sparknlp.annotator.embeddings.universal_sentence_encoder#

Contains classes for the UniversalSentenceEncoder.

Module Contents#

Classes#

UniversalSentenceEncoder

The Universal Sentence Encoder encodes text into high dimensional vectors

class UniversalSentenceEncoder(classname='com.johnsnowlabs.nlp.embeddings.UniversalSentenceEncoder', java_model=None)[source]#

The Universal Sentence Encoder encodes text into high dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks.

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

>>> useEmbeddings = UniversalSentenceEncoder.pretrained() \
...     .setInputCols(["sentence"]) \
...     .setOutputCol("sentence_embeddings")

The default model is "tfhub_use", if no name is provided. For available pretrained models please see the Models Hub.

For extended examples of usage, see the Spark NLP Workshop.

Input Annotation types

Output Annotation type

DOCUMENT

SENTENCE_EMBEDDINGS

Parameters:
dimension

Number of embedding dimensions

loadSP

Whether to load SentencePiece ops file which is required only by multi-lingual models, by default False

configProtoBytes

ConfigProto from tensorflow, serialized into byte array.

References

Universal Sentence Encoder

https://tfhub.dev/google/universal-sentence-encoder/2

Paper abstract:

We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are efficient and result in accurate performance on diverse transfer tasks. Two variants of the encoding models allow for trade-offs between accuracy and compute resources. For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance. Comparisons are made with baselines that use word level transfer learning via pretrained word embeddings as well as baselines do not use any transfer learning. We find that transfer learning using sentence embeddings tends to outperform word level transfer. With transfer learning via sentence embeddings, we observe surprisingly good performance with minimal amounts of supervised training data for a transfer task. We obtain encouraging results on Word Embedding Association Tests (WEAT) targeted at detecting model bias. Our pre-trained sentence encoding models are made freely available for download and on TF Hub.

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 = UniversalSentenceEncoder.pretrained() \
...     .setInputCols(["sentence"]) \
...     .setOutputCol("sentence_embeddings")
>>> embeddingsFinisher = EmbeddingsFinisher() \
...     .setInputCols(["sentence_embeddings"]) \
...     .setOutputCols("finished_embeddings") \
...     .setOutputAsVector(True) \
...     .setCleanAnnotations(False)
>>> pipeline = Pipeline() \
...     .setStages([
...       documentAssembler,
...       sentence,
...       embeddings,
...       embeddingsFinisher
...     ])
>>> data = spark.createDataFrame([["This is a sentence."]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+--------------------------------------------------------------------------------+
|                                                                          result|
+--------------------------------------------------------------------------------+
|[0.04616805538535118,0.022307956591248512,-0.044395286589860916,-0.0016493503...|
+--------------------------------------------------------------------------------+
name = UniversalSentenceEncoder[source]#
loadSP[source]#
configProtoBytes[source]#
setLoadSP(self, value)[source]#

Sets whether to load SentencePiece ops file which is required only by multi-lingual models, by default False.

Parameters:
valuebool

Whether to load SentencePiece ops file which is required only by multi-lingual models

setConfigProtoBytes(self, b)[source]#

Sets configProto from tensorflow, serialized into byte array.

Parameters:
bList[int]

ConfigProto from tensorflow, serialized into byte array

static loadSavedModel(folder, spark_session, loadsp=False)[source]#

Loads a locally saved model.

Parameters:
folderstr

Folder of the saved model

spark_sessionpyspark.sql.SparkSession

The current SparkSession

Returns:
UniversalSentenceEncoder

The restored model

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

Downloads and loads a pretrained model.

Parameters:
namestr, optional

Name of the pretrained model, by default “tfhub_use”

langstr, optional

Language of the pretrained model, by default “en”

remote_locstr, optional

Optional remote address of the resource, by default None. Will use Spark NLPs repositories otherwise.

Returns:
UniversalSentenceEncoder

The restored model