roberta-large-lora-multi-class-classification

This model is a fine-tuned version of roberta-large on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.2042
• Micro f1: 0.7842
• Macro f1: 0.5733

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: 0.0001
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: constant
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Micro f1	Macro f1
0.4408	0.9995	1048	0.2138	0.7841	0.5624
0.448	2.0	2097	0.2127	0.7943	0.5788
0.446	2.9986	3144	0.2042	0.7842	0.5733

Framework versions

• PEFT 0.13.2
• Transformers 4.46.0
• Pytorch 2.1.0+cu118
• Datasets 3.0.2
• Tokenizers 0.20.1