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"""Contains classes for BertSentenceEmbeddings."""
from sparknlp.common import *
[docs]class BertSentenceEmbeddings(AnnotatorModel,
HasEmbeddingsProperties,
HasCaseSensitiveProperties,
HasStorageRef,
HasBatchedAnnotate,
HasEngine,
HasMaxSentenceLengthLimit):
"""Sentence-level embeddings using BERT. BERT (Bidirectional Encoder
Representations from Transformers) provides dense vector representations for
natural language by using a deep, pre-trained neural network with the
Transformer architecture.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>>embeddings = BertSentenceEmbeddings.pretrained() \\
... .setInputCols(["sentence"]) \\
... .setOutputCol("sentence_bert_embeddings")
The default model is ``"sent_small_bert_L2_768"``, if no name is provided.
For available pretrained models please see the
`Models Hub <https://sparknlp.org/models?task=Embeddings>`__.
For extended examples of usage, see the
`Examples <https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20BERT%20Sentence.ipynb>`__.
====================== =======================
Input Annotation types Output Annotation type
====================== =======================
``DOCUMENT`` ``SENTENCE_EMBEDDINGS``
====================== =======================
Parameters
----------
batchSize
Size of every batch, by default 8
caseSensitive
Whether to ignore case in tokens for embeddings matching, by default
False
dimension
Number of embedding dimensions, by default 768
maxSentenceLength
Max sentence length to process, by default 128
isLong
Use Long type instead of Int type for inputs buffer - Some Bert models
require Long instead of Int.
configProtoBytes
ConfigProto from tensorflow, serialized into byte array.
References
----------
`BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`__
https://github.com/google-research/bert
**Paper abstract**
*We introduce a new language representation model called BERT, which stands
for Bidirectional Encoder Representations from Transformers. Unlike recent
language representation models, BERT is designed to pre-train deep
bidirectional representations from unlabeled text by jointly conditioning on
both left and right context in all layers. As a result, the pre-trained BERT
model can be fine-tuned with just one additional output layer to create
state-of-the-art models for a wide range of tasks, such as question
answering and language inference, without substantial task-specific
architecture modifications. BERT is conceptually simple and empirically
powerful. It obtains new state-of-the-art results on eleven natural language
processing tasks, including pushing the GLUE score to 80.5% (7.7% point
absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute
improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point
absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute
improvement).*
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("document")
>>> sentence = SentenceDetector() \\
... .setInputCols(["document"]) \\
... .setOutputCol("sentence")
>>> embeddings = BertSentenceEmbeddings.pretrained("sent_small_bert_L2_128") \\
... .setInputCols(["sentence"]) \\
... .setOutputCol("sentence_bert_embeddings")
>>> embeddingsFinisher = EmbeddingsFinisher() \\
... .setInputCols(["sentence_bert_embeddings"]) \\
... .setOutputCols("finished_embeddings") \\
... .setOutputAsVector(True)
>>> pipeline = Pipeline().setStages([
... documentAssembler,
... sentence,
... embeddings,
... embeddingsFinisher
... ])
>>> data = spark.createDataFrame([["John loves apples. Mary loves oranges. John loves Mary."]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+--------------------------------------------------------------------------------+
| result|
+--------------------------------------------------------------------------------+
|[-0.8951074481010437,0.13753940165042877,0.3108254075050354,-1.65693199634552...|
|[-0.6180210709571838,-0.12179657071828842,-0.191165953874588,-1.4497021436691...|
|[-0.822715163230896,0.7568016648292542,-0.1165061742067337,-1.59048593044281,...|
+--------------------------------------------------------------------------------+
"""
name = "BertSentenceEmbeddings"
inputAnnotatorTypes = [AnnotatorType.DOCUMENT]
outputAnnotatorType = AnnotatorType.SENTENCE_EMBEDDINGS
isLong = Param(Params._dummy(),
"isLong",
"Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int.",
typeConverter=TypeConverters.toBoolean)
configProtoBytes = Param(Params._dummy(),
"configProtoBytes",
"ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()",
TypeConverters.toListInt)
[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 setIsLong(self, value):
"""Sets whether to use Long type instead of Int type for inputs buffer.
Some Bert models require Long instead of Int.
Parameters
----------
value : bool
Whether to use Long type instead of Int type for inputs buffer
"""
return self._set(isLong=value)
@keyword_only
def __init__(self, classname="com.johnsnowlabs.nlp.embeddings.BertSentenceEmbeddings", java_model=None):
super(BertSentenceEmbeddings, self).__init__(
classname=classname,
java_model=java_model
)
self._setDefault(
dimension=768,
batchSize=8,
maxSentenceLength=128,
caseSensitive=False
)
@staticmethod
[docs] def loadSavedModel(folder, spark_session, use_openvino=False):
"""Loads a locally saved model.
Parameters
----------
folder : str
Folder of the saved model
spark_session : pyspark.sql.SparkSession
The current SparkSession
use_openvino: bool
Use OpenVINO backend
Returns
-------
BertSentenceEmbeddings
The restored model
"""
from sparknlp.internal import _BertSentenceLoader
jModel = _BertSentenceLoader(folder, spark_session._jsparkSession, use_openvino)._java_obj
return BertSentenceEmbeddings(java_model=jModel)
@staticmethod
[docs] def pretrained(name="sent_small_bert_L2_768", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "sent_small_bert_L2_768"
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
-------
BertSentenceEmbeddings
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(BertSentenceEmbeddings, name, lang, remote_loc)