Upload wili_2018.py with huggingface_hub

wili_2018.py

@@ -0,0 +1,359 @@
+from pathlib import Path
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """
+@article{thoma2018wili,
+  title={The WiLI benchmark dataset for written language identification},
+  author={Thoma, Martin},
+  journal={arXiv preprint arXiv:1801.07779},
+  year={2018}
+}
+"""
+
+_DATASETNAME = "wili_2018"
+
+_DESCRIPTION = """
+WiLI-2018 is a Wikipedia language identification benchmark dataset. It contains 235000 paragraphs from 235 languages.
+The dataset is balanced, and a train-test split is provided.
+"""
+
+_HOMEPAGE = "https://zenodo.org/records/841984"
+
+_LANGUAGES = ["nrm", "jav", "min", "lao", "mya", "pag", "ind", "cbk", "tet", "tha", "ceb", "tgl", "bjn", "bcl", "vie"]
+
+_LICENSE = Licenses.ODBL.value
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: {"train": "https://drive.google.com/uc?export=download&id=1ZzlIQvw1KNBG97QQCfdatvVrrbeLaM1u", "test": "https://drive.google.com/uc?export=download&id=1Xx4kFc1Xdzz8AhDasxZ0cSa-a35EQSDZ"},
+}
+
+_SUPPORTED_TASKS = [Tasks.LANGUAGE_IDENTIFICATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+_CLASSES = [
+    "cdo",
+    "glk",
+    "jam",
+    "lug",
+    "san",
+    "rue",
+    "wol",
+    "new",
+    "mwl",
+    "bre",
+    "ara",
+    "hye",
+    "xmf",
+    "ext",
+    "cor",
+    "yor",
+    "div",
+    "asm",
+    "lat",
+    "cym",
+    "hif",
+    "ace",
+    "kbd",
+    "tgk",
+    "rus",
+    "nso",
+    "mya",
+    "msa",
+    "ava",
+    "cbk",
+    "urd",
+    "deu",
+    "swa",
+    "pus",
+    "bxr",
+    "udm",
+    "csb",
+    "yid",
+    "vro",
+    "por",
+    "pdc",
+    "eng",
+    "tha",
+    "hat",
+    "lmo",
+    "pag",
+    "jav",
+    "chv",
+    "nan",
+    "sco",
+    "kat",
+    "bho",
+    "bos",
+    "kok",
+    "oss",
+    "mri",
+    "fry",
+    "cat",
+    "azb",
+    "kin",
+    "hin",
+    "sna",
+    "dan",
+    "egl",
+    "mkd",
+    "ron",
+    "bul",
+    "hrv",
+    "som",
+    "pam",
+    "nav",
+    "ksh",
+    "nci",
+    "khm",
+    "sgs",
+    "srn",
+    "bar",
+    "cos",
+    "ckb",
+    "pfl",
+    "arz",
+    "roa-tara",
+    "fra",
+    "mai",
+    "zh-yue",
+    "guj",
+    "fin",
+    "kir",
+    "vol",
+    "hau",
+    "afr",
+    "uig",
+    "lao",
+    "swe",
+    "slv",
+    "kor",
+    "szl",
+    "srp",
+    "dty",
+    "nrm",
+    "dsb",
+    "ind",
+    "wln",
+    "pnb",
+    "ukr",
+    "bpy",
+    "vie",
+    "tur",
+    "aym",
+    "lit",
+    "zea",
+    "pol",
+    "est",
+    "scn",
+    "vls",
+    "stq",
+    "gag",
+    "grn",
+    "kaz",
+    "ben",
+    "pcd",
+    "bjn",
+    "krc",
+    "amh",
+    "diq",
+    "ltz",
+    "ita",
+    "kab",
+    "bel",
+    "ang",
+    "mhr",
+    "che",
+    "koi",
+    "glv",
+    "ido",
+    "fao",
+    "bak",
+    "isl",
+    "bcl",
+    "tet",
+    "jpn",
+    "kur",
+    "map-bms",
+    "tyv",
+    "olo",
+    "arg",
+    "ori",
+    "lim",
+    "tel",
+    "lin",
+    "roh",
+    "sqi",
+    "xho",
+    "mlg",
+    "fas",
+    "hbs",
+    "tam",
+    "aze",
+    "lad",
+    "nob",
+    "sin",
+    "gla",
+    "nap",
+    "snd",
+    "ast",
+    "mal",
+    "mdf",
+    "tsn",
+    "nds",
+    "tgl",
+    "nno",
+    "sun",
+    "lzh",
+    "jbo",
+    "crh",
+    "pap",
+    "oci",
+    "hak",
+    "uzb",
+    "zho",
+    "hsb",
+    "sme",
+    "mlt",
+    "vep",
+    "lez",
+    "nld",
+    "nds-nl",
+    "mrj",
+    "spa",
+    "ceb",
+    "ina",
+    "heb",
+    "hun",
+    "que",
+    "kaa",
+    "mar",
+    "vec",
+    "frp",
+    "ell",
+    "sah",
+    "eus",
+    "ces",
+    "slk",
+    "chr",
+    "lij",
+    "nep",
+    "srd",
+    "ilo",
+    "be-tarask",
+    "bod",
+    "orm",
+    "war",
+    "glg",
+    "mon",
+    "gle",
+    "min",
+    "ibo",
+    "ile",
+    "epo",
+    "lav",
+    "lrc",
+    "als",
+    "mzn",
+    "rup",
+    "fur",
+    "tat",
+    "myv",
+    "pan",
+    "ton",
+    "kom",
+    "wuu",
+    "tcy",
+    "tuk",
+    "kan",
+    "ltg",
+]
+
+
+class Wili2018Dataset(datasets.GeneratorBasedBuilder):
+    """A benchmark dataset for language identification and contains 235000 paragraphs of 235 languages."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    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_text",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_text",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=_CLASSES),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_text":
+            features = schemas.text_features(_CLASSES)
+
+        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."""
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_dir, "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": data_dir, "split": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> tuple[int, dict]:
+        if self.config.schema == "source":
+            with open(filepath[split], encoding="utf-8") as f:
+                for i, line in enumerate(f):
+                    text, label = line.rsplit(",", 1)
+                    text = text.strip('"')
+                    label = int(label.strip())
+                    yield i, {"sentence": text, "label": _CLASSES[label - 1]}
+
+        elif self.config.schema == "seacrowd_text":
+            with open(filepath[split], encoding="utf-8") as f:
+                for i, line in enumerate(f):
+                    text, label = line.rsplit(",", 1)
+                    text = text.strip('"')
+                    label = int(label.strip())
+                    yield i, {"id": str(i), "text": text, "label": _CLASSES[label - 1]}