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from typing import Dict, List, Tuple
import datasets
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Licenses, Tasks
_CITATION = """\
@article{DBLP:journals/corr/abs-2204-02658,
author = {Yingwen Fu and
Jinyi Chen and
Nankai Lin and
Xixuan Huang and
Xin Ying Qiu and
Shengyi Jiang},
title = {Yunshan Cup 2020: Overview of the Part-of-Speech Tagging Task for
Low-resourced Languages},
journal = {CoRR},
volume = {abs/2204.02658},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2204.02658},
doi = {10.48550/arXiv.2204.02658},
eprinttype = {arXiv},
eprint = {2204.02658},
timestamp = {Tue, 12 Apr 2022 18:42:14 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2204-02658.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
_DATASETNAME = "yunshan_cup_2020"
_DESCRIPTION = """\
Lao POS dataset containing 11,000 sentences was released as part of Yunshan-Cup-2020 evaluation track.
"""
_HOMEPAGE = "https://github.com/GKLMIP/Yunshan-Cup-2020"
_LOCAL = False
_LANGUAGES = ["lao"]
_LICENSE = Licenses.UNKNOWN.value # example: Licenses.MIT.value, Licenses.CC_BY_NC_SA_4_0.value, Licenses.UNLICENSE.value, Licenses.UNKNOWN.value
_URLS = {
"train": "https://raw.githubusercontent.com/GKLMIP/Yunshan-Cup-2020/main/train.txt",
"val": "https://raw.githubusercontent.com/GKLMIP/Yunshan-Cup-2020/main/dev.txt",
"test": "https://raw.githubusercontent.com/GKLMIP/Yunshan-Cup-2020/main/test.txt",
}
_SUPPORTED_TASKS = [Tasks.POS_TAGGING] # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class YunshanCup2020Dataset(datasets.GeneratorBasedBuilder):
"""Lao POS dataset containing 11,000 sentences was released as part of Yunshan-Cup-2020 evaluation track."""
class_labels = ["IAC", "COJ", "ONM", "PRE", "PRS", "V", "DBQ", "IBQ", "FIX", "N", "ADJ", "DMN", "IAQ", "CLF", "PRA", "DAN", "NEG", "NTR", "REL", "PVA", "TTL", "DAQ", "PRN", "ADV", "PUNCT", "CNM"]
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_source",
version=SOURCE_VERSION,
description="yunshan_cup_2020 source schema",
schema="source",
subset_id="yunshan_cup_2020",
),
SEACrowdConfig(
name=f"{_DATASETNAME}_seacrowd_seq_label",
version=SEACROWD_VERSION,
description="yunshan_cup_2020 SEACrowd schema",
schema="seacrowd_seq_label",
subset_id="yunshan_cup_2020",
),
]
DEFAULT_CONFIG_NAME = "yunshan_cup_2020_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"index": datasets.Value("string"),
"tokens": [datasets.Value("string")],
"pos_tags": [datasets.Value("string")],
}
)
elif self.config.schema == "seacrowd_seq_label":
features = schemas.seq_label_features(self.class_labels)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
path_dict = dl_manager.download_and_extract(_URLS)
train_path, val_path, test_path = path_dict["train"], path_dict["val"], path_dict["test"]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": train_path,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": test_path
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": val_path,
},
),
]
def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
df = load_postagging_data(filepath)
if self.config.schema == "source":
for i, row in enumerate(df):
ex = {
"index": str(i),
"tokens": row["sentence"],
"pos_tags": row["label"],
}
yield i, ex
elif self.config.schema == "seacrowd_seq_label":
for i, row in enumerate(df):
ex = {
"id": str(i),
"tokens": row["sentence"],
"labels": row["label"],
}
yield i, ex
def load_postagging_data(file_path):
data = open(file_path, "r").readlines()
dataset = []
sentence, seq_label = [], []
for line in data:
if len(line.strip()) > 0:
token, label = " ", ""
if len(line.strip().split(" ")) < 2:
label = line.strip()
else:
token, label = line[:-1].split(" ")
sentence.append(token)
seq_label.append(label)
else:
dataset.append({"sentence": sentence, "label": seq_label})
sentence = []
seq_label = []
return dataset
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