Recognize Entities OntoNotes - ELECTRA Base

Description

A pre-trained pipeline containing NerDl Model. The NER model trained on OntoNotes 5.0 with electra_base_uncased embeddings. It can extract up to following 18 entities:

Predicted Entities

CARDINAL, DATE, EVENT, FAC, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK_OF_ART.

Live Demo Open in Colab Download Copy S3 URI

How to use

from sparknlp.pretrained import PretrainedPipeline

pipeline = PretrainedPipeline('onto_recognize_entities_electra_base')

result = pipeline.annotate("Johnson first entered politics when elected in 2001 as a member of Parliament. He then served eight years as the mayor of London, from 2008 to 2016, before rejoining Parliament.")

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val pipeline = new PretrainedPipeline("onto_recognize_entities_electra_base")

val result = pipeline.annotate("Johnson first entered politics when elected in 2001 as a member of Parliament. He then served eight years as the mayor of London, from 2008 to 2016, before rejoining Parliament.")

import nlu

text = ["""Johnson first entered politics when elected in 2001 as a member of Parliament. He then served eight years as the mayor of London, from 2008 to 2016, before rejoining Parliament."""]
ner_df = nlu.load('en.ner.onto.electra.base').predict(text, output_level='chunk')
ner_df[["entities", "entities_class"]]

Results

+------------+---------+
|chunk       |ner_label|
+------------+---------+
|Johnson     |PERSON   |
|first       |ORDINAL  |
|2001        |DATE     |
|Parliament  |ORG      |
|eight years |DATE     |
|London      |GPE      |
|2008 to 2016|DATE     |
|Parliament  |ORG      |
+------------+---------+

Model Information

Included Models

• DocumentAssembler
• SentenceDetectorDLModel
• Tokenizer
• BertEmbeddings
• NerDLModel
• NerConverter