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"""Contains classes for E5Embeddings."""
from sparknlp.common import *
[docs]class E5Embeddings(AnnotatorModel,
HasEmbeddingsProperties,
HasCaseSensitiveProperties,
HasStorageRef,
HasBatchedAnnotate,
HasMaxSentenceLengthLimit):
"""Sentence embeddings using E5.
E5, a weakly supervised text embedding model that can generate text embeddings tailored to any task (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 = E5Embeddings.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("e5_embeddings")
The default model is ``"e5_small"``, if no name is provided.
For available pretrained models please see the
`Models Hub <https://sparknlp.org/models?q=E5>`__.
====================== ======================
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
----------
`Text Embeddings by Weakly-Supervised Contrastive Pre-training <https://arxiv.org/pdf/2212.03533>`__
https://github.com/microsoft/unilm/tree/master/e5
**Paper abstract**
*This paper presents E5, a family of state-of-the-art text embeddings that transfer
well to a wide range of tasks. The model is trained in a contrastive manner with
weak supervision signals from our curated large-scale text pair dataset (called
CCPairs). E5 can be readily used as a general-purpose embedding model for any
tasks requiring a single-vector representation of texts such as retrieval, clustering,
and classification, achieving strong performance in both zero-shot and fine-tuned
settings. We conduct extensive evaluations on 56 datasets from the BEIR and
MTEB benchmarks. For zero-shot settings, E5 is the first model that outperforms
the strong BM25 baseline on the BEIR retrieval benchmark without using any
labeled data. When fine-tuned, E5 obtains the best results on the MTEB benchmark,
beating existing embedding models with 40× more parameters.*
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("document")
>>> embeddings = E5Embeddings.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("e5_embeddings")
>>> embeddingsFinisher = EmbeddingsFinisher() \\
... .setInputCols(["e5_embeddings"]) \\
... .setOutputCols("finished_embeddings") \\
... .setOutputAsVector(True)
>>> pipeline = Pipeline().setStages([
... documentAssembler,
... embeddings,
... embeddingsFinisher
... ])
>>> data = spark.createDataFrame([["query: how much protein should a female eat",
... "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day." + \
... "But, as you can see from this chart, you'll need to increase that if you're expecting or training for a" + \
... "marathon. Check out the chart below to see how much protein you should be eating each day.",
... ]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+--------------------------------------------------------------------------------+
| result|
+--------------------------------------------------------------------------------+
|[[8.0190285E-4, -0.005974853, -0.072875895, 0.007944068, 0.026059335, -0.0080...|
|[[0.050514214, 0.010061974, -0.04340176, -0.020937217, 0.05170225, 0.01157857...|
+--------------------------------------------------------------------------------+
"""
name = "E5Embeddings"
inputAnnotatorTypes = [AnnotatorType.DOCUMENT]
outputAnnotatorType = AnnotatorType.SENTENCE_EMBEDDINGS
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.E5Embeddings", java_model=None):
super(E5Embeddings, self).__init__(
classname=classname,
java_model=java_model
)
self._setDefault(
dimension=768,
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
-------
E5Embeddings
The restored model
"""
from sparknlp.internal import _E5Loader
jModel = _E5Loader(folder, spark_session._jsparkSession, use_openvino)._java_obj
return E5Embeddings(java_model=jModel)
@staticmethod
[docs] def pretrained(name="e5_small", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "e5_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
-------
E5Embeddings
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(E5Embeddings, name, lang, remote_loc)