BERT Sentence Embeddings (Large Uncased)

Description

This model contains a deep bidirectional transformer trained on Wikipedia and the BookCorpus. The details are described in the paper “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”.

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How to use

...
embeddings = BertEmbeddings.pretrained("sent_bert_large_uncased", "en") \
      .setInputCols("sentence") \
      .setOutputCol("sentence_embeddings")
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, embeddings])
pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
result = pipeline_model.transform(spark.createDataFrame(pd.DataFrame({"text": ["I hate cancer, "Antibiotics aren't painkiller"]})))
...
val embeddings = BertEmbeddings.pretrained("sent_bert_large_uncased", "en")
      .setInputCols("sentence")
      .setOutputCol("sentence_embeddings")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, embeddings))
val result = pipeline.fit(Seq.empty["I hate cancer, "Antibiotics aren't painkiller"].toDS.toDF("text")).transform(data)
import nlu

text = ["I hate cancer", "Antibiotics aren't painkiller"]
embeddings_df = nlu.load('en.embed_sentence.bert_large_uncased').predict(text, output_level='sentence')
embeddings_df

Results

	sentence                            en_embed_sentence_bert_large_uncased_embeddings
	
      I hate cancer 	                  [[-0.13290119171142578, -0.2996622622013092, -...
      Antibiotics aren't painkiller 	[[-0.13290119171142578, -0.2996622622013092, -...

Model Information

Model Name: sent_bert_large_uncased
Type: embeddings
Compatibility: Spark NLP 2.6.0+
License: Open Source
Edition: Official
Input Labels: [sentence]
Output Labels: [sentence_embeddings]
Language: [en]
Dimension: 1024
Case sensitive: false

Data Source

The model is imported from https://tfhub.dev/google/bert_uncased_L-24_H-1024_A-16/1