sparknlp.annotator.embeddings.bge_embeddings#

Classes#

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

Sentence embeddings using BGE.

BGE, or BAAI General Embeddings, a model that can map any text to a low-dimensional dense

vector which can be used for tasks like retrieval, classification, clustering, or semantic search.

Pretrained models can be loaded with pretrained of the companion object:

>>> embeddings = BGEEmbeddings.pretrained() \
...     .setInputCols(["document"]) \
...     .setOutputCol("bge_embeddings")

The default model is "bge_base", 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.

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> embeddings = BGEEmbeddings.pretrained() \
...     .setInputCols(["document"]) \
...     .setOutputCol("bge_embeddings")
>>> embeddingsFinisher = EmbeddingsFinisher() \
...     .setInputCols(["bge_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)[source]#

Loads a locally saved model.

Parameters:

folderstr

Folder of the saved model

spark_sessionpyspark.sql.SparkSession

The current SparkSession

Returns:

BGEEmbeddings

The restored model

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

Downloads and loads a pretrained model.

Parameters:

namestr, optional

Name of the pretrained model, by default “bge_base”

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:

BGEEmbeddings

The restored model