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"""Contains classes for MiniLMEmbeddings."""
from sparknlp.common import *
[docs]class MiniLMEmbeddings(AnnotatorModel,
HasEmbeddingsProperties,
HasCaseSensitiveProperties,
HasStorageRef,
HasBatchedAnnotate,
HasMaxSentenceLengthLimit):
"""Sentence embeddings using MiniLM.
MiniLM, a lightweight and efficient sentence embedding model that can generate text embeddings for various NLP tasks (e.g., classification, retrieval, clustering, text evaluation, etc.)
Note that this annotator is only supported for Spark Versions 3.4 and up.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> embeddings = MiniLMEmbeddings.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("minilm_embeddings")
The default model is ``"minilm_l6_v2"``, if no name is provided.
For available pretrained models please see the
`Models Hub <https://sparknlp.org/models?q=MiniLM>`__.
====================== ======================
Input Annotation types Output Annotation type
====================== ======================
``DOCUMENT`` ``SENTENCE_EMBEDDINGS``
====================== ======================
Parameters
----------
batchSize
Size of every batch , by default 8
dimension
Number of embedding dimensions, by default 384
caseSensitive
Whether to ignore case in tokens for embeddings matching, by default False
maxSentenceLength
Max sentence length to process, by default 512
configProtoBytes
ConfigProto from tensorflow, serialized into byte array.
References
----------
`MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers <https://arxiv.org/abs/2002.10957>`__
`MiniLM Github Repository <https://github.com/microsoft/unilm/tree/master/minilm>`__
**Paper abstract**
*We present a simple and effective approach to compress large pre-trained Transformer models
by distilling the self-attention module of the last Transformer layer. The compressed model
(called MiniLM) can be trained with task-agnostic distillation and then fine-tuned on various
downstream tasks. We evaluate MiniLM on the GLUE benchmark and show that it achieves comparable
results with BERT-base while being 4.3x smaller and 5.5x faster. We also show that MiniLM can
be further compressed to 22x smaller and 12x faster than BERT-base while maintaining comparable
performance.*
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("document")
>>> embeddings = MiniLMEmbeddings.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("minilm_embeddings")
>>> embeddingsFinisher = EmbeddingsFinisher() \\
... .setInputCols(["minilm_embeddings"]) \\
... .setOutputCols("finished_embeddings") \\
... .setOutputAsVector(True)
>>> pipeline = Pipeline().setStages([
... documentAssembler,
... embeddings,
... embeddingsFinisher
... ])
>>> data = spark.createDataFrame([["This is a sample sentence for embedding generation.",
... "Another example sentence to demonstrate MiniLM embeddings.",
... ]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+--------------------------------------------------------------------------------+
| result|
+--------------------------------------------------------------------------------+
|[[0.1234567, -0.2345678, 0.3456789, -0.4567890, 0.5678901, -0.6789012...|
|[[0.2345678, -0.3456789, 0.4567890, -0.5678901, 0.6789012, -0.7890123...|
+--------------------------------------------------------------------------------+
"""
[docs] name = "MiniLMEmbeddings"
[docs] outputAnnotatorType = AnnotatorType.SENTENCE_EMBEDDINGS
[docs] 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)
@keyword_only
def __init__(self, classname="com.johnsnowlabs.nlp.embeddings.MiniLMEmbeddings", java_model=None):
super(MiniLMEmbeddings, self).__init__(
classname=classname,
java_model=java_model
)
self._setDefault(
dimension=384,
batchSize=8,
maxSentenceLength=512,
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
-------
MiniLMEmbeddings
The restored model
"""
from sparknlp.internal import _MiniLMLoader
jModel = _MiniLMLoader(folder, spark_session._jsparkSession, use_openvino)._java_obj
return MiniLMEmbeddings(java_model=jModel)
@staticmethod
[docs] def pretrained(name="minilm_l6_v2", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "minilm_l6_v2"
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
-------
MiniLMEmbeddings
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(MiniLMEmbeddings, name, lang, remote_loc)