NLP Course

0. Setup

1. Transformer models

2. Using 🤗 Transformers

3. Fine-tuning a pretrained model

4. Sharing models and tokenizers

5. The 🤗 Datasets library

6. The 🤗 Tokenizers library

7. Main NLP tasks

8. How to ask for help

9. Building and sharing demos new

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Tokenizers, check!

Great job finishing this chapter!

After this deep dive into tokenizers, you should:

Be able to train a new tokenizer using an old one as a template
Understand how to use offsets to map tokens’ positions to their original span of text
Know the differences between BPE, WordPiece, and Unigram
Be able to mix and match the blocks provided by the 🤗 Tokenizers library to build your own tokenizer
Be able to use that tokenizer inside the 🤗 Transformers library