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from __future__ import absolute_import, division, print_function |
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import os |
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from functools import reduce |
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from pathlib import Path |
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import datasets |
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_CITATION = """\ |
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@inproceedings{kosawat2009best, |
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title={BEST 2009: Thai word segmentation software contest}, |
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author={Kosawat, Krit and Boriboon, Monthika and Chootrakool, Patcharika and Chotimongkol, Ananlada and Klaithin, Supon and Kongyoung, Sarawoot and Kriengket, Kanyanut and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and others}, |
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booktitle={2009 Eighth International Symposium on Natural Language Processing}, |
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pages={83--88}, |
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year={2009}, |
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organization={IEEE} |
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} |
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@inproceedings{boriboon2009best, |
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title={Best corpus development and analysis}, |
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author={Boriboon, Monthika and Kriengket, Kanyanut and Chootrakool, Patcharika and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and Kosawat, Krit}, |
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booktitle={2009 International Conference on Asian Language Processing}, |
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pages={322--327}, |
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year={2009}, |
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organization={IEEE} |
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} |
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""" |
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_LICENSE = "CC-BY-NC-SA 3.0" |
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_DESCRIPTION = """\ |
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`best2009` is a Thai word-tokenization dataset from encyclopedia, novels, news and articles by |
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[NECTEC](https://www.nectec.or.th/) (148,995/2,252 lines of train/test). It was created for |
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[BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10). |
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The test set answers are not provided publicly. |
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""" |
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class Best2009Config(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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"""BuilderConfig |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(Best2009Config, self).__init__(**kwargs) |
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class Best2009(datasets.GeneratorBasedBuilder): |
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_DOWNLOAD_URL = "https://archive.org/download/best_dataset/data.zip" |
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_TRAIN_FOLDER = "train" |
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_TEST_FOLDER = "test" |
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_USELESS_TAGS = {"<NE>": "", "</NE>": "", "<AB>": "", "</AB>": ""} |
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_CHAR_TYPES_DICT = { |
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"กขฃคฆงจชซญฎฏฐฑฒณดตถทธนบปพฟภมยรลวศษสฬอ": "c", |
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"ฅฉผฟฌหฮ": "n", |
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"ะาำิีืึุู": "v", |
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"เแโใไ": "w", |
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"่้๊๋": "t", |
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"์ๆฯ.": "s", |
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"0123456789๑๒๓๔๕๖๗๘๙": "d", |
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'"': "q", |
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"‘": "q", |
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"’": "q", |
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"'": "q", |
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" ": "p", |
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"abcdefghijklmnopqrstuvwxyz": "s_e", |
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"ABCDEFGHIJKLMNOPQRSTUVWXYZ": "b_e", |
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} |
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_CHAR_TYPE_FLATTEN = {} |
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for ks, v in _CHAR_TYPES_DICT.items(): |
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for k in ks: |
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_CHAR_TYPE_FLATTEN[k] = v |
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_CHAR_TYPES = ["b_e", "c", "d", "n", "o", "p", "q", "s", "s_e", "t", "v", "w"] |
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BUILDER_CONFIGS = [ |
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Best2009Config( |
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name="best2009", |
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version=datasets.Version("1.0.0"), |
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description=_DESCRIPTION, |
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), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"fname": datasets.Value("string"), |
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"char": datasets.Sequence(datasets.Value("string")), |
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"char_type": datasets.Sequence(datasets.features.ClassLabel(names=self._CHAR_TYPES)), |
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"is_beginning": datasets.Sequence(datasets.features.ClassLabel(names=["neg", "pos"])), |
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} |
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), |
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supervised_keys=None, |
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homepage="https://aiforthai.in.th/", |
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citation=_CITATION, |
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license=_LICENSE, |
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) |
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def _split_generators(self, dl_manager): |
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arch_path = dl_manager.download_and_extract(self._DOWNLOAD_URL) |
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data_dir = os.path.join(arch_path, "data") |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": os.path.join(data_dir, self._TRAIN_FOLDER), "split": "train"}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"filepath": os.path.join(data_dir, self._TEST_FOLDER), "split": "train"}, |
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), |
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] |
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def _generate_examples(self, filepath, split): |
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for fname in sorted(Path(filepath).rglob("*.txt")): |
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with open(fname, encoding="utf-8") as f: |
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for _id, line in enumerate(f): |
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chars = [] |
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char_types = [] |
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is_beginnings = [] |
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line = reduce(lambda a, kv: a.replace(*kv), self._USELESS_TAGS.items(), line) |
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splits = line.split("|") |
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for token in splits: |
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for i in range(len(token)): |
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chars.append(token[i]) |
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char_types.append(self._CHAR_TYPE_FLATTEN.get(token[i], "o")) |
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is_beginning = 1 if i == 0 else 0 |
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is_beginnings.append(is_beginning) |
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yield _id, { |
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"fname": fname.name, |
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"char": chars, |
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"char_type": char_types, |
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"is_beginning": is_beginnings if split == "train" else [0 for i in range(len(chars))], |
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} |
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