bert-ner-it

• Loss: 0.1663
• Loc: {'precision': 0.902371288186285, 'recall': 0.9182608695652174, 'f1': 0.9102467406529469, 'number': 4600}
• Org: {'precision': 0.8682170542635659, 'recall': 0.8707482993197279, 'f1': 0.8694808345463367, 'number': 4116}
• Per: {'precision': 0.9575375863470134, 'recall': 0.9596823457544288, 'f1': 0.9586087663988611, 'number': 4911}
• Overall Precision: 0.9119
• Overall Recall: 0.9188
• Overall F1: 0.9153
• Overall Accuracy: 0.9666

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 3e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Framework versions

• Transformers 4.57.1
• Pytorch 2.6.0+cu124
• Datasets 4.4.1
• Tokenizers 0.22.1