sparknlp.annotator.embeddings.e5_embeddings#

Classes#

class E5Embeddings(classname='com.johnsnowlabs.nlp.embeddings.E5Embeddings', java_model=None)[source]#

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 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.

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

microsoft/unilm

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...|
+--------------------------------------------------------------------------------+

setConfigProtoBytes(b)[source]#

Sets configProto from tensorflow, serialized into byte array.

Parameters:

bList[int]

ConfigProto from tensorflow, serialized into byte array

static loadSavedModel(folder, spark_session, use_openvino=False)[source]#

Loads a locally saved model.

Parameters:

folderstr

Folder of the saved model

spark_sessionpyspark.sql.SparkSession

The current SparkSession

use_openvinobool

Use OpenVINO backend

Returns:

E5Embeddings

The restored model

static pretrained(name='e5_small', lang='en', remote_loc=None)[source]#

Downloads and loads a pretrained model.

Parameters:

namestr, optional

Name of the pretrained model, by default “e5_small”

langstr, optional

Language of the pretrained model, by default “en”

remote_locstr, optional

Optional remote address of the resource, by default None. Will use Spark NLPs repositories otherwise.

Returns:

E5Embeddings

The restored model