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.
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")
|Compatibility:||Spark NLP 2.1.0+|
|Input Labels:||[sentence, token, embeddings]|
The model is trained based on data from https://de.wikipedia.org