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peft-adapter-jul – AI Model by fgiauna | AlphaNeural AI
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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
