adithya-domain-specific-bpe-tokenizer 0.1.0

A domain-specific Byte Pair Encoding tokenizer for medical and general NLP corpora

Domain-Specific-BPE-Tokenizer

A from-scratch implementation of a scalable domain-specific Byte Pair Encoding (BPE) tokenizer for medical and general NLP corpora.

This project implements a complete byte-level BPE tokenizer pipeline without relying on external tokenizer frameworks. It includes scalable corpus collection, corpus cleaning, weighted BPE training, merge-rule optimization, serialization, evaluation tooling, benchmarking against GPT-2/tiktoken, automated testing, and PyPI-ready packaging.

The tokenizer was designed for large-scale medical-domain language modeling experiments and was later integrated into a custom GPT-style language model training pipeline.

---

Project Overview

This repository focuses on building and evaluating a domain-specific BPE tokenizer from scratch.

Core goals:

• Implement byte-level BPE training from scratch
• Support scalable weighted word-frequency training
• Train tokenizers on medical and general corpora
• Optimize merge-rule lookup and encoding speed
• Provide serialization and checkpoint support
• Benchmark against GPT-2/tiktoken
• Evaluate fragmentation quality for biomedical terms
• Create a reusable PyPI-ready tokenizer package

---

Key Features

• Byte-level BPE tokenizer
• Weighted word-frequency BPE training
• Domain-specific tokenizer training
• Medical corpus preprocessing pipeline
• Incremental merge-rule learning
• Optimized ranked merge lookup
• Save/load tokenizer support
• Checkpointed tokenizer training
• Scalable corpus chunk processing
• Special token support
• Serialization utilities
• PyPI-ready package structure
• Automated unit tests
• Tokenizer evaluation pipeline
• GPT-2/tiktoken benchmarking

---

Repository Structure

Domain-Specific-BPE-Tokenizer/
├── domain_specific_bpe_tokenizer/
│   ├── __init__.py
│   ├── bpe_tokenizer.py
│   ├── trainer.py
│   ├── encoder.py
│   ├── decoder.py
│   ├── serialization.py
│   └── vocab.py
│
├── tests/
│   ├── test_bpe_tokenizer.py
│   ├── test_vocab.py
│   ├── test_trainer.py
│   ├── test_encoder.py
│   ├── test_decoder.py
│   ├── test_serialization.py
│   ├── test_save_load.py
│   ├── test_encode_decode.py
│   ├── test_special_tokens.py
│   └── test_invalid_inputs.py
│
├── examples/
│   ├── basic_usage.py
│   ├── load_pretrained_tokenizer.py
│   └── medical_tokenization_demo.py
│
├── scripts/
│   ├── prepare_medical_corpus.py
│   ├── collect_general_corpus.py
│   ├── collect_pubmed_corpus.py
│   ├── collect_pmc_open_corpus.py
│   ├── clean_corpus.py
│   ├── build_word_frequencies.py
│   ├── train_tokenizer.py
│   ├── tokenize_corpus.py
│   ├── evaluate_tokenizer.py
│   └── benchmark_against_tiktoken.py
│
├── resources/
│   ├── raw_corpus/
│   ├── cleaned_corpus/
│   ├── tokenized_corpus/
│   ├── trained_tokenizer/
│   └── evaluation/
│
├── docs/
│   ├── architecture.md
│   ├── evaluation.md
│   └── benchmarks.md
│
├── .github/
│   └── workflows/
│       └── tests.yml
│
├── pyproject.toml
├── requirements.txt
├── .gitignore
└── README.md

---

Installation

1. Clone the Repository

git clone https://github.com/adithya-prabhu-22/Domain-Specific-BPE-Tokenizer.git

cd Domain-Specific-BPE-Tokenizer

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2. Install the Package

pip install -e .

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3. Install Optional Benchmark Dependency

pip install tiktoken

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Package Usage

Import the Tokenizer

from domain_specific_bpe_tokenizer import BPETokenizer

---

Train a Tokenizer

from domain_specific_bpe_tokenizer import BPETokenizer

tokenizer = BPETokenizer(
    vocab_size=5000,
    min_frequency=2,
)

text = "medical corpus text goes here"

tokenizer.train(text)

---

Encode Text

encoded = tokenizer.encode(
    "myocardial infarction"
)

print(encoded)

---

Decode Tokens

decoded = tokenizer.decode(encoded)

print(decoded)

---

Save Tokenizer

tokenizer.save(
    "resources/trained_tokenizer/bpe_tokenizer.json"
)

---

Load Tokenizer

loaded_tokenizer = BPETokenizer.load(
    "resources/trained_tokenizer/bpe_tokenizer.json"
)

---

Special Tokens

The tokenizer supports the following built-in special tokens:

Token	Purpose
[UNK]	Unknown token
[PAD]	Padding token
[BOS]	Beginning of sequence
[EOS]	End of sequence

---

Training Pipeline

The tokenizer training pipeline supports scalable corpus preprocessing.

Pipeline stages:

1. Corpus collection
2. Corpus cleaning
3. Word-frequency building
4. Weighted BPE training
5. Merge-rule optimization
6. Tokenization
7. Evaluation
8. Benchmarking

---

Corpus Sources

The project includes scripts for collecting:

• General English corpora
• PubMed abstracts
• PMC Open Access biomedical papers

Example:

python -m scripts.collect_pubmed_corpus

---

Weighted BPE Training

The tokenizer supports weighted word-frequency BPE training for scalable preprocessing.

Features:

• Frequency-based merge learning
• Rare-word filtering
• Checkpoint saving
• Large-corpus subset selection
• Optimized pair-frequency updates

Example:

python -m scripts.train_tokenizer

---

Tokenizer Evaluation

The repository includes tokenizer evaluation tooling.

Evaluation metrics include:

• Compression ratio
• Average characters per token
• Unknown token rate
• Medical term fragmentation
• Token count statistics

Run evaluation:

python -m scripts.evaluate_tokenizer

---

Benchmark Against GPT-2/tiktoken

The repository includes benchmarking against GPT-2/tiktoken.

Comparison metrics:

• Token count
• Compression ratio
• Medical-term fragmentation
• Domain-token efficiency

Run benchmark:

python -m scripts.benchmark_against_tiktoken

---

Example Benchmark Result

Medical Term Fragmentation

Medical Term	Custom BPE Tokens	GPT-2 Tokens
electrocardiogram	1	5
pneumothorax	1	5
myocardial infarction	3	6
hepatocellular carcinoma	3	7

The custom medical-domain tokenizer significantly reduces fragmentation for biomedical terminology compared with GPT-2/tiktoken.

---

Tokenizer Training Scale

The tokenizer training pipeline evolved across multiple scalability stages:

Stage	Vocabulary Size	Corpus Scale
Initial Prototype	500	Small sample corpus
Intermediate Training	24K	Multi-million token corpus
Final Biomedical Tokenizer	52K	~240M-token medical corpus

---

Optimization Features

The tokenizer includes several performance optimizations:

• Ranked merge lookup
• Pair-frequency indexing
• Safe affected-word updates
• Chunk-based corpus processing
• Cached frequency-table loading
• Incremental merge updates

These optimizations significantly improved scalability during large-corpus tokenizer training.

---

Testing

The repository includes a comprehensive automated test suite.

Current coverage includes:

• Tokenizer initialization
• Encoding
• Decoding
• Save/load serialization
• Special tokens
• Invalid input handling
• End-to-end tokenizer consistency

Run tests:

pytest

Current Status

26 tests passed

---

PyPI Packaging

The repository is structured as a reusable Python package using pyproject.toml.

Editable installation:

pip install -e .

The public API is exposed through:

from domain_specific_bpe_tokenizer import BPETokenizer

---

Documentation

Additional documentation is available in:

docs/

Including:

• tokenizer architecture
• evaluation methodology
• benchmark reports

---

Integration with LLM Training

This tokenizer was later integrated into a custom GPT-style medical language model training pipeline.

The tokenizer was used for:

• domain-specific tokenization
• autoregressive GPT training
• tokenizer-quality experiments
• KV-cache benchmarking studies

---

Future Work

Future improvements include:

• Rust implementation for faster preprocessing
• Parallel BPE training
• Memory-efficient billion-token preprocessing
• Streaming tokenizer training
• Unicode normalization improvements
• Faster serialization formats
• Hugging Face tokenizer interoperability
• Tokenizer visualization tools
• Vocabulary pruning experiments

---

Conclusion

This project demonstrates a complete from-scratch implementation of a scalable domain-specific BPE tokenizer.

The repository evolved from a simple educational tokenizer into a scalable biomedical NLP preprocessing pipeline featuring weighted BPE training, optimized merge learning, tokenizer evaluation, benchmarking against GPT-2/tiktoken, automated testing, and PyPI-ready packaging.

The strongest results appeared in medical-domain tokenization, where the tokenizer significantly reduced fragmentation for biomedical terminology compared with GPT-2/tiktoken.

Overall, the project demonstrates the importance of tokenizer design, merge-rule optimization, scalable preprocessing, and domain-specific vocabulary construction for modern NLP and LLM systems.