finetuned-customer-intent-distilbert

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

• Loss: 1.2456
• Accuracy: 0.8247
• F1: 0.8247

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	F1
No log	1.0	25	3.1005	0.2835	0.2666
No log	2.0	50	2.5885	0.6598	0.6428
No log	3.0	75	2.0839	0.6959	0.6772
No log	4.0	100	1.6845	0.7371	0.7289
No log	5.0	125	1.4019	0.7835	0.7799
No log	6.0	150	1.2387	0.8093	0.8090
No log	7.0	175	1.1484	0.8144	0.8143
No log	8.0	200	1.1057	0.8247	0.8247
No log	9.0	225	1.1020	0.8247	0.8247
No log	10.0	250	1.1103	0.8247	0.8247
No log	11.0	275	1.1397	0.8247	0.8247
No log	12.0	300	1.1622	0.8247	0.8247
No log	13.0	325	1.1783	0.8247	0.8247
No log	14.0	350	1.1990	0.8247	0.8247
No log	15.0	375	1.2142	0.8247	0.8247
No log	16.0	400	1.2248	0.8247	0.8247
No log	17.0	425	1.2333	0.8247	0.8247
No log	18.0	450	1.2397	0.8247	0.8247
No log	19.0	475	1.2447	0.8247	0.8247
No log	20.0	500	1.2456	0.8247	0.8247

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1