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openlid.py

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+from pathlib import Path
+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 = """\
+@inproceedings{burchell-etal-2023-open,
+    title = "An Open Dataset and Model for Language Identification",
+    author = "Burchell, Laurie  and
+      Birch, Alexandra  and
+      Bogoychev, Nikolay  and
+      Heafield, Kenneth",
+    editor = "Rogers, Anna  and
+      Boyd-Graber, Jordan  and
+      Okazaki, Naoaki",
+    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
+    month = jul,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.acl-short.75",
+    doi = "10.18653/v1/2023.acl-short.75",
+    pages = "865--879",
+    abstract = "Language identification (LID) is a fundamental step in many natural language processing pipelines. However, current LID 
+    systems are far from perfect, particularly on lower-resource languages. We present a LID model which achieves a macro-average F1 
+    score of 0.93 and a false positive rate of 0.033{\%} across 201 languages, outperforming previous work. We achieve this by training 
+    on a curated dataset of monolingual data, which we audit manually to ensure reliability. We make both the model and the dataset 
+    available to the research community. Finally, we carry out detailed analysis into our model{'}s performance, both in comparison to 
+    existing open models and by language class.",
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["ace", "ban", "bjn", "bug", "ceb", "ilo", "ind", "jav", "kac", "khm", "lao", "min", "lus", "mya", "pag", "shn", "sun", "tgl", "tha", "vie", "war", "zsm"]  
+_DATASETNAME = "openlid"
+
+_DESCRIPTION = """\
+This is an open dataset for language identification covering 201 languages, which are curated and audited manually to 
+ensure high confidence in its data and language labels. 22 languages are native to Southeast Asia speakers.
+"""
+
+_HOMEPAGE = "https://github.com/laurieburchell/open-lid-dataset"
+_LICENSE = Licenses.GPL_3_0.value
+_URLS = {
+    _DATASETNAME: "https://data.statmt.org/lid/lid201-data.tsv.gz",
+}
+_SUPPORTED_TASKS = [Tasks.LANGUAGE_IDENTIFICATION]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+# 201 languages. Each element contains a code for the language, and script (e.g. wol_Latn = Wolof in Latin script)
+_TAGS = ['kbp_Latn', 'zul_Latn', 'zho_Hans', 'uig_Arab', 'smo_Latn', 'hrv_Latn', 'tgk_Cyrl', 'guj_Gujr', 'azj_Latn', 'mai_Deva', 'bul_Cyrl', 'hne_Deva', 'wol_Latn', 'ind_Latn', 'lit_Latn', 'epo_Latn', 'prs_Arab', 'kmr_Latn', 'fao_Latn', 'swh_Latn', 'slk_Latn', 'srp_Cyrl', 'bod_Tibt', 'eus_Latn', 'tir_Ethi', 'tam_Taml', 'kas_Deva', 'glg_Latn', 'crh_Latn', 'kon_Latn', 'ayr_Latn', 'por_Latn', 'ben_Beng', 'zho_Hant', 'bug_Latn', 'umb_Latn', 'tzm_Tfng', 'kan_Knda', 'tgl_Latn', 'luo_Latn', 'lij_Latn', 'hun_Latn', 'kin_Latn', 'hat_Latn', 'sag_Latn', 'khm_Khmr', 'heb_Hebr', 'hye_Armn', 'fuv_Latn', 'cjk_Latn', 'ckb_Arab', 'srd_Latn', 'cat_Latn', 'dan_Latn', 'lao_Laoo', 'fra_Latn', 'kam_Latn', 'aeb_Arab', 'ydd_Hebr', 'afr_Latn', 'khk_Cyrl', 'lug_Latn', 'lin_Latn', 'nya_Latn', 'tsn_Latn', 'dzo_Tibt', 'min_Latn', 'war_Latn', 'rus_Cyrl', 'nob_Latn', 'tpi_Latn', 'mlt_Latn', 'mni_Beng', 'ilo_Latn', 'amh_Ethi', 'taq_Latn', 'acq_Arab', 'gaz_Latn', 'ltg_Latn', 'kac_Latn', 'ibo_Latn', 'gle_Latn', 'mya_Mymr', 'grn_Latn', 'kik_Latn', 'jav_Latn', 'awa_Deva', 'ars_Arab', 'swe_Latn', 'uzn_Latn', 'mos_Latn', 'lus_Latn', 'mal_Mlym', 'ita_Latn', 'dik_Latn', 'ewe_Latn', 'sat_Olck', 'pan_Guru', 'est_Latn', 'kab_Latn', 'bam_Latn', 'pag_Latn', 'isl_Latn', 'eng_Latn', 'fon_Latn', 'kas_Arab', 'asm_Beng', 'lim_Latn', 'bjn_Arab', 'taq_Tfng', 'deu_Latn', 'pbt_Arab', 'pap_Latn', 'quy_Latn', 'kea_Latn', 'npi_Deva', 'xho_Latn', 'shn_Mymr', 'nso_Latn', 'urd_Arab', 'bos_Latn', 'ron_Latn', 'fur_Latn', 'gla_Latn', 'nus_Latn', 'ltz_Latn', 'arz_Arab', 'bem_Latn', 'fin_Latn', 'kir_Cyrl', 'tha_Thai', 'mag_Deva', 'azb_Arab', 'tel_Telu', 'ell_Grek', 'sot_Latn', 'spa_Latn', 'vie_Latn', 'yor_Latn', 'ceb_Latn', 'vec_Latn', 'sin_Sinh', 'pol_Latn', 'als_Latn', 'lmo_Latn', 'scn_Latn', 'ces_Latn', 'fij_Latn', 'run_Latn', 'som_Latn', 'mkd_Cyrl', 'mar_Deva', 'ast_Latn', 'san_Deva', 'ary_Arab', 'twi_Latn', 'acm_Arab', 'nno_Latn', 'zsm_Latn', 'mri_Latn', 'kor_Hang', 'sna_Latn', 'pes_Arab', 'ace_Latn', 'bak_Cyrl', 'kat_Geor', 'tur_Latn', 'jpn_Jpan', 'arb_Arab', 'ukr_Cyrl', 'yue_Hant', 'kaz_Cyrl', 'hau_Latn', 'nld_Latn', 'oci_Latn', 'apc_Arab', 'tum_Latn', 'ace_Arab', 'dyu_Latn', 'knc_Latn', 'knc_Arab', 'kmb_Latn', 'bel_Cyrl', 'slv_Latn', 'lvs_Latn', 'bho_Deva', 'tuk_Latn', 'snd_Arab', 'sun_Latn', 'lua_Latn', 'ajp_Arab', 'hin_Deva', 'tso_Latn', 'tat_Cyrl', 'cym_Latn', 'ory_Orya', 'ban_Latn', 'szl_Latn', 'plt_Latn', 'bjn_Latn', 'ssw_Latn']
+
+
+class OpenLID(datasets.GeneratorBasedBuilder):
+    """This is an open dataset for language identification covering 201 languages. 22 languages are native to Southeast Asia speakers."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name="openlid_source",
+            version=SOURCE_VERSION,
+            description="OpenLID source schema",
+            schema="source",
+            subset_id="openlid",
+        ),
+        SEACrowdConfig(
+            name="openlid_seacrowd_text",
+            version=SEACROWD_VERSION,
+            description="OpenLID Nusantara schema",
+            schema="seacrowd_text",
+            subset_id="openlid",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "openlid_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features({"id": datasets.Value("string"), "text": datasets.Value("string"), "label": datasets.Value("string"), "source": datasets.Value("string")})
+        elif self.config.schema == "seacrowd_text":
+            features = schemas.text_features(_TAGS)
+
+        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."""
+        # Dataset does not have predetermined split, putting all as TRAIN
+        urls = _URLS[_DATASETNAME]
+        filepath = Path(dl_manager.download_and_extract(urls))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": filepath,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        # Dataset does not have id, using row index as id
+        with open(filepath) as f:
+            lines = f.readlines()
+
+        if self.config.schema == "source":
+            for _id, line in enumerate(lines):
+                line = line.split("\t")
+                ex = {
+                    "id": str(_id),
+                    "text": line[0],
+                    "label": line[1],
+                    "source": line[2].strip(),
+                }
+                yield _id, ex
+
+        elif self.config.schema == "seacrowd_text":
+            for _id, line in enumerate(lines):
+                line = line.split("\t")
+                ex = {
+                    "id": str(_id),
+                    "text": line[0],
+                    "label": line[1],
+                }
+                yield _id, ex
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")