This model is a fine-tuned version of google/bert_uncased_L-2_H-768_A-12 on the massive dataset. It achieves the following results on the evaluation set:

Loss: 0.5434
Accuracy: 0.8746

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
2.5143	1.0	180	1.2564	0.7024
1.0135	2.0	360	0.7279	0.8205
0.6173	3.0	540	0.5817	0.8559
0.433	4.0	720	0.5234	0.8598
0.312	5.0	900	0.5019	0.8657
0.23	6.0	1080	0.5028	0.8711
0.1742	7.0	1260	0.5037	0.8682
0.1314	8.0	1440	0.5018	0.8692
0.1031	9.0	1620	0.5188	0.8731
0.081	10.0	1800	0.5231	0.8711
0.0671	11.0	1980	0.5407	0.8716
0.0569	12.0	2160	0.5309	0.8721
0.0466	13.0	2340	0.5463	0.8711
0.0414	14.0	2520	0.5434	0.8746
0.039	15.0	2700	0.5464	0.8721

Framework versions

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

gokuls/bert_uncased_L-2_H-768_A-12_massive

bert_uncased_L-2_H-768_A-12_massive

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gokuls/
bert_uncased_L-2_H-768_A-12_massive