Datasets:

SEACrowd
/

unimorph

+from pathlib import Path
+from typing import Any, 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 = """
+@misc{batsuren2022unimorph,
+      title={UniMorph 4.0: Universal Morphology},
+      author={
+        Khuyagbaatar Batsuren and Omer Goldman and Salam Khalifa and Nizar
+        Habash and Witold Kieraś and Gábor Bella and Brian Leonard and Garrett
+        Nicolai and Kyle Gorman and Yustinus Ghanggo Ate and Maria Ryskina and
+        Sabrina J. Mielke and Elena Budianskaya and Charbel El-Khaissi and Tiago
+        Pimentel and Michael Gasser and William Lane and Mohit Raj and Matt
+        Coler and Jaime Rafael Montoya Samame and Delio Siticonatzi Camaiteri
+        and Benoît Sagot and Esaú Zumaeta Rojas and Didier López Francis and
+        Arturo Oncevay and Juan López Bautista and Gema Celeste Silva Villegas
+        and Lucas Torroba Hennigen and Adam Ek and David Guriel and Peter Dirix
+        and Jean-Philippe Bernardy and Andrey Scherbakov and Aziyana Bayyr-ool
+        and Antonios Anastasopoulos and Roberto Zariquiey and Karina Sheifer and
+        Sofya Ganieva and Hilaria Cruz and Ritván Karahóǧa and Stella
+        Markantonatou and George Pavlidis and Matvey Plugaryov and Elena
+        Klyachko and Ali Salehi and Candy Angulo and Jatayu Baxi and Andrew
+        Krizhanovsky and Natalia Krizhanovskaya and Elizabeth Salesky and Clara
+        Vania and Sardana Ivanova and Jennifer White and Rowan Hall Maudslay and
+        Josef Valvoda and Ran Zmigrod and Paula Czarnowska and Irene Nikkarinen
+        and Aelita Salchak and Brijesh Bhatt and Christopher Straughn and Zoey
+        Liu and Jonathan North Washington and Yuval Pinter and Duygu Ataman and
+        Marcin Wolinski and Totok Suhardijanto and Anna Yablonskaya and Niklas
+        Stoehr and Hossep Dolatian and Zahroh Nuriah and Shyam Ratan and Francis
+        M. Tyers and Edoardo M. Ponti and Grant Aiton and Aryaman Arora and
+        Richard J. Hatcher and Ritesh Kumar and Jeremiah Young and Daria
+        Rodionova and Anastasia Yemelina and Taras Andrushko and Igor Marchenko
+        and Polina Mashkovtseva and Alexandra Serova and Emily Prud'hommeaux and
+        Maria Nepomniashchaya and Fausto Giunchiglia and Eleanor Chodroff and
+        Mans Hulden and Miikka Silfverberg and Arya D. McCarthy and David
+        Yarowsky and Ryan Cotterell and Reut Tsarfaty and Ekaterina Vylomova},
+      year={2022},
+      eprint={2205.03608},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+_LOCAL = False
+_LANGUAGES = ["ind", "kod", "ceb", "hil", "tgl"]
+_DATASETNAME = "unimorph"
+_DESCRIPTION = """\
+The Universal Morphology (UniMorph) project is a collaborative effort providing
+broad-coverage instantiated normalized morphological inflection tables for
+undreds of diverse world languages. The project comprises two major thrusts: a
+language-independent feature schema for rich morphological annotation, and a
+type-level resource of annotated data in diverse languages realizing that
+schema. 5 Austronesian languages spoken in Southeast Asia, consisting 2
+Malayo-Polynesian languages and 3 Greater Central Philippine languages, become
+the part of UniMorph 4.0 release.
+"""
+_HOMEPAGE = "https://unimorph.github.io"
+_LICENSE = Licenses.CC_BY_SA_3_0.value
+_URL = "https://raw.githubusercontent.com/unimorph/"
+_SUPPORTED_TASKS = [Tasks.MORPHOLOGICAL_INFLECTION]
+_SOURCE_VERSION = "4.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+class UnimorphDataset(datasets.GeneratorBasedBuilder):
+    """Unimorh 4.0 dataset by Batsuren et al., (2022)"""
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+    SEACROWD_SCHEMA_NAME = "pairs_multi"
+    dataset_names = sorted([f"{_DATASETNAME}_{lang}" for lang in _LANGUAGES])
+    BUILDER_CONFIGS = []
+    for name in dataset_names:
+        source_config = SEACrowdConfig(
+            name=f"{name}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=name,
+        )
+        BUILDER_CONFIGS.append(source_config)
+        seacrowd_config = SEACrowdConfig(
+            name=f"{name}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=name,
+        )
+        BUILDER_CONFIGS.append(seacrowd_config)
+    # Add configuration that allows loading all datasets at once.
+    BUILDER_CONFIGS.extend(
+        [
+            # unimorph_source
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_source",
+                version=SOURCE_VERSION,
+                description=f"{_DATASETNAME} source schema (all)",
+                schema="source",
+                subset_id=_DATASETNAME,
+            ),
+            # unimorph_seacrowd_pairs
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+                version=SEACROWD_VERSION,
+                description=f"{_DATASETNAME} SEACrowd schema (all)",
+                schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+                subset_id=_DATASETNAME,
+            ),
+        ]
+    )
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+    # https://huggingface.co/datasets/universal_morphologies/blob/main/universal_morphologies.py
+    CLASS_CATEGORIES = {
+        "Aktionsart": ["STAT", "DYN", "TEL", "ATEL", "PCT", "DUR", "ACH", "ACCMP", "SEMEL", "ACTY"],
+        "Animacy": ["ANIM", "INAN", "HUM", "NHUM"],
+        "Argument_Marking": [
+            "ARGNO1S",
+            "ARGNO2S",
+            "ARGNO3S",
+            "ARGNO1P",
+            "ARGNO2P",
+            "ARGNO3P",
+            "ARGAC1S",
+            "ARGAC2S",
+            "ARGAC3S",
+            "ARGAC1P",
+            "ARGAC2P",
+            "ARGAC3P",
+            "ARGAB1S",
+            "ARGAB2S",
+            "ARGAB3S",
+            "ARGAB1P",
+            "ARGAB2P",
+            "ARGAB3P",
+            "ARGER1S",
+            "ARGER2S",
+            "ARGER3S",
+            "ARGER1P",
+            "ARGER2P",
+            "ARGER3P",
+            "ARGDA1S",
+            "ARGDA2S",
+            "ARGDA3S",
+            "ARGDA1P",
+            "ARGDA2P",
+            "ARGDA3P",
+            "ARGBE1S",
+            "ARGBE2S",
+            "ARGBE3S",
+            "ARGBE1P",
+            "ARGBE2P",
+            "ARGBE3P",
+        ],
+        "Aspect": ["IPFV", "PFV", "PRF", "PROG", "PROSP", "ITER", "HAB"],
+        "Case": [
+            "NOM",
+            "ACC",
+            "ERG",
+            "ABS",
+            "NOMS",
+            "DAT",
+            "BEN",
+            "PRP",
+            "GEN",
+            "REL",
+            "PRT",
+            "INS",
+            "COM",
+            "VOC",
+            "COMPV",
+            "EQTV",
+            "PRIV",
+            "PROPR",
+            "AVR",
+            "FRML",
+            "TRANS",
+            "BYWAY",
+            "INTER",
+            "AT",
+            "POST",
+            "IN",
+            "CIRC",
+            "ANTE",
+            "APUD",
+            "ON",
+            "ONHR",
+            "ONVR",
+            "SUB",
+            "REM",
+            "PROXM",
+            "ESS",
+            "ALL",
+            "ABL",
+            "APPRX",
+            "TERM",
+        ],
+        "Comparison": ["CMPR", "SPRL", "AB", "RL", "EQT"],
+        "Definiteness": ["DEF", "INDF", "SPEC", "NSPEC"],
+        "Deixis": ["PROX", "MED", "REMT", "REF1", "REF2", "NOREF", "PHOR", "VIS", "NVIS", "ABV", "EVEN", "BEL"],
+        "Evidentiality": ["FH", "DRCT", "SEN", "VISU", "NVSEN", "AUD", "NFH", "QUOT", "RPRT", "HRSY", "INFER", "ASSUM"],
+        "Finiteness": ["FIN", "NFIN"],
+        "Gender": [
+            "MASC",
+            "FEM",
+            "NEUT",
+            "NAKH1",
+            "NAKH2",
+            "NAKH3",
+            "NAKH4",
+            "NAKH5",
+            "NAKH6",
+            "NAKH7",
+            "NAKH8",
+            "BANTU1",
+            "BANTU2",
+            "BANTU3",
+            "BANTU4",
+            "BANTU5",
+            "BANTU6",
+            "BANTU7",
+            "BANTU8",
+            "BANTU9",
+            "BANTU10",
+            "BANTU11",
+            "BANTU12",
+            "BANTU13",
+            "BANTU14",
+            "BANTU15",
+            "BANTU16",
+            "BANTU17",
+            "BANTU18",
+            "BANTU19",
+            "BANTU20",
+            "BANTU21",
+            "BANTU22",
+            "BANTU23",
+        ],
+        "Information_Structure": ["TOP", "FOC"],
+        "Interrogativity": ["DECL", "INT"],
+        "Language_Specific": [
+            "LGSPEC1",
+            "LGSPEC2",
+            "LGSPEC3",
+            "LGSPEC4",
+            "LGSPEC5",
+            "LGSPEC6",
+            "LGSPEC7",
+            "LGSPEC8",
+            "LGSPEC9",
+            "LGSPEC10",
+        ],
+        "Mood": [
+            "IND",
+            "SBJV",
+            "REAL",
+            "IRR",
+            "AUPRP",
+            "AUNPRP",
+            "IMP",
+            "COND",
+            "PURP",
+            "INTEN",
+            "POT",
+            "LKLY",
+            "ADM",
+            "OBLIG",
+            "DEB",
+            "PERM",
+            "DED",
+            "SIM",
+            "OPT",
+        ],
+        "Number": ["SG", "PL", "GRPL", "DU", "TRI", "PAUC", "GRPAUC", "INVN"],
+        "Part_Of_Speech": [
+            "N",
+            "PROPN",
+            "ADJ",
+            "PRO",
+            "CLF",
+            "ART",
+            "DET",
+            "V",
+            "ADV",
+            "AUX",
+            "V.PTCP",
+            "V.MSDR",
+            "V.CVB",
+            "ADP",
+            "COMP",
+            "CONJ",
+            "NUM",
+            "PART",
+            "INTJ",
+        ],
+        "Person": ["0", "1", "2", "3", "4", "INCL", "EXCL", "PRX", "OBV"],
+        "Polarity": ["POS", "NEG"],
+        "Politeness": [
+            "INFM",
+            "FORM",
+            "ELEV",
+            "HUMB",
+            "POL",
+            "AVOID",
+            "LOW",
+            "HIGH",
+            "STELEV",
+            "STSUPR",
+            "LIT",
+            "FOREG",
+            "COL",
+        ],
+        "Possession": [
+            "ALN",
+            "NALN",
+            "PSS1S",
+            "PSS2S",
+            "PSS2SF",
+            "PSS2SM",
+            "PSS2SINFM",
+            "PSS2SFORM",
+            "PSS3S",
+            "PSS3SF",
+            "PSS3SM",
+            "PSS1D",
+            "PSS1DI",
+            "PSS1DE",
+            "PSS2D",
+            "PSS2DM",
+            "PSS2DF",
+            "PSS3D",
+            "PSS3DF",
+            "PSS3DM",
+            "PSS1P",
+            "PSS1PI",
+            "PSS1PE",
+            "PSS2P",
+            "PSS2PF",
+            "PSS2PM",
+            "PSS3PF",
+            "PSS3PM",
+        ],
+        "Switch_Reference": ["SS", "SSADV", "DS", "DSADV", "OR", "SIMMA", "SEQMA", "LOG"],
+        "Tense": ["PRS", "PST", "FUT", "IMMED", "HOD", "1DAY", "RCT", "RMT"],
+        "Valency": ["IMPRS", "INTR", "TR", "DITR", "REFL", "RECP", "CAUS", "APPL"],
+        "Voice": ["ACT", "MID", "PASS", "ANTIP", "DIR", "INV", "AGFOC", "PFOC", "LFOC", "BFOC", "ACFOC", "IFOC", "CFOC"],
+    }
+    TAG_TO_CAT = dict([(tag, cat) for cat, tags in CLASS_CATEGORIES.items() for tag in tags])
+    CLASS_LABELS = [feat for _, category in CLASS_CATEGORIES.items() for feat in category]
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "lemma": datasets.Value("string"),
+                    "forms": datasets.Sequence(
+                        dict(
+                            [("word", datasets.Value("string"))]
+                            + [(cat, datasets.Sequence(datasets.ClassLabel(names=tasks))) for cat, tasks in self.CLASS_CATEGORIES.items()]
+                            + [("Other", datasets.Sequence(datasets.Value("string")))]  # for misspecified tags
+                        )
+                    ),
+                }
+            )
+        if self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            all_features = [feat for _, category in self.CLASS_CATEGORIES.items() for feat in category]
+            features = schemas.pairs_multi_features(label_names=self.CLASS_LABELS)
+        return datasets.DatasetInfo(description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION)
+    def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]:
+        """Return SplitGenerators."""
+        source_data = []
+        lang = self.config.name.split("_")[1]
+        if lang in _LANGUAGES:
+            # Load data per language
+            source_data.append(dl_manager.download_and_extract(_URL + f"{lang}/main/{lang}"))
+        else:
+            # Load examples from all languages at once.
+            for lang in _LANGUAGES:
+                source_data.append(dl_manager.download_and_extract(_URL + f"{lang}/main/{lang}"))
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepaths": source_data,
+                },
+            )
+        ]
+    def _generate_examples(self, filepaths: List[Path]) -> Tuple[int, Dict]:
+        """Yield examples as (key, example) tuples"""
+        all_forms: Dict[str, List[Dict[str, Any]]] = {}
+        for source_file in filepaths:
+            with open(source_file, encoding="utf-8") as file:
+                for row in file:
+                    if row.strip() == "" or row.strip().startswith("#"):
+                        continue
+                    lemma, word, tags = row.strip().split("\t")
+                    all_forms[lemma] = all_forms.get(lemma, [])
+                    tag_list = tags.replace("NDEF", "INDF").split(";")
+                    form = dict([("word", word), ("Other", [])] + [(cat, []) for cat, tasks in self.CLASS_CATEGORIES.items()])
+                    for tag_pre in tag_list:
+                        tag = tag_pre.split("+")
+                        if tag[0] in self.TAG_TO_CAT:
+                            form[self.TAG_TO_CAT[tag[0]]] = tag
+                        else:
+                            form["Other"] += tag
+                    all_forms[lemma] += [form]
+        if self.config.schema == "source":
+            for id_, (lemma, forms) in enumerate(all_forms.items()):
+                res = {"lemma": lemma, "forms": {}}
+                for k in ["word", "Other"] + list(self.CLASS_CATEGORIES.keys()):
+                    res["forms"][k] = [form[k] for form in forms]
+                yield id_, res
+        if self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            idx = 0
+            for lemma, forms in all_forms.items():
+                for form in forms:
+                    inflection = form.pop("word")
+                    feats = [feat[0] for feat in list(form.values()) if feat and feat[0] in self.CLASS_LABELS]
+                    example = {
+                        "id": idx,
+                        "text_1": lemma,
+                        "text_2": inflection,
+                        "label": feats,
+                    }
+                    idx += 1
+                    yield idx, example