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: 0.5342
• Accuracy: 0.885
• Balanced accuracy: 0.8457
• F1: 0.8861

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Balanced accuracy	F1
1.6372	1.0	25	1.4302	0.53	0.2661	0.4247
1.3152	2.0	50	1.1864	0.57	0.2892	0.4648
1.0588	3.0	75	1.0524	0.605	0.3390	0.5390
0.8495	4.0	100	0.8517	0.76	0.5691	0.7315
0.6198	5.0	125	0.6699	0.79	0.6073	0.7671
0.4309	6.0	150	0.5773	0.835	0.7656	0.8382
0.2887	7.0	175	0.5278	0.84	0.7435	0.8391
0.203	8.0	200	0.4942	0.865	0.8268	0.8669
0.1459	9.0	225	0.4451	0.885	0.8189	0.8847
0.1053	10.0	250	0.4940	0.865	0.7809	0.8641
0.0786	11.0	275	0.5234	0.865	0.7746	0.8629
0.0659	12.0	300	0.5266	0.86	0.7944	0.8601
0.0591	13.0	325	0.5427	0.845	0.7628	0.8461
0.0456	14.0	350	0.5309	0.86	0.8072	0.8620
0.0352	15.0	375	0.5377	0.87	0.8119	0.8711
0.032	16.0	400	0.5320	0.87	0.7908	0.8690
0.0274	17.0	425	0.5240	0.87	0.8119	0.8698
0.0247	18.0	450	0.5326	0.88	0.8429	0.8812
0.0231	19.0	475	0.5309	0.88	0.8384	0.8802
0.0227	20.0	500	0.5342	0.885	0.8457	0.8861

Framework versions

• Transformers 4.38.1
• Pytorch 2.1.0+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2