BERT 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.

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How to use

embeddings = BertEmbeddings.pretrained("bert_wiki_books", "en") \
.setInputCols("sentence", "token") \
.setOutputCol("embeddings")

nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings])

val embeddings = BertEmbeddings.pretrained("bert_wiki_books", "en")
.setInputCols("sentence", "token")
.setOutputCol("embeddings")

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

import nlu

text = ["I love NLP"]
embeddings_df = nlu.load('en.embed.bert.wiki_books').predict(text, output_level='token')
embeddings_df

Model Information

Data Source

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