---
license: mit
language:
- sk
pipeline_tag: token-classification
tags:
- SlovakBERT
---

# SlovakBERT address NER
[SlovakBERT](https://huggingface.co/gerulata/slovakbert) based model for named entity recognition of Slovak addresses.

This work is a joint effort of Slovak National Competence Center for High-Performance Computing and Nettle, s.r.o., a Slovak-based start-up focusing on natural language processing, chatbots and voicebots.

## Model usage
The model recognizes following entities: STREET, HOUSENUMBER, MUNICIPALITY, POSTALNUMBER.
It uses BIO annotation scheme and therefore together with the O label has 9 labels in total.

It is inteded to be used only on SLOVAK addresses.

The primary use is to annotate input from speech-to-text transcriptions, therefore it handles natural speech hesitations (e.g. "Ďalej", "no", "uh").

Both house number and cadastral registration number are labelled as HOUSENUMBER.
Names of parts of a municipalities are also labelled as MUNICIPALITY.

### Preprocessing and input format
The input is preprocessed so that it doesn't contain any commas!
The input can be both lower case and upper case, even contain errors when a proper noun starts lowercase.
The house number can have two parts separated by a slash and it can contain a letter from A to F at the end (e.g. "Mätová ulica 97/25C").
The postal number is always composed of 5 digits, but can be split into two parts by 3 and 2 digits respectively (e.g. "923 12", "84401").
The street can contain shortened parts (e.g. "Ulica J. Matúšku").

### Code example
```
from transformers import pipeline

ner_pipeline = pipeline(task='ner', model='nettle-ai/slovakbert-address-ner')
input_sentence = "Žiškova uhm 21 85510 no Pezinok"
classifications = ner_pipeline(input_sentence)
```

## Acknowledgement
The research results were obtained with the support of the Slovak National competence centre for
HPC, the EuroCC 2 project and Slovak National Supercomputing Centre under grant agreement
101101903-EuroCC 2-DIGITAL-EUROHPC-JU-2022-NCC-01.

## Framework Versions

- Transformers 4.26.0
- PyTorch 1.13.1
- Tokenizers 0.13.2