Onto 300

Description

Onto is a Named Entity Recognition (or NER) model, meaning it annotates text to find features like the names of people, places, and organizations. Onto was trained on the OntoNotes text corpus. 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. Onto 300 is trained with GloVe 840B 300 word embeddings, so be sure to use the same embeddings in the pipeline.

Live Demo Open in Colab Download

How to use


ner = NerDLModel.pretrained("onto_300", "en") \
        .setInputCols(["document", "token", "embeddings"]) \
        .setOutputCol("ner")

val ner = NerDLModel.pretrained("onto_300", "en")
        .setInputCols(Array("document", "token", "embeddings"))
        .setOutputCol("ner")

Model Information

Model Name: onto_300
Type: ner
Compatibility: Spark NLP 2.4.0+
License: Open Source
Edition: Official
Input Labels: [sentence, token, embeddings]
Output Labels: [ner]
Language: en
Case sensitive: false

Data Source

The model is trained based on data fromhttps://catalog.ldc.upenn.edu/LDC2013T19