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from pathlib import Path
from typing import Dict, List, Tuple
import datasets
from datasets.download.download_manager import DownloadManager
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Licenses, Tasks
_CITATION = """
@article{hidayatullah2023corpus,
title={Corpus creation and language identification for code-mixed Indonesian-Javanese-English Tweets},
author={Hidayatullah, Ahmad Fathan and Apong, Rosyzie Anna and Lai, Daphne TC and Qazi, Atika},
journal={PeerJ Computer Science},
volume={9},
pages={e1312},
year={2023},
publisher={PeerJ Inc.}
}
"""
_LOCAL = False
_LANGUAGES = ["ind", "jav", "eng"]
_DATASETNAME = "ijelid"
_DESCRIPTION = """\
This is a code-mixed Indonesian-Javanese-English dataset for token-level
language identification. We named this dataset as IJELID
(Indonesian-Javanese-English Language Identification). This dataset contains
tweets that have been tokenized with the corresponding token and its language
label. There are seven language labels in the dataset, namely: ID (Indonesian)JV
(Javanese), EN (English), MIX_ID_EN (mixed Indonesian-English), MIX_ID_JV (mixed
Indonesian-Javanese), MIX_JV_EN (mixed Javanese-English), OTH (Other).
"""
_HOMEPAGE = "https://github.com/fathanick/Code-mixed-Indonesian-Javanese-English-Twitter-Data"
_LICENSE = Licenses.CC_BY_NC_SA_4_0.value
_URLS = {
"train": "https://raw.githubusercontent.com/fathanick/Code-mixed-Indonesian-Javanese-English-Twitter-Data/main/train.tsv",
"dev": "https://raw.githubusercontent.com/fathanick/Code-mixed-Indonesian-Javanese-English-Twitter-Data/main/val.tsv",
"test": "https://raw.githubusercontent.com/fathanick/Code-mixed-Indonesian-Javanese-English-Twitter-Data/main/test.tsv",
}
_SUPPORTED_TASKS = [Tasks.TOKEN_LEVEL_LANGUAGE_IDENTIFICATION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class IJELIDDataset(datasets.GeneratorBasedBuilder):
"""IJELID dataset from https://github.com/fathanick/Code-mixed-Indonesian-Javanese-English-Twitter-Data"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
SEACROWD_SCHEMA_NAME = "seq_label"
LABEL_CLASSES = ["ID", "JV", "EN", "MIX_ID_EN", "MIX_ID_JV", "MIX_JV_EN", "OTH"]
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_source",
version=SOURCE_VERSION,
description=f"{_DATASETNAME} source schema",
schema="source",
subset_id=_DATASETNAME,
),
SEACrowdConfig(
name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
version=SEACROWD_VERSION,
description=f"{_DATASETNAME} SEACrowd schema",
schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
subset_id=_DATASETNAME,
),
]
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
def _info(self) -> datasets.DatasetInfo:
# No specific schema for the source, so for consistency,
# I will use the same schema with SEACrowd
features = schemas.seq_label_features(self.LABEL_CLASSES)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
data_files = {
"train": Path(dl_manager.download_and_extract(_URLS["train"])),
"dev": Path(dl_manager.download_and_extract(_URLS["dev"])),
"test": Path(dl_manager.download_and_extract(_URLS["test"])),
}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": data_files["train"], "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": data_files["dev"], "split": "dev"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": data_files["test"], "split": "test"},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yield examples as (key, example) tuples"""
with open(filepath, encoding="utf-8") as f:
guid = 0
tokens = []
labels = []
for line in f:
if line == "" or line == "\n":
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"labels": labels,
}
guid += 1
tokens = []
labels = []
else:
# IJELID TSV are separated by \t
token, label = line.split("\t")
tokens.append(token)
labels.append(label.rstrip())
# Last example
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"labels": labels,
}
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