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"""Contains classes for UAEEmbeddings."""
from sparknlp.common import *
[docs]class UAEEmbeddings(AnnotatorModel,
HasEmbeddingsProperties,
HasCaseSensitiveProperties,
HasStorageRef,
HasBatchedAnnotate,
HasMaxSentenceLengthLimit):
"""Sentence embeddings using Universal AnglE Embedding (UAE).
UAE is a novel angle-optimized text embedding model, designed to improve semantic textual
similarity tasks, which are crucial for Large Language Model (LLM) applications. By
introducing angle optimization in a complex space, AnglE effectively mitigates saturation of
the cosine similarity function.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> embeddings = UAEEmbeddings.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("UAE_embeddings")
The default model is ``"uae_large_v1"``, if no name is provided.
For available pretrained models please see the
`Models Hub <https://sparknlp.org/models?q=UAE>`__.
====================== ======================
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
----------
`AnglE-optimized Text Embeddings <https://arxiv.org/abs/2309.12871>`__
`UAE Github Repository <https://github.com/baochi0212/uae-embedding>`__
**Paper abstract**
*High-quality text embedding is pivotal in improving semantic textual similarity (STS) tasks,
which are crucial components in Large Language Model (LLM) applications. However, a common
challenge existing text embedding models face is the problem of vanishing gradients, primarily
due to their reliance on the cosine function in the optimization objective, which has
saturation zones. To address this issue, this paper proposes a novel angle-optimized text
embedding model called AnglE. The core idea of AnglE is to introduce angle optimization in a
complex space. This novel approach effectively mitigates the adverse effects of the saturation
zone in the cosine function, which can impede gradient and hinder optimization processes. To
set up a comprehensive STS evaluation, we experimented on existing short-text STS datasets and
a newly collected long-text STS dataset from GitHub Issues. Furthermore, we examine
domain-specific STS scenarios with limited labeled data and explore how AnglE works with
LLM-annotated data. Extensive experiments were conducted on various tasks including short-text
STS, long-text STS, and domain-specific STS tasks. The results show that AnglE outperforms the
state-of-the-art (SOTA) STS models that ignore the cosine saturation zone. These findings
demonstrate the ability of AnglE to generate high-quality text embeddings and the usefulness
of angle optimization in STS.*
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("document")
>>> embeddings = UAEEmbeddings.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...|
+--------------------------------------------------------------------------------+
"""
[docs] outputAnnotatorType = AnnotatorType.SENTENCE_EMBEDDINGS
[docs] 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.UAEEmbeddings", java_model=None):
super(UAEEmbeddings, 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
-------
UAEEmbeddings
The restored model
"""
from sparknlp.internal import _UAEEmbeddingsLoader
jModel = _UAEEmbeddingsLoader(folder, spark_session._jsparkSession)._java_obj
return UAEEmbeddings(java_model=jModel)
@staticmethod
[docs] def pretrained(name="uae_large_v1", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "UAE_small"
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
-------
UAEEmbeddings
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(UAEEmbeddings, name, lang, remote_loc)