2_massive