---
license: mit
language:
- en
library_name: spacy
pipeline_tag: token-classification
---

This is a NER-model trained using the spacy library in Python. It recognizes addresses (postal codes, cities, streets and housenumbers) and uses a separate spacy model to find connected entities. It is based on spacy's English transformer pipeline (roberta-base). You can use it as any other spacy model.

This model was created as part of the [Qanary-NER-automl-component's multi result branch](https://github.com/WSE-research/Qanary-NER-automl-component/tree/multi-result#automation-service). To include it in this component, refer to the [corresponding Readme chapter](https://github.com/WSE-research/Qanary-NER-automl-component#option-1). There are images containing this, and other, models already available to download. A list can be found in [the final chapter of the Readme](https://github.com/WSE-research/Qanary-NER-automl-component/tree/multi-result#ready-to-go-docker-images).