sparknlp.annotator.embeddings.mpnet_embeddings#

Classes#

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

Sentence embeddings using MPNet.

MPNet adopts a novel pre-training method, named masked and permuted language modeling, to inherit the advantages of masked language modeling and permuted language modeling for natural language understanding.

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

>>> embeddings = MPNetEmbeddings.pretrained() \
...     .setInputCols(["document"]) \
...     .setOutputCol("mpnet_embeddings")

The default model is "all_mpnet_base_v2", 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

MPNet: Masked and Permuted Pre-training for Language Understanding

microsoft/MPNet

Paper abstract

*BERT adopts masked language modeling (MLM) for pre-training and is one of the most successful pre-training models.

Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for pre-training to address this problem. However, XLNet does not leverage the full position information of a sentence and thus suffers from position discrepancy between pre-training and fine-tuning. In this paper, we propose MPNet, a novel pre-training method that inherits the advantages of BERT and XLNet and avoids their limitations. MPNet leverages the dependency among predicted tokens through permuted language modeling (vs. MLM in BERT), and takes auxiliary position information as input to make the model see a full sentence and thus reducing the position discrepancy (vs. PLM in XLNet). We pre-train MPNet on a large-scale dataset (over 160GB text corpora) and fine-tune on a variety of down-streaming tasks (GLUE, SQuAD, etc). Experimental results show that MPNet outperforms MLM and PLM by a large margin, and achieves better results on these tasks compared with previous state-of-the-art pre-trained methods (e.g., BERT, XLNet, RoBERTa) under the same model setting.*

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> embeddings = MPNetEmbeddings.pretrained() \
...     .setInputCols(["document"]) \
...     .setOutputCol("mpnet_embeddings")
>>> embeddingsFinisher = EmbeddingsFinisher() \
...     .setInputCols(["mpnet_embeddings"]) \
...     .setOutputCols("finished_embeddings") \
...     .setOutputAsVector(True)
>>> pipeline = Pipeline().setStages([
...     documentAssembler,
...     embeddings,
...     embeddingsFinisher
... ])
>>> data = spark.createDataFrame([["This is an example sentence", "Each sentence is converted"]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+--------------------------------------------------------------------------------+
|                                                                          result|
+--------------------------------------------------------------------------------+
|[[0.022502584, -0.078291744, -0.023030775, -0.0051000593, -0.080340415, 0.039...|
|[[0.041702367, 0.0010974605, -0.015534201, 0.07092203, -0.0017729357, 0.04661...|
+--------------------------------------------------------------------------------+

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:

MPNetEmbeddings

The restored model

static pretrained(name='all_mpnet_base_v2', lang='en', remote_loc=None)[source]#

Downloads and loads a pretrained model.

Parameters:

namestr, optional

Name of the pretrained model, by default “all_mpnet_base_v2”

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:

MPNetEmbeddings

The restored model