The model has trained twice. In the first run hyperparameters was the same except num_epochs was 3. So results below actually shows 8 epoch of fine-tuning in total.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.1508	1.0	250	0.1969	0.934	0.9334
0.1035	2.0	500	0.1660	0.9335	0.9341
0.0926	3.0	750	0.1626	0.935	0.9359
0.0855	4.0	1000	0.1680	0.934	0.9337
0.0682	5.0	1250	0.1669	0.94	0.9404

Framework versions

Transformers 4.13.0
Pytorch 1.13.1+cu116
Datasets 2.8.0
Tokenizers 0.10.3

Kosee/roberta-base-finetuned-emotion

roberta-base-finetuned-emotion

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Kosee/
roberta-base-finetuned-emotion