SentencePiece: Turning Text into Tidy Tokens for Our Translator 🧱➡️🔢

Welcome to our tutorial on using SentencePiece to create a powerful, language-agnostic tokenizer! Learn how to transform raw English and Spanish text into organized, numerical tokens – an essential first step for building high-quality Neural Machine Translation models. 🗣️🔁

This repository contains a tutorial notebook (.ipynb) that guides you through the process of training your own SentencePiece tokenizer from scratch. We cover everything from preparing your text data to training the model and testing its capabilities.

Why This Tutorial? 🤔

• Practical Focus: Get hands-on experience with a widely-used tokenization tool in NLP.
• Clear Steps: Follow a simple, step-by-step guide to train your own tokenizer for English-Spanish.
• NMT Foundation: Understand a crucial preprocessing step required for many translation and language generation tasks.
• Beginner-Friendly: Suitable for those new to tokenization or SentencePiece, with clear explanations.

Tutorial Outline ("Our Tokenizing Adventure") 📚

1. Setting the Stage:
   • Introduction to why tokenization is needed for NLP models.
   • What SentencePiece is and its key advantages (subword units, language-agnostic).
2. Getting Your Tools Ready:
   • Installing the sentencepiece library.
   • Downloading and preparing the English-Spanish dataset (spa-eng.zip).
3. Training Your Custom Tokenizer:
   • Preparing a combined text file from English and Spanish sentences for SentencePiece training.
   • Using SentencePieceTrainer to train your model, understanding key parameters like vocab_size, model_type (Unigram vs. BPE), character_coverage, and special token IDs (<pad>, <s>, </s>, <unk>).
4. Testing Your New Tokenizer:
   • Loading your trained SentencePiece model (.model file).
   • Encoding text into subword pieces and numerical IDs.
   • Decoding IDs back into human-readable text to verify.
   • Understanding the role of the .vocab file.

Key SentencePiece Concepts/Functions Covered ✨

• Subword Tokenization (Unigram & BPE)
• sentencepiece.SentencePieceTrainer.train(): Training a new tokenizer.
  • input: Specifying training data.
  • model_prefix: Naming your output model files.
  • vocab_size: Setting the desired vocabulary size.
  • character_coverage: Ensuring representation of characters.
  • model_type: Choosing between unigram or bpe.
  • Defining pad_id, bos_id, eos_id, unk_id.
• sentencepiece.SentencePieceProcessor(): Loading a trained model.
  • load(): (Implicitly called or can use model_file= in constructor).
  • encode_as_pieces(): Converting text to subword strings.
  • encode_as_ids(): Converting text to numerical IDs.
  • decode_pieces(): Converting subword strings back to text.
  • decode_ids(): Converting numerical IDs back to text.
  • Accessing special token IDs (bos_id(), eos_id(), etc.) and vocabulary size (get_piece_size()).
• Understanding the .model and .vocab output files.
• Text Normalization (default NFKC, nmt_nfkc_cf).

Dive into the notebook and start turning your text into tidy tokens!