Sentence embeddings using MPNet.

The MPNet model was proposed in MPNet: Masked and Permuted Pre-training for Language Understanding by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu. 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.

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:

val 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.

For extended examples of usage, see MPNetEmbeddingsTestSpec.

Sources :

MPNet: Masked and Permuted Pre-training for Language Understanding

MPNet Github Repository

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.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotators.Tokenizer
import com.johnsnowlabs.nlp.embeddings.MPNetEmbeddings
import com.johnsnowlabs.nlp.EmbeddingsFinisher
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val embeddings = MPNetEmbeddings.pretrained("all_mpnet_base_v2", "en")
  .setInputCols("document")
  .setOutputCol("mpnet_embeddings")

val embeddingsFinisher = new EmbeddingsFinisher()
  .setInputCols("mpnet_embeddings")
  .setOutputCols("finished_embeddings")
  .setOutputAsVector(true)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  embeddings,
  embeddingsFinisher
))

val data = Seq("This is an example sentence", "Each sentence is converted").toDF("text")
val result = pipeline.fit(data).transform(data)

result.selectExpr("explode(finished_embeddings) as result").show(1, 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...|
+--------------------------------------------------------------------------------+