Summary  
This chapter demonstrates how to implement code that builds a byte-pair-encoding tokenizer by initializing a BPE model with whitespace pre-tokenization, training it on text files with custom vocabulary and frequency settings, saving and loading the tokenizer, and encoding input strings.

General domain of usage  
Natural language processing

When pre-training a language model on a domain-specific corpus, you often need a tokenizer trained on that same data. Hugging Face's `tokenizers` library lets you build a custom BPE tokenizer in three steps: **initialize**, **train**, **save**.



## Step 1: Initialize

```python
from tokenizers import Tokenizer, models, pre_tokenizers

# Creating a BPE tokenizer with whitespace pre-tokenization
tokenizer = Tokenizer(models.BPE())
tokenizer.pre_tokenizer = pre_tokenizers.Whitespace()
```

`pre_tokenizers.Whitespace()` splits text on whitespace before BPE merges are applied – this keeps words as the base units before subword splitting.



## Step 2: Train

```python
from tokenizers.trainers import BpeTrainer

trainer = BpeTrainer(
    vocab_size=10000,
    min_frequency=2,
    special_tokens=["<pad>", "<unk>", "<s>", "</s>"]
)

# Training on a list of plain text files
files = ["corpus_part1.txt", "corpus_part2.txt"]
tokenizer.train(files, trainer)
```

`min_frequency=2` means a pair must appear at least twice to be considered for merging. `special_tokens` are reserved entries added to the vocabulary regardless of frequency.



## Step 3: Save and Load

```python
# Saving vocabulary and merge rules to disk
tokenizer.save("bpe_tokenizer.json")

# Loading it back
tokenizer = Tokenizer.from_file("bpe_tokenizer.json")

# Encoding a sample string
encoding = tokenizer.encode("Language models learn from text.")
print(encoding.tokens)
print(encoding.ids)
```

Run this locally with a small `.txt` file as your corpus to see which merges the tokenizer learns and how it splits unseen text.

Which of the following is NOT a required step when building a BPE tokenizer with Hugging Face's tokenizers library?

Master the process of training large language models from scratch: explore tokenization, data pipelines, language modeling objectives, training loops, optimization strategies, and evaluation metrics. Gain hands-on experience with Hugging Face tools and PyTorch, culminating in a capstone implementation challenge.

From raw text to a trained large language model: tokenization, data processing, training objectives, optimization, and evaluation.

Building a BPE Tokenizer with Hugging Face

Step 1: Initialize

Step 2: Train

Step 3: Save and Load