BERT Embeddings trained on Wikipedia and BooksCorpus and fine-tuned on QQP

Description

This model uses a BERT base architecture initialized from https://tfhub.dev/google/experts/bert/wiki_books/1 and fine-tuned on QQP.

This is a BERT base architecture but some changes have been made to the original training and export scheme based on more recent learnings.

Download Copy S3 URI

How to use

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

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

val embeddings = BertEmbeddings.pretrained("bert_wiki_books_qqp", "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_qqp').predict(text, output_level='token')
embeddings_df

Model Information

Model Name:	bert_wiki_books_qqp
Compatibility:	Spark NLP 3.2.0+
License:	Open Source
Edition:	Official
Input Labels:	[sentence, token]
Output Labels:	[bert]
Language:	en
Case sensitive:	false

Data Source

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

PREVIOUSBERT Embeddings trained on Wikipedia and BooksCorpus and fine-tuned on QNLI

NEXTBERT Embeddings trained on Wikipedia and BooksCorpus and fine-tuned on SQuAD 2.0