bert-complaint-classifier

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

• Loss: 0.0486
• Accuracy: 0.9926
• F1 Macro: 0.9927
• F1 Weighted: 0.9926
• Precision Macro: 0.9932
• Recall Macro: 0.9921

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 3
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	F1 Weighted	Precision Macro	Recall Macro
1.0494	0.5556	100	0.9475	0.7085	0.6573	0.6771	0.8076	0.6735
0.5058	1.1111	200	0.2953	0.9852	0.9859	0.9853	0.9865	0.9853
0.0956	1.6667	300	0.0682	0.9815	0.9824	0.9815	0.9835	0.9818
0.0065	2.2222	400	0.0484	0.9889	0.9892	0.9889	0.9899	0.9887
0.0023	2.7778	500	0.0486	0.9926	0.9927	0.9926	0.9932	0.9921

Framework versions

• Transformers 4.51.3
• Pytorch 2.7.0+cu126
• Datasets 3.6.0
• Tokenizers 0.21.1