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        "author": "Pattarawat Chormai et al.",
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            "License :: OSI Approved :: MIT License",
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        "description": "# AttaCut: Fast and Reasonably Accurate Word Tokenizer for Thai\n\n[![Build Status](https://travis-ci.org/PyThaiNLP/attacut.svg?branch=master)](https://travis-ci.org/PyThaiNLP/attacut)\n[![Build status](https://ci.appveyor.com/api/projects/status/ittfnb2pyg95kpxk/branch/master?svg=true)](https://ci.appveyor.com/project/wannaphongcom/attacut/branch/master)\n[![](https://img.shields.io/badge/-presentation-informational)](https://drive.google.com/file/d/16AUNZv1HXVmERgryfBf4JpCo1QrQyHHE/view?usp=sharing)\n![](https://img.shields.io/badge/doi-WIP-informational)\n\n## How does AttaCut look like?\n\n<div align=\"center\">\n    <img src=\"https://i.imgur.com/8yMq7IB.png\" width=\"700px\"/>\n    <br/>\n    <b>TL;DR:</b> \n3-Layer Dilated CNN on syllable and character features. It\u2019s <b>6x faster</b> than DeepCut (SOTA) while its WL-f1 on BEST is <b>91%</b>, only 2% lower.\n</div>\n\n## Installation\n\n```\n$ pip install attacut\n```\n\n**Remarks:** Windows users need to install **PyTorch** before the command above.\nPlease consult [PyTorch.org](https://pytorch.org) for more details.\n\n## Usage\n\n### Command-Line Interface\n```\n$ attacut-cli -h\nAttaCut: Fast and Reasonably Accurate Tokenizer for Thai\n\nUsage:\n  attacut-cli <src> [--dest=<dest>] [--model=<model>]\n  attacut-cli (-h | --help)\n\nOptions:\n  -h --help         Show this screen.\n  --model=<model>   Model to be used [default: attacut-sc].\n  --dest=<dest>     If not specified, it'll be <src>-tokenized-by-<model>.txt\n```\n\n### High-Level API \n```\nfrom attacut import tokenize, Tokenizer\n\n# tokenize `txt` using our best model `attacut-sc`\nwords = tokenize(txt)\n\n# alternatively, an AttaCut tokenizer might be instantiated directly, allowing\n# one to specify whether to use `attacut-sc` or `attacut-c`.\natta = Tokenizer(model=\"attacut-sc\")\nwords = atta.tokenize(txt)\n```\n\n## Benchmark Results\n\nBelows are brief summaries. More details can be found on [our benchmarking page](https://pythainlp.github.io/attacut/benchmark.html).\n\n\n### Tokenization Quality\n![](https://pythainlp.github.io/attacut/_images/quality-benchmark-in-of-domain.png)\n\n### Speed\n![](https://pythainlp.github.io/attacut/_images/speed-benchmark-ec2.png)\n\n\n## Retraining on Custom Dataset\nPlease refer to [our retraining page](https://pythainlp.github.io/attacut/)\n\n## Related Resources\n- [Tokenization Visualization][tovis]\n- [Thai Tokenizer Dockers][docker]\n\n## Acknowledgements\nThis repository was initially done by [Pattarawat Chormai][pat], while interning at [Dr. Attapol Thamrongrattanarit's NLP Lab][ate], Chulalongkorn University, Bangkok, Thailand.\nMany people have involed in this project. Complete list of names can be found on [Acknowledgement](https://pythainlp.github.io/attacut/acknowledgement.html).\n\n\n[pat]: http://pat.chormai.org\n[ate]: https://attapol.github.io/lab.html\n[noom]: https://github.com/Ekkalak-T\n[can]: https://github.com/c4n\n[ake]: https://github.com/ekapolc\n[tovis]: https://pythainlp.github.io/tokenization-benchmark-visualization/\n[docker]: https://github.com/PyThaiNLP/docker-thai-tokenizers\n\n\n",
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