https://github.com/dvdagames/pgn-tokenizer
A byte pair encoding (BPE) tokenizer for chess portable game notation (PGN)
https://github.com/dvdagames/pgn-tokenizer
bpe byte-pair-encoding chess llm pgn tokenizer
Last synced: 3 months ago
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A byte pair encoding (BPE) tokenizer for chess portable game notation (PGN)
- Host: GitHub
- URL: https://github.com/dvdagames/pgn-tokenizer
- Owner: DVDAGames
- License: mit
- Created: 2025-01-24T14:00:29.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-28T17:03:15.000Z (about 1 year ago)
- Last Synced: 2025-10-29T01:49:51.700Z (7 months ago)
- Topics: bpe, byte-pair-encoding, chess, llm, pgn, tokenizer
- Language: Python
- Homepage: https://huggingface.co/InterwebAlchemy/PGNTokenizer
- Size: 1.5 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: docs/CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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README
# PGN Tokenizer
This is a Byte Pair Encoding (BPE) tokenizer for chess Portable Game Notation (PGN).
## Installation
You can install it with your package manager of choice:
### uv
```bash
uv add pgn-tokenizer
```
### pip
```bash
pip install pgn-tokenizer
```
## Usage
It exposes a simple interface with `.encode()` and `.decode()` methods, and a `.vocab_size` property, but you can also access the underlying `PreTrainedTokenizerFast` class from the `transformers` library via the `.tokenizer` property.
```python
from pgn_tokenizer import PGNTokenizer
# Initialize the tokenizer
tokenizer = PGNTokenizer()
# Tokenize a PGN string
tokens = tokenizer.encode("1.e4 Nf6 2.e5 Nd5 3.c4 Nb6")
# Decode the tokens back to a PGN string
decoded = tokenizer.decode(tokens)
# get vocab from underlying tokenizer class
vocab = tokenizer.tokenizer.get_vocab()
```
## Implementation
It is uses the [`tokenizers`](https://huggingface.co/docs/tokenizers/) library from Hugging Face for training the tokenizer and the [`transformers`](https://huggingface.co/docs/transformers/) library from Hugging Face for initializing the tokenizer from the pretrained tokenizer model for faster tokenization.
**Note**: This is part of a work-in-progress project to investigate how language models might understand chess without an engine or any chess-specific knowledge.
## Tokenizer Comparison
More traditional, language-focused BPE tokenizer implementations are not suited for PGN strings because they are more likely to break the actual moves apart.
For example `1.e4 Nf6` would likely be tokenized as `1`, `.`, `e`, `4`, ` N`, `f`, `6` or `1`, `.e`, `4`, ` `, ` N`, `f`, `6` depending on the tokenizer's vocabulary, but with the specialized PGN tokenizer it would be tokenized as `1.`, `e4`, ` Nf6`.
### Visualization
Here is a visualization of the vocabulary of this specialized PGN tokenizer compared to the BPE tokenizer vocabularies of the `cl100k_base` (the vocabulary for the `gpt-3.5-turbo` and `gpt-4` models' tokenizer) and the `o200k_base` (the vocabulary for the `gpt-4o` model's tokenizer):
#### PGN Tokenizer
**Note**: The tokenizer was trained with ~2.8 Million chess games in PGN notation with a target vocabulary size of `4096`.
#### GPT-3.5-turbo and GPT-4 Tokenizers
#### GPT-4o Tokenizer
**Note**: These visualizations were generated with a function adapted from an [educational Jupyter Notebook in the `tiktoken` repository](https://github.com/openai/tiktoken/blob/main/tiktoken/_educational.py#L186).
## Acknowledgements
- [@karpathy](https://github.com/karpathy) for the [Let's build the GPT Tokenizer tutorial](https://youtu.be/zduSFxRajkE)
- [Hugging Face](https://huggingface.co/) for the [`tokenizers`](https://huggingface.co/docs/tokenizers/) and [`transformers`](https://huggingface.co/docs/transformers/) libraries.
- Kaggle user [MilesH14](https://www.kaggle.com/milesh14), whoever you are for the now-missing dataset of 3.5 million chess games referenced in many places, including this [research documentation](https://chess-research-project.readthedocs.io/en/latest/)