best2009 / best2009.py
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 file_idx, fname in enumerate(sorted(Path(filepath).rglob("*.txt"))):
with open(fname, encoding="utf-8") as f:
for line_idx, 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 f"{file_idx}_{line_idx}", {
"fname": fname.name,
"char": chars,
"char_type": char_types,
"is_beginning": is_beginnings if split == "train" else [0 for i in range(len(chars))],
}