This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.2137
Accuracy: 0.4310
F1: 0.4257
Precision: 0.4466
Recall: 0.4310

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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
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	F1	Precision	Recall
2.7164	1.0	276	2.6348	0.2840	0.2368	0.4033	0.2840
1.3322	2.0	552	2.0566	0.4183	0.4064	0.4338	0.4183
0.5727	3.0	828	1.9395	0.4029	0.3975	0.4292	0.4029
0.2102	4.0	1104	1.9605	0.4156	0.4115	0.4400	0.4156
0.0697	5.0	1380	2.0963	0.4365	0.4205	0.4438	0.4365
0.0261	6.0	1656	2.2137	0.4310	0.4257	0.4466	0.4310

Framework versions

Transformers 4.39.3
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

sherelyn912/emotional-distilbert-1

emotional-distilbert

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sherelyn912/
emotional-distilbert-1