Next Steps

Learn about how SRL, NER and Q/A tasks are performed with more advanced models.

We'll cover the following

Exploring Q&A with a RoBERTa model
- Code playground
Exploring Q&A with a GPT-3 engine

There is no easy way to implement question answering or shortcuts. We began to implement methods that could generate questions automatically. Automatic question generation is a critical aspect of NLP.

More transformer models need to be pretrained with multi-task datasets containing NER, SRL, and question-answering problems to solve. Project managers also need to learn how to combine several NLP tasks to help solve a specific task, such as question answering.

Coreference resolution could have helped our model identify the main subjects in the sequence we worked on. This result produced with AllenNLP shows an interesting analysis:

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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

Next Steps