distilbert-base-uncased-finetuned-emotion

This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set:

• Loss: 0.1538
• Accuracy: 0.936
• F1: 0.9361

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: 256
• eval_batch_size: 256
• seed: 42
• 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	F1
No log	1.0	63	0.8113	0.745	0.6916
No log	2.0	126	0.2909	0.911	0.9107
No log	3.0	189	0.2003	0.928	0.9277
0.5629	4.0	252	0.1698	0.938	0.9376
0.5629	5.0	315	0.1561	0.9365	0.9364
0.5629	6.0	378	0.1531	0.933	0.9334
0.5629	7.0	441	0.1584	0.9355	0.9345
0.1065	8.0	504	0.1493	0.9325	0.9321
0.1065	9.0	567	0.1504	0.936	0.9364
0.1065	10.0	630	0.1481	0.9395	0.9395
0.1065	11.0	693	0.1501	0.935	0.9353
0.0684	12.0	756	0.1504	0.936	0.9360
0.0684	13.0	819	0.1526	0.935	0.9352
0.0684	14.0	882	0.1526	0.9355	0.9357
0.0684	15.0	945	0.1538	0.936	0.9361

Framework versions

• Transformers 4.37.2
• Pytorch 2.1.0+cu121
• Datasets 2.14.6
• Tokenizers 0.15.2