
This is a NER-model trained using the spacy library in Python. It recognizes first names, second names and last names and uses a separate spacy model to find connected entities. It is based on spacy's German transformer pipeline (bert-base-german-cased). You can use it as any other spacy-generated transformer 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).
{"language": ["de"], "license": "mit", "library_name": "spacy", "pipeline_tag": "token-classification"}
spacy token-classification de license:mit region:us
TAGS
#spacy #token-classification #de #license-mit #region-us 


This is a NER-model trained using the spacy library in Python. It recognizes first names, second names and last names and uses a separate spacy model to find connected entities. It is based on spacy's German transformer pipeline (bert-base-german-cased). You can use it as any other spacy-generated transformer model.

This model was created as part of the Qanary-NER-automl-component's multi result branch. To include it in this component, refer to the corresponding Readme chapter. There are images containing this, and other, models already available to download. A list can be found in the final chapter of the Readme.
TAGS
#spacy #token-classification #de #license-mit #region-us 

TAGS
#spacy #token-classification #de #license-mit #region-us 
