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ㅁㅗㅅㅎㅏㄴ ㄴㅏㅇㅁㅏㄴㅇㅣㄹ ㄱㅓㅇㅑ\n* __Tokens__ : ▁ㄱㅗㄱㅏ / ㄷㅗㄹㅗ / ㅇㅔ / ▁ㅃㅣ / ㅈㅕㄴ / ㅏㅇㅗㄴ / ▁ㅊ / ㅗㄹ / ㅗㄱ / ㅇㅣ / ㅍ / ▁ㅇㅏㅁㅏ / ▁ㅇㅣ / ▁ㄷㅗㅅㅣ / ㅇㅔㅅㅓ / ▁ㅇㅠㅇㅣㄹ / ㅎㅣ / ▁ㅈㅓㄱㅇㅡㅇ / ▁ㅁㅗㅅㅎㅏㄴ / ▁ㄴㅏㅇㅁㅏㄴ / ㅇㅣㄹ / ▁ㄱㅓㅇㅑ\n* __Composed tokens__ : ▁고가 / 도로 / 에 / ▁삐 / 젼 / ㅏ온 / ▁ㅊ / ㅗㄹ / ㅗㄱ / 이 / ㅍ / ▁아마 / ▁이 / ▁도시 / 에서 / ▁유일 / 히 / ▁적응 / ▁못한 / ▁낭만 / 일 / ▁거야\n\n\n\n\n## Installation\n\n    pip install parasol-nlp\n\n## Experiment\n\nThe figure shows the results of the perplexity comparison experiment. `with decomposition` is tokenized with charactor decomposition and `no decomposition` is just tokenized.\nExperiment source code is [here](notebooks/Experiment.ipynb).\n\n![comparison_experiment_figure](https://user-images.githubusercontent.com/5267023/92320080-b3f2a280-f059-11ea-9ed9-a937a365d889.png)\n\n## Usage\n\n### Tokenizer\n\nUse [SentencePiece](https://github.com/google/sentencepiece)'s BPE model as tokenizer and [hgtk](https://github.com/bluedisk/hangul-toolkit) for decomposition.\n\n```python\nfrom parasol import Tokenizer\n\n# tokenize after decomposition  \nt1 = Tokenizer(decompose=True)\n# tokenize without decomposition\nt2 = Tokenizer(decompose=False)\n```\n\nthen\n\n```python\n\u003e\u003e\u003e t1.tokenize(\"고가도로에 삐져나온 초록잎 아마 이 도시에서 유일히 적응 못한 낭만일 거야\")\n['▁고가', '도로', '에', '▁삐', '젼', 'ㅏ온', '▁ㅊ', 'ㅗ록', '잎', '▁아마', '▁이', '▁도시', '에서', '▁유일', '히', '▁적응', '▁못한', '▁낭만', '일', '▁거야']\n\u003e\u003e\u003e t2.tokenize(\"고가도로에 삐져나온 초록잎 아마 이 도시에서 유일히 적응 못한 낭만일 거야\")\n['▁고가', '도로', '에', '▁삐', '져', '나온', '▁초록', '잎', '▁아마', '▁이', '▁도시', '에서', '▁유일', '히', '▁적응', '▁못한', '▁낭만', '일', '▁거야']\n\n# Output as vocabulary id\n\u003e\u003e\u003e t1.tokenize(\"고가도로에 삐져나온 초록잎 아마 이 도시에서 유일히 적응 못한 낭만일 거야\", as_id=True)\n[17687, 2135, 36, 8351, 3904, 3842, 52, 12256, 27398, 3469, 30, 6105, 160, 3767, 198, 8953, 2345, 13164, 89, 6872]\n```\n\n### Composer\n\nHangul jamo composer\n\n```python\nfrom parasol import Composer\n\nc = Composer()\n```\n\nthen\n\n```python\n\u003e\u003e\u003e c.compose(\"ㄷㅏㄹㅇㅣ ㄱㅣㅇㅜㄴ ㅂㅏㅁ ㅍㅓㄹㅓㄴㅂㅣㅊㅇㅣ 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