# Copyright 2017-2022 John Snow Labs
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""Contains classes for SnowFlakeEmbeddings."""
from sparknlp.common import *
[docs]class SnowFlakeEmbeddings(AnnotatorModel,
HasEmbeddingsProperties,
HasCaseSensitiveProperties,
HasStorageRef,
HasBatchedAnnotate,
HasMaxSentenceLengthLimit):
"""Sentence embeddings using SnowFlake.
snowflake-arctic-embed is a suite of text embedding models that focuses on creating
high-quality retrieval models optimized for performance.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> embeddings = SnowFlakeEmbeddings.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("SnowFlake_embeddings")
The default model is ``"snowflake_artic_m"``, if no name is provided.
For available pretrained models please see the
`Models Hub <https://sparknlp.org/models?q=SnowFlake>`__.
====================== ======================
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 768
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
----------
`Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models <https://arxiv.org/abs/2405.05374>`__
`Snowflake Arctic-Embed Models <https://github.com/Snowflake-Labs/arctic-embed>`__
**Paper abstract**
*The models are trained by leveraging existing open-source text representation models, such
as bert-base-uncased, and are trained in a multi-stage pipeline to optimize their retrieval
performance. First, the models are trained with large batches of query-document pairs where
negatives are derived in-batch—pretraining leverages about 400m samples of a mix of public
datasets and proprietary web search data. Following pretraining models are further optimized
with long training on a smaller dataset (about 1m samples) of triplets of query, positive
document, and negative document derived from hard harmful mining. Mining of the negatives and
data curation is crucial to retrieval accuracy. A detailed technical report will be available
shortly. *
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("document")
>>> embeddings = SnowFlakeEmbeddings.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("embeddings")
>>> embeddingsFinisher = EmbeddingsFinisher() \\
... .setInputCols("embeddings") \\
... .setOutputCols("finished_embeddings") \\
... .setOutputAsVector(True)
>>> pipeline = Pipeline().setStages([
... documentAssembler,
... embeddings,
... embeddingsFinisher
... ])
>>> data = spark.createDataFrame([["hello world", "hello moon"]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+--------------------------------------------------------------------------------+
| result|
+--------------------------------------------------------------------------------+
|[0.50387806, 0.5861606, 0.35129607, -0.76046336, -0.32446072, -0.117674336, 0...|
|[0.6660665, 0.961762, 0.24854276, -0.1018044, -0.6569202, 0.027635604, 0.1915...|
+--------------------------------------------------------------------------------+
"""
name = "SnowFlakeEmbeddings"
inputAnnotatorTypes = [AnnotatorType.DOCUMENT]
outputAnnotatorType = AnnotatorType.SENTENCE_EMBEDDINGS
poolingStrategy = Param(Params._dummy(),
"poolingStrategy",
"Pooling strategy to use for sentence embeddings",
TypeConverters.toString)
[docs] def setPoolingStrategy(self, value):
"""Pooling strategy to use for sentence embeddings.
Available pooling strategies for sentence embeddings are:
- `"cls"`: leading `[CLS]` token
- `"cls_avg"`: leading `[CLS]` token + mean of all other tokens
- `"last"`: embeddings of the last token in the sequence
- `"avg"`: mean of all tokens
- `"max"`: max of all embedding features of the entire token sequence
- `"int"`: An integer number, which represents the index of the token to use as the
embedding
Parameters
----------
value : str
Pooling strategy to use for sentence embeddings
"""
valid_strategies = {"cls", "cls_avg", "last", "avg", "max"}
if value in valid_strategies or value.isdigit():
return self._set(poolingStrategy=value)
else:
raise ValueError(f"Invalid pooling strategy: {value}. "
f"Valid strategies are: {', '.join(self.valid_strategies)} or an integer.")
@keyword_only
def __init__(self, classname="com.johnsnowlabs.nlp.embeddings.SnowFlakeEmbeddings", java_model=None):
super(SnowFlakeEmbeddings, self).__init__(
classname=classname,
java_model=java_model
)
self._setDefault(
dimension=1024,
batchSize=8,
maxSentenceLength=512,
caseSensitive=False,
poolingStrategy="cls"
)
@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
-------
SnowFlakeEmbeddings
The restored model
"""
from sparknlp.internal import _SnowFlakeEmbeddingsLoader
jModel = _SnowFlakeEmbeddingsLoader(folder, spark_session._jsparkSession)._java_obj
return SnowFlakeEmbeddings(java_model=jModel)
@staticmethod
[docs] def pretrained(name="snowflake_artic_m", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "snowflake_artic_m"
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
-------
SnowFlakeEmbeddings
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(SnowFlakeEmbeddings, name, lang, remote_loc)