Upload abui_wordnet.py with huggingface_hub

abui_wordnet.py

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+# coding=utf-8
+
+
+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{kratochvil-morgado-da-costa-2022-abui,
+title = "{A}bui {W}ordnet: Using a Toolbox Dictionary to develop a wordnet for a low-resource language",
+author = "Kratochvil, Frantisek  and
+  Morgado da Costa, Lu{\'}s",
+editor = "Serikov, Oleg  and
+  Voloshina, Ekaterina  and
+  Postnikova, Anna  and
+  Klyachko, Elena  and
+  Neminova, Ekaterina  and
+  Vylomova, Ekaterina  and
+  Shavrina, Tatiana  and
+  Ferrand, Eric Le  and
+  Malykh, Valentin  and
+  Tyers, Francis  and
+  Arkhangelskiy, Timofey  and
+  Mikhailov, Vladislav  and
+  Fenogenova, Alena",
+booktitle = "Proceedings of the first workshop on NLP applications to field linguistics",
+month = oct,
+year = "2022",
+address = "Gyeongju, Republic of Korea",
+publisher = "International Conference on Computational Linguistics",
+url = "https://aclanthology.org/2022.fieldmatters-1.7",
+pages = "54--63",
+abstract = "This paper describes a procedure to link a Toolbox dictionary of a low-resource language to correct
+synsets, generating a new wordnet. We introduce a bootstrapping technique utilising the information in the gloss
+fields (English, national, and regional) to generate sense candidates using a naive algorithm based on
+multilingual sense intersection. We show that this technique is quite effective when glosses are available in
+more than one language. Our technique complements the previous work by Rosman et al. (2014) which linked the
+SIL Semantic Domains to wordnet senses. Through this work we have created a small, fully hand-checked wordnet
+for Abui, containing over 1,400 concepts and 3,600 senses.",
+}
+"""
+_DATASETNAME = "abui_wordnet"
+_DESCRIPTION = """\
+A small fully hand-checked wordnet for Abui, containing over 1,400 concepts and 3,600 senses, is created. A
+bootstrapping technique is introduced to utilise the information in the gloss fields (English, national, and regional)
+to generate sense candidates using a naive algorithm based on multilingual sense intersection.
+"""
+
+_HOMEPAGE = "https://github.com/fanacek/abuiwn"
+_LANGUAGES = ["abz"]
+_LICENSE = Licenses.CC_BY_4_0.value
+_LOCAL = False
+_URLS = {
+    _DATASETNAME: "https://raw.githubusercontent.com/fanacek/abuiwn/main/abwn_lmf.tsv",
+}
+
+_SUPPORTED_TASKS = [Tasks.WORD_ANALOGY]
+
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class AbuiwordnetDataset(datasets.GeneratorBasedBuilder):
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=_DESCRIPTION,
+            schema="source",
+            subset_id="abui_wordnet",
+        ),
+        # SEACrowdConfig(
+        #     name="abui_wordnet_seacrowd_ww",
+        #     version=SEACROWD_VERSION,
+        #     description="abuiw SEACrowd schema",
+        #     schema="seacrowd_a",
+        #     subset_id="abui_wordnet",
+        # ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        features = None
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "sense": datasets.Value("string"),
+                    "pos": datasets.Value("string"),
+                    "lang": datasets.Value("string"),
+                    "lemma": datasets.Value("string"),
+                    "form": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "seacrowd_pair":
+            features = schemas.pairs_features
+            raise NotImplementedError()
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name="senses",
+                gen_kwargs={
+                    "filepath": data_dir,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
+        with open(filepath, "r") as filein:
+            data_instances = [inst.strip("\n").split("\t") for inst in filein.readlines()]
+        if self.config.schema == "source":
+            for idx, example in enumerate(data_instances):
+                sense = example[0]
+                pos = example[0][-1]
+                lang = example[1]
+                lemma = example[2]
+                form = "" if len(example) == 3 else example[3]
+                yield idx, {
+                    "sense": sense,
+                    "pos": pos,
+                    "lang": lang,
+                    "lemma": lemma,
+                    "form": form,
+                }
+        # elif self.config.schema == "seacrowd_pair":
+        #