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

Loss: 0.4810
Accuracy: 0.9175

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: 16
eval_batch_size: 16
seed: 33
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
3.8247	1.0	1000	2.5170	0.7745
1.9864	2.0	2000	1.3436	0.874
1.1126	3.0	3000	0.8299	0.894
0.6924	4.0	4000	0.6500	0.9025
0.5272	5.0	5000	0.6097	0.908
0.4298	6.0	6000	0.5913	0.904
0.3936	7.0	7000	0.5165	0.9135
0.3238	8.0	8000	0.5120	0.9075
0.3018	9.0	9000	0.4989	0.916
0.2605	10.0	10000	0.4810	0.9175
0.2512	11.0	11000	0.4757	0.9135
0.219	12.0	12000	0.4676	0.914
0.2046	13.0	13000	0.4794	0.911

Framework versions

Transformers 4.22.1
Pytorch 1.12.1+cu113
Datasets 2.5.1
Tokenizers 0.12.1

gokuls/bert-tiny-emotion-KD-BERT

bert-tiny-emotion-KD-BERT

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gokuls/
bert-tiny-emotion-KD-BERT