File size: 4,559 Bytes
625705b c6f339a 625705b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
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):
# Source data:"https://raw.githubusercontent.com/PyThaiNLP/wisesight-sentiment/master/word-tokenization/wisesight-1000-samples-tokenised.label"
_DOWNLOAD_URL = "data/wisesight-1000-samples-tokenised.label.gz"
# 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,
}
|