bert-base-uncased-finetuned-emotion

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

• Loss: 0.3648
• Accuracy: 0.9405
• F1: 0.9404

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: 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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.0177	1.0	250	0.3372	0.933	0.9331
0.0149	2.0	500	0.3434	0.9385	0.9386
0.012	3.0	750	0.3878	0.9355	0.9353
0.0135	4.0	1000	0.3981	0.938	0.9371
0.0088	5.0	1250	0.3695	0.94	0.9400
0.0112	6.0	1500	0.4133	0.933	0.9334
0.0105	7.0	1750	0.3733	0.937	0.9370
0.0117	8.0	2000	0.3625	0.938	0.9381
0.0126	9.0	2250	0.3539	0.9405	0.9405
0.0095	10.0	2500	0.3963	0.9315	0.9318
0.0088	11.0	2750	0.3692	0.9355	0.9353
0.0072	12.0	3000	0.3646	0.9385	0.9385
0.0064	13.0	3250	0.3630	0.9375	0.9373
0.0052	14.0	3500	0.3659	0.9405	0.9403
0.005	15.0	3750	0.3648	0.9405	0.9404

Framework versions

• Transformers 4.29.2
• Pytorch 2.0.1
• Datasets 2.12.0
• Tokenizers 0.13.3