pretrained_pipeline.py

# Import Spark NLP            
from sparknlp.base import *
from sparknlp.annotator import *
from sparknlp.embeddings import *
import sparknlp

# Start Spark Session with Spark NLP
spark = sparknlp.start()

# Download a pre-trained pipeline 
pipeline = PretrainedPipeline('explain_document_dl', lang='en')

# Your testing dataset
text = """
The Mona Lisa is a 16th century oil painting created by Leonardo. 
It's held at the Louvre in Paris.
"""

# Annotate your testing dataset
result = pipeline.annotate(text)

# What's in the pipeline
list(result.keys())
Output: ['entities', 'stem', 'checked', 'lemma', 'document',
'pos', 'token', 'ner', 'embeddings', 'sentence']

# Check the results
result['entities']
Output: ['Mona Lisa', 'Leonardo', 'Louvre', 'Paris']