TRANSFORMER MODELS

Natural Language Processing

Transformers, what can they do?

How do Transformers work?

Encoder models

Decoder models

Sequence-to-sequence models

Bias and limitations

Summary

Using Hugging Face Transformers

Behind the pipeline

Models

Tokenizers

Handling multiple sequences

Putting it all together

Basic usage completed!

Fine-tuning a Pre-trained Model

Processing the data

Fine-tuning a model with the Trainer API or Keras

A full training

Fine-tuning, Check!

The Hugging Face Hub

Using pretrained models

Sharing pretrained models

Building a model card

Datasets Library

What if my dataset isn't on the Hub?

Time to slice and dice

Big data? The Hugging Face Datasets to the rescue!

Creating your own dataset

Semantic search with FAISS

The Hugging Face Datasets, check!

Tokenizers Library

Training a new tokenizer from an old one

Fast tokenizers' special powers

Fast tokenizers in the QA pipeline

Normalization and pre-tokenization

Byte-Pair Encoding tokenization

WordPiece tokenization

Unigram tokenization

Building a tokenizer, block by block

Tokenizers, check!

Main NLP Tasks.

Token classification

Fine-tuning a masked language model

Translation

Summarization

Training a causal language model from scratch

Question answering

Mastering NLP

How to ask for help.

What to do when you get an error

Asking for help on the forums

Debugging the training pipeline

How to write a good issue

Introduction to Gradio

Building your first demo

Understanding the Interface class

Sharing demos with others

Integrations with the Hugging Face Hub

Advanced Interface features

Introduction to Blocks #1

Introduction to Blocks #2

Gradio, check!