Method 0: Trial and Error

Learn about the trial and error method.

We'll cover the following

Importing Hugging Face
Initializing transformer models and tasks
Providing text to the transformer
Code playground
Takeaways

Let’s start with the trial and error approach.

Question-answering seems very easy. Is that true? Let’s find out. We'll be using the QA_0.ipynb Jupyter notebook this lesson. We will run the notebook cell by cell.

We will first import Hugging Face’s pipeline, which contains many ready-to-use transformer resources. They provide high-level abstraction functions for the Hugging Face library resources to perform a wide range of tasks. We can access those NLP tasks through a simple API.

Importing Hugging Face

The pipeline is imported with one line of code:

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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 0: Trial and Error

Importing Hugging Face