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"""Contains classes for BertForSequenceClassification."""
from sparknlp.common import *
[docs]class BertForSequenceClassification(AnnotatorModel,
HasCaseSensitiveProperties,
HasBatchedAnnotate,
HasClassifierActivationProperties,
HasEngine,
HasMaxSentenceLengthLimit):
"""BertForSequenceClassification can load Bert Models with sequence classification/regression head on top
(a linear layer on top of the pooled output) e.g. for multi-class document classification tasks.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> sequenceClassifier = BertForSequenceClassification.pretrained() \\
... .setInputCols(["token", "document"]) \\
... .setOutputCol("label")
The default model is ``"bert_base_sequence_classifier_imdb"``, if no name is
provided.
For available pretrained models please see the `Models Hub
<https://sparknlp.org/models?task=Text+Classification>`__.
To see which models are compatible and how to import them see
`Import Transformers into Spark NLP 🚀
<https://github.com/JohnSnowLabs/spark-nlp/discussions/5669>`_.
====================== ======================
Input Annotation types Output Annotation type
====================== ======================
``DOCUMENT, TOKEN`` ``CATEGORY``
====================== ======================
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
coalesceSentences
Instead of 1 class per sentence (if inputCols is `sentence`) output 1
class per document by averaging probabilities in all sentences, by
default False
activation
Whether to calculate logits via Softmax or Sigmoid, by default
`"softmax"`.
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")
>>> sequenceClassifier = BertForSequenceClassification.pretrained() \\
... .setInputCols(["token", "document"]) \\
... .setOutputCol("label") \\
... .setCaseSensitive(True)
>>> pipeline = Pipeline().setStages([
... documentAssembler,
... tokenizer,
... sequenceClassifier
... ])
>>> data = spark.createDataFrame([["I loved this movie when I was a child.", "It was pretty boring."]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.select("label.result").show(truncate=False)
+------+
|result|
+------+
|[pos] |
|[neg] |
+------+
"""
[docs] name = "BertForSequenceClassification"
[docs] outputAnnotatorType = AnnotatorType.CATEGORY
[docs] configProtoBytes = Param(Params._dummy(),
"configProtoBytes",
"ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()",
TypeConverters.toListInt)
[docs] coalesceSentences = Param(Params._dummy(), "coalesceSentences",
"Instead of 1 class per sentence (if inputCols is '''sentence''') output 1 class per document by averaging probabilities in all sentences.",
TypeConverters.toBoolean)
[docs] def getClasses(self):
"""
Returns labels used to train this model
"""
return self._call_java("getClasses")
[docs] def setConfigProtoBytes(self, b):
"""Sets configProto from tensorflow, serialized into byte array.
Parameters
----------
b : List[int]
ConfigProto from tensorflow, serialized into byte array
"""
return self._set(configProtoBytes=b)
[docs] def setCoalesceSentences(self, value):
"""Instead of 1 class per sentence (if inputCols is '''sentence''') output 1 class per document by averaging probabilities in all sentences.
Due to max sequence length limit in almost all transformer models such as BERT (512 tokens), this parameter helps feeding all the sentences
into the model and averaging all the probabilities for the entire document instead of probabilities per sentence. (Default: true)
Parameters
----------
value : bool
If the output of all sentences will be averaged to one output
"""
return self._set(coalesceSentences=value)
@keyword_only
def __init__(self, classname="com.johnsnowlabs.nlp.annotators.classifier.dl.BertForSequenceClassification",
java_model=None):
super(BertForSequenceClassification, self).__init__(
classname=classname,
java_model=java_model
)
self._setDefault(
batchSize=8,
maxSentenceLength=128,
caseSensitive=True,
coalesceSentences=False,
activation="softmax"
)
@staticmethod
[docs] def loadSavedModel(folder, spark_session):
"""Loads a locally saved model.
Parameters
----------
folder : str
Folder of the saved model
spark_session : pyspark.sql.SparkSession
The current SparkSession
Returns
-------
BertForSequenceClassification
The restored model
"""
from sparknlp.internal import _BertSequenceClassifierLoader
jModel = _BertSequenceClassifierLoader(folder, spark_session._jsparkSession)._java_obj
return BertForSequenceClassification(java_model=jModel)
@staticmethod
[docs] def pretrained(name="bert_base_sequence_classifier_imdb", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default
"bert_base_sequence_classifier_imdb"
lang : str, optional
Language of the pretrained model, by default "en"
remote_loc : str, optional
Optional remote address of the resource, by default None. Will use
Spark NLPs repositories otherwise.
Returns
-------
BertForSequenceClassification
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(BertForSequenceClassification, name, lang, remote_loc)