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