File size: 3,127 Bytes
34bced1 d597db4 34bced1 d597db4 34bced1 8ca5f84 565cd29 34bced1 8ca5f84 d597db4 8ca5f84 34bced1 f532880 b1ee066 34bced1 8ca5f84 565cd29 34bced1 565cd29 34bced1 7abf816 565cd29 7abf816 565cd29 7abf816 34bced1 22e37f8 34bced1 9ee7640 ad00e52 8d4b1d8 ad00e52 8d4b1d8 ad00e52 |
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 |
import os
import datasets
_DESCRIPTION = """\
UTS_WTK
"""
_CITATION = """\
"""
_BASE_URL = "https://huggingface.co/datasets/undertheseanlp/UTS_WTK/raw/main/data/"
TRAIN_FILE = "train.txt"
DEV_FILE = "validation.txt"
TEST_FILE = "test.txt"
class UTSWTKConfig(datasets.BuilderConfig):
"""BuilderConfig"""
def __init__(self, **kwargs):
super(UTSWTKConfig, self).__init__(**kwargs)
class UTSWTK(datasets.GeneratorBasedBuilder):
"""UTS Word Tokenize datasets"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
# UTSWTKConfig(
# name="small", version=VERSION, description="UTS_WTK Small"),
UTSWTKConfig(
name="base", version=VERSION, description="UTS_WTK Base"),
# UTSWTKConfig(
# name="large", version=VERSION, description="UTS_WTK Large"),
]
BUILDER_CONFIG_CLASS = UTSWTKConfig
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"tags": datasets.Sequence(
datasets.features.ClassLabel(names=["B-W", "I-W"])
),
}
),
supervised_keys=None,
homepage=None,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
subset_folder = self.config.name
train_file = dl_manager.download(os.path.join(_BASE_URL, subset_folder, TRAIN_FILE))
dev_file = dl_manager.download(os.path.join(_BASE_URL, subset_folder, DEV_FILE))
test_file = dl_manager.download(os.path.join(_BASE_URL, subset_folder, TEST_FILE))
splits = [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": train_file}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": dev_file},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": test_file}
),
]
return splits
def _generate_examples(self, filepath):
with open(filepath, encoding="utf-8") as f:
guid = 0
tokens = []
tags = []
for line in f:
if line == "" or line == "\n":
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"tags": tags,
}
guid += 1
tokens = []
tags = []
else:
# each line is tab separated
splits = line.strip().split("\t")
tokens.append(splits[0])
tags.append(splits[1])
|