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  • John Snow Labs
  • Jul 16, 2025

Sentence Embeddings - Bluebert uncased (MedNLI, OpenVINO)

  • openvino
  • english
  • embeddings
  • licensed
  • bert
  • clinical
  • en

Description

This model is trained to generate contextual sentence embeddings of input sentences. It has been fine-tuned on MedNLI dataset to provide sota performance on STS and SentEval Benchmarks.

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How to use

from johnsnowlabs import nlp

documentAssembler = nlp.DocumentAssembler() \
    .setInputCol("text") \
    .setOutputCol("document")

sentence = nlp.SentenceDetector() \
    .setInputCols(["document"]) \
    .setOutputCol("sentence")

embeddings = nlp.BertSentenceEmbeddings.pretrained("sbluebert_base_uncased_mli_openvino", "en", "clinical/models") \
    .setInputCols(["sentence"]) \
    .setOutputCol("sentence_bert_embeddings")

embeddingsFinisher = nlp.EmbeddingsFinisher() \
    .setInputCols(["sentence_bert_embeddings"]) \
    .setOutputCols("finished_embeddings") \
    .setOutputAsVector(True)

pipeline = nlp.Pipeline().setStages([
    documentAssembler,
    sentence,
    embeddings,
    embeddingsFinisher
])

data = spark.createDataFrame([
    ['William Henry Gates III (born October 28, 1955) is an American business magnate, software developer, investor, and philanthropist.']
]).toDF("text")

model = pipeline.fit(data)
result = model.transform(data)

result.selectExpr("explode(finished_embeddings) as result").show(5, 80)


import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotator.SentenceDetector
import com.johnsnowlabs.nlp.embeddings.BertSentenceEmbeddings
import com.johnsnowlabs.nlp.EmbeddingsFinisher
import org.apache.spark.ml.Pipeline

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

val sentenceDetector = new SentenceDetector()
  .setInputCols("document")
  .setOutputCol("sentence")

val embeddings = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli_openvino", "en", "clinical/models")
  .setInputCols("sentence")
  .setOutputCol("sentence_bert_embeddings")

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

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

val data = Seq(
  "William Henry Gates III (born October 28, 1955) is an American business magnate, software developer, investor, and philanthropist."
).toDF("text")

val model = pipeline.fit(data)
val result = model.transform(data)

result.selectExpr("explode(finished_embeddings) as result").show(5, 80)

Results


+--------------------------------------------------------------------------------+
|                                                                          result|
+--------------------------------------------------------------------------------+
|[-0.4706500768661499,-0.605597734451294,0.6036401987075806,-0.590848147869110...|
+--------------------------------------------------------------------------------+

Model Information

Model Name: sbluebert_base_uncased_mli_openvino
Compatibility: Spark NLP 6.0.0+
License: Licensed
Edition: Official
Input Labels: [document]
Output Labels: [bert]
Language: en
Size: 407.1 MB