sparknlp.annotator.classifier_dl.bert_for_token_classification#

Contains classes for BertForTokenClassification.

Module Contents#

Classes#

BertForTokenClassification

BertForTokenClassification can load Bert Models with a token

class BertForTokenClassification(classname='com.johnsnowlabs.nlp.annotators.classifier.dl.BertForTokenClassification', java_model=None)[source]#

BertForTokenClassification can load Bert Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.

Pretrained models can be loaded with pretrained() of the companion object:

>>> embeddings = BertForTokenClassification.pretrained() \
...     .setInputCols(["token", "document"]) \
...     .setOutputCol("label")

The default model is "bert_base_token_classifier_conll03", if no name is provided.

For available pretrained models please see the Models Hub.

To see which models are compatible and how to import them see Import Transformers into Spark NLP ๐Ÿš€.

Input Annotation types

Output Annotation type

DOCUMENT, TOKEN

NAMED_ENTITY

Parameters:
batchSize

Batch size. Large values allows faster processing but requires more memory, by default 8

caseSensitive

Whether to ignore case in tokens for embeddings matching, by default True

configProtoBytes

ConfigProto from tensorflow, serialized into byte array.

maxSentenceLength

Max sentence length to process, by default 128

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> tokenizer = Tokenizer() \
...     .setInputCols(["document"]) \
...     .setOutputCol("token")
>>> tokenClassifier = BertForTokenClassification.pretrained() \
...     .setInputCols(["token", "document"]) \
...     .setOutputCol("label") \
...     .setCaseSensitive(True)
>>> pipeline = Pipeline().setStages([
...     documentAssembler,
...     tokenizer,
...     tokenClassifier
... ])
>>> data = spark.createDataFrame([["John Lenon was born in London and lived in Paris. My name is Sarah and I live in London"]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.select("label.result").show(truncate=False)
+------------------------------------------------------------------------------------+
|result
|
+------------------------------------------------------------------------------------+
|[B-PER, I-PER, O, O, O, B-LOC, O, O, O, B-LOC, O, O, O, O, B-PER, O, O, O,
O, B-LOC]|
+------------------------------------------------------------------------------------+
name = BertForTokenClassification[source]#
maxSentenceLength[source]#
configProtoBytes[source]#
getClasses(self)[source]#

Returns labels used to train this model

setConfigProtoBytes(self, b)[source]#

Sets configProto from tensorflow, serialized into byte array.

Parameters:
bList[int]

ConfigProto from tensorflow, serialized into byte array

setMaxSentenceLength(self, value)[source]#

Sets max sentence length to process, by default 128.

Parameters:
valueint

Max sentence length to process

static loadSavedModel(folder, spark_session)[source]#

Loads a locally saved model.

Parameters:
folderstr

Folder of the saved model spark_session : pyspark.sql.SparkSession The current SparkSession

Returns:
BertForTokenClassification

The restored model

static pretrained(name='bert_base_token_classifier_conll03', lang='en', remote_loc=None)[source]#

Downloads and loads a pretrained model.

Parameters:
namestr, optional

Name of the pretrained model, by default โ€œbert_base_token_classifier_conll03โ€ lang : str, optional Language of the pretrained model, by default โ€œenโ€ remote_loc : str, optional Optional remote address of the resource, by default None. Will use Spark NLPs repositories otherwise.

Returns:
BertForTokenClassification

The restored model