sparknlp.annotator.date2_chunk#

Classes#

class Date2Chunk[source]#

Converts DATE type Annotations to CHUNK type.

This can be useful if the following annotators after DateMatcher and MultiDateMatcher require `CHUNK` types.

Parameters:

entityName

Entity name for the metadata, by default "DATE".

Examples

>>> from pyspark.ml import Pipeline

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> date = DateMatcher() \
...     .setInputCols(["document"]) \
...     .setOutputCol("date")
>>> date2Chunk = Date2Chunk() \
...     .setInputCols(["date"]) \
...     .setOutputCol("date_chunk")
>>> pipeline = Pipeline().setStages([
...     documentAssembler,
...     date,
...     date2Chunk
... ])
>>> data = spark.createDataFrame([["Omicron is a new variant of COVID-19, which the World Health Organization designated a variant of concern on Nov. 26, 2021/26/11."]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.select("date_chunk").show(1, truncate=False)
   ----------------------------------------------------+
   |date_chunk                                          |
   ----------------------------------------------------+
   |[{chunk, 118, 121, 2021/01/01, {sentence -> 0}, []}]|
   ----------------------------------------------------+

setEntityName(name)[source]#

Sets Learning Rate, by default 0.001.

Parameters:

vfloat

Learning Rate