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Getting Started with Google BERT

You're reading from   Getting Started with Google BERT Build and train state-of-the-art natural language processing models using BERT

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Product type Paperback
Published in Jan 2021
Publisher Packt
ISBN-13 9781838821593
Length 352 pages
Edition 1st Edition
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Author (1):
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Sudharsan Ravichandiran Sudharsan Ravichandiran
Author Profile Icon Sudharsan Ravichandiran
Sudharsan Ravichandiran
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Table of Contents (15) Chapters Close

Preface 1. Section 1 - Starting Off with BERT
2. A Primer on Transformers FREE CHAPTER 3. Understanding the BERT Model 4. Getting Hands-On with BERT 5. Section 2 - Exploring BERT Variants
6. BERT Variants I - ALBERT, RoBERTa, ELECTRA, and SpanBERT 7. BERT Variants II - Based on Knowledge Distillation 8. Section 3 - Applications of BERT
9. Exploring BERTSUM for Text Summarization 10. Applying BERT to Other Languages 11. Exploring Sentence and Domain-Specific BERT 12. Working with VideoBERT, BART, and More 13. Assessments 14. Other Books You May Enjoy

Exploring the sentence-transformers library

The sentence-transformers library can be installed using pip as shown in the following code:

pip install -U sentence-transformers

The researchers of Sentence-BERT have also made their pre-trained Sentence-BERT models available online. All available pre-trained models can be found here: https://public.ukp.informatik.tu-darmstadt.de/reimers/sentence-transformers/v0.2/.

We can find pre-trained models named bert-base-nli-cls-token, bert-base-nli-mean-token, roberta-base-nli-max-tokens, distilbert-base-nli-mean-tokens, and so on. Let's understand what this means:

  • bert-base-nli-cls-token is a pre-trained Sentence-BERT model where we have taken a pre-trained BERT-base model and fine-tuned it with the NLI dataset, and the model uses a [CLS] token as the sentence representation.
  • bert-base-nli-mean-token is a pre-trained Sentence-BERT model where we have taken a pre-trained BERT-base model and fine-tuned it with the NLI dataset, and the model...
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