Datasets:
Tasks:
Token Classification
Languages:
Thai
Multilinguality:
monolingual
Size Categories:
n<1K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
extended|wisesight_sentiment
Tags:
word-tokenization
License:
import datasets | |
_CITATION = """\ | |
@software{bact_2019_3457447, | |
author = {Suriyawongkul, Arthit and | |
Chuangsuwanich, Ekapol and | |
Chormai, Pattarawat and | |
Polpanumas, Charin}, | |
title = {PyThaiNLP/wisesight-sentiment: First release}, | |
month = sep, | |
year = 2019, | |
publisher = {Zenodo}, | |
version = {v1.0}, | |
doi = {10.5281/zenodo.3457447}, | |
url = {https://doi.org/10.5281/zenodo.3457447} | |
} | |
""" | |
_LICENSE = "CC0" | |
_DESCRIPTION = """\ | |
`wisesight1000` contains Thai social media texts randomly drawn from the full `wisesight-sentiment`, tokenized by human annotators. | |
Out of the labels `neg` (negative), `neu` (neutral), `pos` (positive), `q` (question), 250 samples each. Some texts are removed because | |
they look like spam.Because these samples are representative of real world content, we believe having these annotaed samples will allow | |
the community to robustly evaluate tokenization algorithms. | |
""" | |
class Wisesight1000Config(datasets.BuilderConfig): | |
def __init__(self, **kwargs): | |
"""BuilderConfig | |
Args: | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
super(Wisesight1000Config, self).__init__(**kwargs) | |
class Wisesight1000(datasets.GeneratorBasedBuilder): | |
_DOWNLOAD_URL = "https://raw.githubusercontent.com/PyThaiNLP/wisesight-sentiment/master/word-tokenization/wisesight-1000-samples-tokenised.label" | |
# 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 = [ | |
Wisesight1000Config( | |
name="wisesight1000", | |
version=datasets.Version("1.0.0"), | |
description="993 word-annotated social media messages sampled from `wisesight-sentiment`", | |
), | |
] | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"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://github.com/PyThaiNLP/wisesight-sentiment", | |
citation=_CITATION, | |
license=_LICENSE, | |
) | |
def _split_generators(self, dl_manager): | |
data_path = dl_manager.download_and_extract(self._DOWNLOAD_URL) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={"filepath": data_path}, | |
), | |
] | |
def _generate_examples(self, filepath): | |
with open(filepath, encoding="utf-8") as f: | |
for _id, line in enumerate(f): | |
chars = [] | |
char_types = [] | |
is_beginnings = [] | |
# 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, { | |
"char": chars, | |
"char_type": char_types, | |
"is_beginning": is_beginnings, | |
} | |