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---
language:
- de
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- germeval_14
metrics:
- precision
- recall
- f1
- accuracy
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
base_model: bert-base-uncased
model-index:
- name: bert-base-uncased-de-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: germeval_14
type: germeval_14
config: germeval_14
split: test
args: germeval_14
metrics:
- type: precision
value: 0.8109431552054502
name: Precision
- type: recall
value: 0.771990271584921
name: Recall
- type: f1
value: 0.7909874364032811
name: F1
- type: accuracy
value: 0.9786213727432309
name: Accuracy
---
<!-- 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 |