brand_ner_model

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

• Loss: 0.0132
• Brand: {'precision': 0.9935705381940648, 'recall': 0.9933967421012354, 'f1': 0.99348363254685, 'number': 45735}
• Overall Precision: 0.9936
• Overall Recall: 0.9934
• Overall F1: 0.9935
• Overall Accuracy: 0.9974

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: 2e-05
• train_batch_size: 32
• eval_batch_size: 32
• 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: 4
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.52.4
• Pytorch 2.7.0+cu118
• Datasets 3.6.0
• Tokenizers 0.21.1