Qtok 0.11.0

Qtok: quality control tool for tokenization

Qtok: Quality Control Tool for Tokenizers

Qtok is a Python-based tool designed for quality control and analysis of tokenizers used in natural language processing (NLP) tasks.

Features

• Analyze multiple tokenizer vocabularies simultaneously
• Generate statistics on token distribution
• Produce visualizations of token characteristics
• Compare multiple tokenizers
• Analyze Unicode coverage
• Assess language-specific token distributions (Latin and Cyrillic scripts)

Installation

You can install Qtok using pip:

pip install qtok

Or clone the repository and install:

git clone https://github.com/nup-csai/Qtok.git
cd Qtok
pip install .

Usage

Qtok can be used as a command-line tool:

qtok -i /path/to/tokenizer1.json /path/to/tokenizer2.json ... -l label1 label2 ... -o /path/to/output/folder [--latex]

Arguments:

• -i: Paths to the tokenizer JSON files or URLs (required, multiple inputs accepted)
• -l: Labels for the tokenizers (required, must match the number of input files)
• -o: Output folder for results (required)
• --latex: Optional flag to generate LaTeX and PDF reports (default: False)

Example:

qtok -i /path/to/tokenizer1.json /path/to/tokenizer2.json -l label1 label2 -o /path/to/output/folder --latex

• Arguments:
  • -i: Paths to the tokenizer JSON files or URLs (required, multiple inputs accepted)
  • -l: Labels for the tokenizers (required, must match the number of input files)
  • -o: Output folder for results (required)

Output

Qtok generates several output files:

1. basic_stats.tsv and basic_stats.png: Basic statistics of the tokenizers
2. unicode_stats.tsv and unicode_stats.png: Unicode coverage statistics
3. latin_stats.tsv and latin_stats.png: Statistics for Latin script tokens
4. cyrillic_stats.tsv and cyrillic_stats.png: Statistics for Cyrillic script tokens
5. report.html: An HTML report summarizing all analyses
6. report.tex and report.pdf: LaTeX and PDF versions of the report (if --latex flag is used and pdflatex is installed)

Requirements

• Python 3.6+
• matplotlib
• numpy
• pandas
• requests
• tqdm

Reproducibility

For full tables and data, please refer to the Jupyter notebook available at:

Qtok/paper/Qtok_v3.ipynb

Contributing

Contributions to Qtok are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Authors

• Aleksey Komissarov
• Iaroslav Chelombitko
• Egor Safronov

Contact

For any queries, please contact [e-mail].

Acknowledgments

• Thanks to all contributors and users of Qtok
• Special thanks to the NLP community for inspiration and support