This model is a fine-tuned version of google/bert_uncased_L-2_H-256_A-4 on the massive dataset. It achieves the following results on the evaluation set:

Loss: 0.8268
Accuracy: 0.8062

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
3.6523	1.0	180	3.0957	0.3114
2.7875	2.0	360	2.3220	0.5352
2.1742	3.0	540	1.8439	0.6483
1.7765	4.0	720	1.5345	0.6940
1.4988	5.0	900	1.3275	0.7137
1.3009	6.0	1080	1.1805	0.7368
1.1512	7.0	1260	1.0746	0.7511
1.0374	8.0	1440	0.9977	0.7649
0.9466	9.0	1620	0.9426	0.7757
0.8821	10.0	1800	0.8991	0.7909
0.828	11.0	1980	0.8648	0.7929
0.7824	12.0	2160	0.8426	0.7988
0.7565	13.0	2340	0.8268	0.8062
0.7378	14.0	2520	0.8180	0.8052
0.7231	15.0	2700	0.8142	0.8047

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-256_A-4_massive

bert_uncased_L-2_H-256_A-4_massive

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gokuls/
bert_uncased_L-2_H-256_A-4_massive