This model is a fine-tuned version of google/bert_uncased_L-10_H-256_A-4 on the emotion dataset. It achieves the following results on the evaluation set:

Loss: 0.1663
Accuracy: 0.9375

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.9039	1.0	250	0.4065	0.89
0.3103	2.0	500	0.2536	0.913
0.207	3.0	750	0.1875	0.9355
0.156	4.0	1000	0.1739	0.9335
0.1301	5.0	1250	0.1663	0.928
0.1072	6.0	1500	0.1663	0.9375
0.0958	7.0	1750	0.1669	0.9345
0.0824	8.0	2000	0.1706	0.9345
0.0757	9.0	2250	0.1739	0.9285
0.0694	10.0	2500	0.1735	0.9285

Framework versions

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

gokuls/bert_uncased_L-10_H-256_A-4_emotion

bert_uncased_L-10_H-256_A-4_emotion

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gokuls/
bert_uncased_L-10_H-256_A-4_emotion