BERT Sentence Embeddings trained on Wikipedia and BooksCorpus

Description

This model uses a BERT base architecture pretrained from scratch on Wikipedia and BooksCorpus. This is a BERT base architecture but some changes have been made to the original training and export scheme based on more recent learning that improve its accuracy over the original BERT base checkpoint.

This model is intended to be used for a variety of English NLP tasks. The pre-training data contains more formal text and the model may not generalize to more colloquial text such as social media or messages.

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How to use

sent_embeddings = BertSentenceEmbeddings.pretrained("sent_bert_wiki_books", "en") \
.setInputCols("sentence") \
.setOutputCol("bert_sentence")

nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, sent_embeddings ])

val sent_embeddings = BertSentenceEmbeddings.pretrained("sent_bert_wiki_books", "en")
.setInputCols("sentence")
.setOutputCol("bert_sentence")

val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, sent_embeddings ))

import nlu

text = ["I love NLP"]
sent_embeddings_df = nlu.load('en.embed_sentence.bert.wiki_books').predict(text, output_level='sentence')
sent_embeddings_df

Model Information

Data Source

This Model has been imported from: https://tfhub.dev/google/experts/bert/wiki_books/2