nhankins/en_distilbert_lora_adapter_2.0

distilbert-base-multilingual-cased-lora-text-classification

This model is a fine-tuned version of distilbert/distilbert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5714
• Precision: 0.7417
• Recall: 1.0
• F1 and accuracy: {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847}

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: 0.001
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1 and accuracy
No log	1.0	391	0.5780	0.7417	1.0	{'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847}
0.647	2.0	782	0.5748	0.7417	1.0	{'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847}
0.6216	3.0	1173	0.5713	0.7417	1.0	{'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847}
0.6201	4.0	1564	0.5726	0.7417	1.0	{'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847}
0.6201	5.0	1955	0.5765	0.7417	1.0	{'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847}
0.6199	6.0	2346	0.5756	0.7417	1.0	{'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847}
0.6365	7.0	2737	0.5827	0.7417	1.0	{'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847}
0.6165	8.0	3128	0.5715	0.7417	1.0	{'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847}
0.6185	9.0	3519	0.5715	0.7417	1.0	{'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847}
0.6185	10.0	3910	0.5714	0.7417	1.0	{'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847}

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.17.0
• Tokenizers 0.15.1