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"""Contains classes for AlbertForZeroShotClassification."""
from sparknlp.common import *
[docs]class AlbertForZeroShotClassification(AnnotatorModel,
HasCaseSensitiveProperties,
HasBatchedAnnotate,
HasClassifierActivationProperties,
HasCandidateLabelsProperties,
HasEngine,
HasMaxSentenceLengthLimit):
"""AlbertForZeroShotClassification using a `ModelForSequenceClassification` trained on NLI (natural language
inference) tasks. Equivalent of `DistilBertForSequenceClassification` models, but these models don't require a hardcoded
number of potential classes, they can be chosen at runtime. It usually means it's slower but it is much more
flexible.
Note that the model will loop through all provided labels. So the more labels you have, the
longer this process will take.
Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis
pair and passed to the pretrained model.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> sequenceClassifier = AlbertForZeroShotClassification.pretrained() \\
... .setInputCols(["token", "document"]) \\
... .setOutputCol("label")
The default model is ``"albert_base_zero_shot_classifier_onnx"``, if no name is
provided.
For available pretrained models please see the `Models Hub
<https://sparknlp.orgtask=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 = AlbertForZeroShotClassification.pretrained() \\
... .setInputCols(["token", "document"]) \\
... .setOutputCol("label") \\
... .setCaseSensitive(True)
>>> pipeline = Pipeline().setStages([
... documentAssembler,
... tokenizer,
... sequenceClassifier
... ])
>>> data = spark.createDataFrame([["I have a problem with my iphone that needs to be resolved asap!!"]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.select("label.result").show(truncate=False)
+---------+
|result |
+---------+
|[urgent] |
+---------+
"""
name = "AlbertForZeroShotClassification"
inputAnnotatorTypes = [AnnotatorType.DOCUMENT, AnnotatorType.TOKEN]
outputAnnotatorType = AnnotatorType.CATEGORY
configProtoBytes = Param(Params._dummy(),
"configProtoBytes",
"ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()",
TypeConverters.toListInt)
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 Bart
(512 tokens), this parameter helps to feed 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.AlbertForZeroShotClassification",
java_model=None):
super(AlbertForZeroShotClassification, 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
-------
AlbertForZeroShotClassification
The restored model
"""
from sparknlp.internal import _AlbertForZeroShotClassificationLoader
jModel = _AlbertForZeroShotClassificationLoader(folder, spark_session._jsparkSession)._java_obj
return AlbertForZeroShotClassification(java_model=jModel)
@staticmethod
[docs] def pretrained(name="albert_zero_shot_classifier_onnx", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default
"albert_zero_shot_classifier_onnx"
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
-------
BartForZeroShotClassification
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(AlbertForZeroShotClassification, name, lang, remote_loc)