WikiNER is a Named Entity Recognition (or NER) model, meaning it annotates text to find features like the 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. WikiNER 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", "pt") \ .setInputCols(["document", "token", "embeddings"]) \ .setOutputCol("ner")
val ner = NerDLModel.pretrained("wikiner_6B_100", "pt") .setInputCols(Array("document", "token", "embeddings")) .setOutputCol("ner")
|Compatibility:||Spark NLP 2.5.0+|
|Input Labels:||[sentence, token, embeddings]|
The model was trained based on data from https://pt.wikipedia.org