voiceassistant-intent-bert-lora

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

• Loss: 0.4182
• Accuracy: 0.9081
• F1 Macro: 0.9130

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: 0.0003
• train_batch_size: 32
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro
No log	1.0	477	2.4694	0.4748	0.4208
3.7133	2.0	954	1.3183	0.7181	0.7047
1.7805	3.0	1431	0.8659	0.8052	0.8030
1.0098	4.0	1908	0.6768	0.8461	0.8478
0.6774	5.0	2385	0.5619	0.8726	0.8758
0.5035	6.0	2862	0.4999	0.8881	0.8908
0.3952	7.0	3339	0.4620	0.8955	0.9007
0.3162	8.0	3816	0.4396	0.9006	0.9056
0.2796	9.0	4293	0.4250	0.9087	0.9134
0.261	10.0	4770	0.4182	0.9081	0.9130

Framework versions

• PEFT 0.12.0
• Transformers 4.44.2
• Pytorch 2.13.0+cu126
• Datasets 2.21.0
• Tokenizers 0.19.1