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


This model uses a BERT base architecture initialized from and fine-tuned on QNLI.

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


How to use

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

nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings])
val embeddings = BertEmbeddings.pretrained("bert_wiki_books_qnli", "en")
      .setInputCols("sentence", "token")

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_qnli').predict(text, output_level='token')

Model Information

Model Name: bert_wiki_books_qnli
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: