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---
library_name: transformers
license: mit
base_model: bert-base-german-cased
tags:
- generated_from_trainer
model-index:
- name: brand_ner_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# brand_ner_model
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/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
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