peft-adapter-jul

This model is a fine-tuned version of Jean-Baptiste/camembert-ner on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0858
• Loc: {'precision': 0.6808510638297872, 'recall': 0.7407407407407407, 'f1': 0.7095343680709535, 'number': 216}
• Misc: {'precision': 0.5416666666666666, 'recall': 0.325, 'f1': 0.40624999999999994, 'number': 40}
• Org: {'precision': 0.75, 'recall': 0.81, 'f1': 0.7788461538461539, 'number': 200}
• Per: {'precision': 0.7989130434782609, 'recall': 0.75, 'f1': 0.7736842105263159, 'number': 196}
• Overall Precision: 0.7314
• Overall Recall: 0.7393
• Overall F1: 0.7353
• Overall Accuracy: 0.9799

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Framework versions

• Transformers 4.26.1
• Pytorch 2.0.0+cu118
• Datasets 2.12.0
• Tokenizers 0.13.3