nhankins/yo_distilbert_lora_adapter_1.0

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: 0.6038
• Precision: 0.7039
• Recall: 0.9283
• F1 and accuracy: {'accuracy': 0.7017994858611826, 'f1': 0.8006872852233677}

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: 1e-05
• 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	388	0.6457	0.6452	1.0	{'accuracy': 0.6452442159383034, 'f1': 0.784375}
0.6427	2.0	776	0.6410	0.6452	1.0	{'accuracy': 0.6452442159383034, 'f1': 0.784375}
0.6298	3.0	1164	0.6290	0.6486	1.0	{'accuracy': 0.6503856041131105, 'f1': 0.786833855799373}
0.6216	4.0	1552	0.6243	0.6552	0.9841	{'accuracy': 0.6555269922879178, 'f1': 0.7866242038216561}
0.6216	5.0	1940	0.6148	0.6799	0.9562	{'accuracy': 0.6812339331619537, 'f1': 0.7947019867549668}
0.6091	6.0	2328	0.6131	0.6791	0.9442	{'accuracy': 0.6760925449871465, 'f1': 0.79}
0.5892	7.0	2716	0.6102	0.6871	0.9363	{'accuracy': 0.6838046272493573, 'f1': 0.7925801011804384}
0.5818	8.0	3104	0.6051	0.6997	0.9283	{'accuracy': 0.6966580976863753, 'f1': 0.7979452054794521}
0.5818	9.0	3492	0.6061	0.6953	0.9363	{'accuracy': 0.6940874035989717, 'f1': 0.797962648556876}
0.5766	10.0	3880	0.6038	0.7039	0.9283	{'accuracy': 0.7017994858611826, 'f1': 0.8006872852233677}

Framework versions

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