---
library_name: flair
tags:
- flair
- token-classification
- sequence-tagger-model
language: de
license: other
license_name: flukes-nc-1.0
license_link: https://github.com/flairNLP/flukes/blob/main/MODEL_LICENSE.md
widget:
- text: "Die Deutsche Nationalmannschaft, trainiert von Helmut Schön, gewann in München bei der Weltmeisterschaft 1974 das Endspiel gegen die Niederlande."
---


## German NER in Flair (8-class)

This is the 8-class NER model for German for [Flair](https://github.com/flairNLP/flair/).

F1-Score: **95.6**

Predicts 8 tags:

| **tag**                        | **meaning** |
|---------------------------------|-----------|
| PER         | person name |
| LOC         | location name |
| ORG         | organization name |
| PRODUCT         | product name ("Oura Ring 5")|
| WORK_OF_ART         | film/song/album/book name |
| EVENT         | named event ("1970 World Cup") |
| IDENT         | nationality, ethnicity or adherence |
| MISC         | other name |

> ⚠️ **Default license: noncommercial use only.** This model is released under the
> [Flukes NC 1.0 License](https://github.com/flairNLP/flukes/blob/main/MODEL_LICENSE.md).
> Commercial use — including using this model's predictions in a commercial
> product or service — requires a separate license. Contact
> `alan.akbik@gmail.com`.
> 

---

### Demo: How to use in Flair

Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)

```python
from flair.data import Sentence
from flair.models import SequenceTagger

# load tagger
tagger = SequenceTagger.load("flair/entity-german-4class")

# make example sentence
sentence = Sentence("Die Deutsche Nationalmannschaft, trainiert von Helmut Schön, gewann in München bei der Weltmeisterschaft 1974 das Endspiel gegen die Niederlande.")

# predict NER tags
tagger.predict(sentence)

# print sentence
print(sentence)

# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
    print(entity)
```

This yields the following output:
```
Span[1:3]: "Deutsche Nationalmannschaft" → ORG (1.0000)
Span[6:8]: "Helmut Schön" → PER (1.0000)
Span[11:12]: "München" → LOC (1.0000)
Span[14:16]: "Weltmeisterschaft 1974" → EVENT (1.0000)
Span[20:21]: "Niederlande" → ORG (1.0000)
```

So, the entities "*Deutsche Nationalmannschaft*" and "*Niederlande*" are tagged as **organization** (soccer teams), "*Helmut Schön*" is tagged as a **person**, 
"*Mexico*" as a **location** and  "*, together with
the entity "*Weltmeisterschaft 1974*", labeled as **event**.

---

### Cite

Please cite the following paper when using this model.

```
@inproceedings{akbik2019flair,
    title={{FLAIR}: An easy-to-use framework for state-of-the-art {NLP}},
    author={Akbik, Alan and Bergmann, Tanja and Blythe, Duncan and Rasul, Kashif and Schweter, Stefan and Vollgraf, Roland},
    booktitle={{NAACL} 2019, 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)},
    pages={54--59},
    year={2019}
}
```

---

## License

**Model weights**: [Flukes Noncommercial License 1.0](https://github.com/flairNLP/flukes/blob/main/MODEL_LICENSE.md).
Personal, academic, and other noncommercial use permitted. Commercial use
requires a separate license — contact `alan.akbik@gmail.com`.