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filwordnet.py

@@ -0,0 +1,146 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import csv
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses
+
+_CITATION = """\
+@article{article,
+author = {Borra, Allan and Pease, Adam and Edita, Rachel and Roxas, and Dita, Shirley},
+year = {2010},
+month = {01},
+pages = {},
+title = {Introducing Filipino WordNet}
+}
+"""
+
+_DATASETNAME = "filwordnet"
+
+_DESCRIPTION = """\
+Filipino WordNet (FilWordNet) is a lexical database of Filipino language.
+It was derived from the Princeton WordNet and translated by humans to Filipino.
+It documents 13,539 unique words and 9,519 synsets. Each synset includes the definition,
+part-of-speech, word senses, and Suggested Upper Merged Ontology terms (SUMO terms).
+"""
+
+_HOMEPAGE = "https://github.com/danjohnvelasco/Filipino-WordNet"
+
+_LANGUAGES = ["fil"]
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: "https://raw.githubusercontent.com/danjohnvelasco/Filipino-WordNet/main/filwordnet.csv",
+}
+
+_SUPPORTED_TASKS = []
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class FilWordNetDataset(datasets.GeneratorBasedBuilder):
+    """The Filipino WordNet (FilWordNet) is a lexical database of Filipino language containing 13,539 unique words and 9,519 synsets."""
+
+    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=f"{_DATASETNAME}",
+        )
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "word_id": datasets.Value("int32"),
+                    "lemma": datasets.Value("string"),
+                    "synset_id": datasets.Value("int32"),
+                    "sense_id": datasets.Value("int32"),
+                    "pos": datasets.Value("string"),
+                    "lexdomain_id": datasets.Value("int32"),
+                    "definition": datasets.Value("string"),
+                    "last_modifier": datasets.Value("int32"),
+                    "sumo": datasets.Value("string"),
+                }
+            )
+
+        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]
+        file = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": file,
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        rows = []
+        is_first_row = True
+        with open(filepath, "r") as file:
+            csv_reader = csv.reader(file, delimiter=",")
+            for row in csv_reader:
+                if is_first_row:  # skip first row, they are column names
+                    is_first_row = False
+                    continue
+
+                rows.append(row)
+
+        if self.config.schema == "source":
+            for key, row in enumerate(rows):
+                example = {
+                    "word_id": row[0],
+                    "lemma": row[1],
+                    "synset_id": row[2],
+                    "sense_id": row[3],
+                    "pos": row[4],
+                    "lexdomain_id": row[5],
+                    "definition": row[6],
+                    "last_modifier": row[7],
+                    "sumo": row[8],
+                }
+                yield key, example