Detect Persons, Locations, Organizations and Misc Entities - DE (Wiki NER 6B 100)

Description

Wiki NER is a Named Entity Recognition (or NER) model, that can be used to find features such as names of people, places, and organizations. This NER model does not read words directly but instead reads word embeddings, which represent words as points such that more semantically similar words are closer together. Wiki NER 6B 100 is trained with GloVe 6B 100 word embeddings, so be sure to use the same embeddings in the pipeline.

Predicted Entities

Persons, Locations, Organizations, Misc.

Live Demo Open in Colab Download Copy S3 URI

How to use

ner = NerDLModel.pretrained("wikiner_6B_100", "de") \
        .setInputCols(["document", "token", "embeddings"]) \
        .setOutputCol("ner")

val ner = NerDLModel.pretrained("wikiner_6B_100", "de")
        .setInputCols(Array("document", "token", "embeddings"))
        .setOutputCol("ner")

Model Information

Model Name:	wikiner_6B_100
Type:	ner
Compatibility:	Spark NLP 2.1.0+
License:	Open Source
Edition:	Official
Input Labels:	[sentence, token, embeddings]
Output Labels:	[ner]
Language:	de
Case sensitive:	false

Data Source

The model is trained based on data from https://de.wikipedia.org

NEXTGlove Embeddings 6B 100