Datasets:
best2009

	from __future__ import absolute_import, division, print_function

	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 = {"<NE>": "", "</NE>": "", "<AB>": "", "</AB>": ""}
	# 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))],
	}