---
library_name: transformers
license: mit
base_model: bert-base-german-cased
tags:
- generated_from_trainer
datasets:
- germeval_14
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ner_model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: germeval_14
      type: germeval_14
      config: germeval_14
      split: test
      args: germeval_14
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9703112769708805
    - name: F1
      type: f1
      value: 0.8348410033576931
    - name: Precision
      type: precision
      value: 0.8311310366525091
    - name: Recall
      type: recall
      value: 0.8385842393460836
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# ner_model

This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the germeval_14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1446
- Accuracy: 0.9703
- F1: 0.8348
- Precision: 0.8311
- Recall: 0.8386

## 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: 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

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1274        | 1.0   | 3000 | 0.1132          | 0.9671   | 0.8144 | 0.8031    | 0.8260 |
| 0.065         | 2.0   | 6000 | 0.1382          | 0.9690   | 0.8301 | 0.8452    | 0.8155 |
| 0.0365        | 3.0   | 9000 | 0.1446          | 0.9703   | 0.8348 | 0.8311    | 0.8386 |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0