# 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Contains classes for MxbaiEmbeddings."""
from sparknlp.common import *
[docs]class MxbaiEmbeddings(AnnotatorModel,
HasEmbeddingsProperties,
HasCaseSensitiveProperties,
HasStorageRef,
HasBatchedAnnotate,
HasMaxSentenceLengthLimit):
"""Sentence embeddings using Mxbai Embeddings.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> embeddings = MxbaiEmbeddings.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("Mxbai_embeddings")
The default model is ``"mxbai_large_v1"``, if no name is provided.
For available pretrained models please see the
`Models Hub <https://sparknlp.org/models?q=Mxbai>`__.
====================== ======================
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.
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("document")
>>> embeddings = MxbaiEmbeddings.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 = "MxbaiEmbeddings"
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.MxbaiEmbeddings", java_model=None):
super(MxbaiEmbeddings, 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
-------
MxbaiEmbeddings
The restored model
"""
from sparknlp.internal import _MxbaiEmbeddingsLoader
jModel = _MxbaiEmbeddingsLoader(folder, spark_session._jsparkSession)._java_obj
return MxbaiEmbeddings(java_model=jModel)
@staticmethod
[docs] def pretrained(name="mxbai_large_v1", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "mxbai_large_v1"
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
-------
MxbaiEmbeddings
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(MxbaiEmbeddings, name, lang, remote_loc)