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