---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- germeval_14
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased-de-ner
  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: Precision
      type: precision
      value: 0.8109431552054502
    - name: Recall
      type: recall
      value: 0.771990271584921
    - name: F1
      type: f1
      value: 0.7909874364032811
    - name: Accuracy
      type: accuracy
      value: 0.9786213727432309
language:
- de
widget:
- text: Mein Name ist Wolfgang und ich  lebe in Berlin
  example_title: Example 1
- text: Mein Name ist Sarah und ich lebe in London
  example_title: Example 2
- text: Mein Name ist Clara und ich lebe in Berkeley, California.
  example_title: Example 3
---

<!-- 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. -->

# bert-base-uncased-de-ner

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the germeval_14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1374
- Precision: 0.8109
- Recall: 0.7720
- F1: 0.7910
- Accuracy: 0.9786

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

The model was trained on data that follows the [`IOB`](<https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)>) convention. Full tagset with indices:

```python
{'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6,}
```

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.104         | 1.0   | 3000  | 0.0973          | 0.7027    | 0.7323 | 0.7172 | 0.9712   |
| 0.0597        | 2.0   | 6000  | 0.0942          | 0.8135    | 0.7172 | 0.7623 | 0.9766   |
| 0.0345        | 3.0   | 9000  | 0.1051          | 0.7924    | 0.7569 | 0.7742 | 0.9773   |
| 0.0172        | 4.0   | 12000 | 0.1170          | 0.8074    | 0.7628 | 0.7844 | 0.9779   |
| 0.0092        | 5.0   | 15000 | 0.1264          | 0.8068    | 0.7803 | 0.7933 | 0.9788   |
| 0.0035        | 6.0   | 18000 | 0.1374          | 0.8109    | 0.7720 | 0.7910 | 0.9786   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2