sparknlp.annotator.embeddings.snowflake_embeddings#

Classes#

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

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 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.

Input Annotation types	Output Annotation type
DOCUMENT	SENTENCE_EMBEDDINGS

References

Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models Snowflake Arctic-Embed Models

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. *

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 = 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'[source]#

inputAnnotatorTypes[source]#

outputAnnotatorType = 'sentence_embeddings'[source]#

poolingStrategy[source]#

setPoolingStrategy(value)[source]#

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:

valuestr

Pooling strategy to use for sentence embeddings

static loadSavedModel(folder, spark_session)[source]#

Loads a locally saved model.

Parameters:

folderstr

Folder of the saved model

spark_sessionpyspark.sql.SparkSession

The current SparkSession

Returns:

SnowFlakeEmbeddings

The restored model

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

Downloads and loads a pretrained model.

Parameters:

namestr, optional

Name of the pretrained model, by default “snowflake_artic_m”

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:

SnowFlakeEmbeddings

The restored model