Upload tmad_malay_corpus.py with huggingface_hub

tmad_malay_corpus.py

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+# 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.
+
+"""
+The Towards Malay Abbreviation Disambiguation (TMAD) Malay Corpus includes sentences from Malay news sites with abbreviations and their meanings. Only abbreviations with more than one possible meaning are included.
+"""
+import csv
+import json
+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 = """\
+@article{article,
+author = {Ciosici, Manuel and Sommer, Tobias},
+year = {2019},
+month = {04},
+pages = {},
+title = {Unsupervised Abbreviation Disambiguation Contextual disambiguation using word embeddings}
+}
+"""
+
+_DATASETNAME = "tmad_malay_corpus"
+
+_DESCRIPTION = """\
+The Towards Malay Abbreviation Disambiguation (TMAD) Malay Corpus includes sentences from Malay news sites with abbreviations and their meanings. Only abbreviations with more than one possible meaning are included.
+"""
+
+_HOMEPAGE = "https://github.com/bhysss/TMAD-CUM/tree/master"
+
+_LANGUAGES = ["zlm"]
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = False
+
+_URLS = {
+    "train": "https://raw.githubusercontent.com/bhysss/TMAD-CUM/master/data/Malay/data_train.csv",
+    "dev": "https://raw.githubusercontent.com/bhysss/TMAD-CUM/master/data/Malay/data_dev.csv",
+    "test": "https://raw.githubusercontent.com/bhysss/TMAD-CUM/master/data/Malay/data_test.csv",
+    "dict": "https://raw.githubusercontent.com/bhysss/TMAD-CUM/master/data/Malay/May_dic.json",
+}
+_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
+
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class TMADMalayCorpusDataset(datasets.GeneratorBasedBuilder):
+    """Abbreviation disambiguation dataset from Malay news sites."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description="{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_qa",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_qa",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features({"abbr": datasets.Value("string"), "definition": datasets.Value("string"), "sentence": datasets.Value("string"), "choices": datasets.Sequence(datasets.Value("string"))})
+
+        elif self.config.schema == "seacrowd_qa":
+            features = schemas.qa_features
+
+        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."""
+
+        data_dirs = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # Whatever you put in gen_kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": data_dirs["train"], "dictpath": data_dirs["dict"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": data_dirs["test"], "dictpath": data_dirs["dict"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": data_dirs["dev"], "dictpath": data_dirs["dict"]},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, dictpath: Path) -> Tuple[int, Dict]:
+
+        with open(dictpath) as f:
+            may_dict = json.load(f)
+
+        if self.config.schema == "source":
+            with open(filepath, encoding="utf-8") as f:
+                for row_idx, row in enumerate(csv.DictReader(f)):
+                    yield row_idx, {"abbr": row["Abbr"], "definition": row["Definition"], "sentence": row["Sentence"], "choices": may_dict[row["Abbr"]]}
+
+        elif self.config.schema == "seacrowd_qa":
+            with open(filepath, encoding="utf-8") as f:
+                for row_idx, row in enumerate(csv.DictReader(f)):
+                    yield row_idx, {"id": row_idx, "question_id": 0, "document_id": 0, "question": row["Abbr"], "type": "multiple_choice", "choices": may_dict[row["Abbr"]], "context": row["Sentence"], "answer": [row["Definition"]], "meta": {}}