Method 1: NER First

Learn about how NER is used for Q/A tasks for transformers.

We'll cover the following

Using NER to find questions
Location entity questions
Applying heuristics
Project management
Person entity questions
Code playground

This lesson will use NER to help us find ideas for good questions. Transformer models are continuously trained and updated. Also, the datasets used for training might change. Finally, these are not rule-based algorithms that produce the same result each time. The outputs might change from one run to another.

NER can detect people, locations, organizations, and other entities in a sequence. We will first run a NER task that will give us some of the main parts of the paragraph we can focus on to ask questions.

Using NER to find questions

We will continue to run QA_1.ipynb cell by cell. The program now initializes the pipeline with the NER task to perform with the default model and tokenizer:

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Course Introduction

Basics of Transformers

Architecture of the Transformer Model

Fine-Tuning BERT Models

Pretraining a RoBERTa Model from Scratch

Downstream NLP Tasks with Transformers

Machine Translation with the Transformer

The Rise of Transformers with GPT-3 Engines

Applying Transformers for AI Text Summarization

Matching Tokenizers and Datasets

Transformers for NLP—Exam 1

Semantic Role Labeling with BERT-Based Transformers

Your Data Speaks: Story, Questions, and Answers

Detecting Customer Emotions to Make Predictions

Analyzing Fake News with Transformers

Interpreting Black Box Transformer Models

From NLP to Task-Agnostic Transfomer Models

The Emergence of Transformer-Driven Copilots

Transformers for NLP—Exam 2

Conclusion

Appendix

Method 1: NER First

Using NER to find questions