init commit (#604)

seacrowd/sea_datasets/minang_senti/minang_senti.py

@@ -0,0 +1,170 @@
+# 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.
+
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+from pandas import read_excel
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import TASK_TO_SCHEMA, Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{koto-koto-2020-towards,
+    title = "Towards Computational Linguistics in {M}inangkabau Language:
+    Studies on Sentiment Analysis and Machine Translation",
+    author = "Koto, Fajri  and
+        Koto, Ikhwan",
+    editor = "Nguyen, Minh Le  and
+        Luong, Mai Chi  and
+        Song, Sanghoun",
+    booktitle = "Proceedings of the 34th Pacific Asia Conference on Language,
+    Information and Computation",
+    month = oct,
+    year = "2020",
+    address = "Hanoi, Vietnam",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.paclic-1.17",
+    pages = "138--148",
+}
+"""
+
+_DATASETNAME = "minang_senti"
+
+_DESCRIPTION = """\
+We release the Minangkabau corpus for sentiment analysis by manually translating
+5,000 sentences of Indonesian sentiment analysis corpora. In this work, we
+conduct a binary sentiment classification on positive and negative sentences by
+first manually translating the Indonesian sentiment analysis corpus to the
+Minangkabau language (Agam-Tanah Datar dialect)
+"""
+
+_HOMEPAGE = "https://github.com/fajri91/minangNLP"
+
+_LANGUAGES = ["ind", "min"]
+
+_LICENSE = Licenses.MIT.value
+
+_LOCAL = False
+
+_BASE_URL = "https://github.com/fajri91/minangNLP/raw/master/sentiment/data/folds/{split}{index}.xlsx"
+
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
+_SEACROWD_SCHEMA = f"seacrowd_{TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()}"  # text
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "1.0.0"
+
+
+class MinangSentiDataset(datasets.GeneratorBasedBuilder):
+    """Binary sentiment classification on manually translated Minangkabau corpus."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = []
+    for subset in _LANGUAGES:
+        BUILDER_CONFIGS += [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{subset}_source",
+                version=SOURCE_VERSION,
+                description=f"{_DATASETNAME} {subset} source schema",
+                schema="source",
+                subset_id=subset,
+            ),
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{subset}_{_SEACROWD_SCHEMA}",
+                version=SEACROWD_VERSION,
+                description=f"{_DATASETNAME} {subset} SEACrowd schema",
+                schema=_SEACROWD_SCHEMA,
+                subset_id=subset,
+            ),
+        ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_{_LANGUAGES[0]}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "minang": datasets.Value("string"),
+                    "indo": datasets.Value("string"),
+                    "sentiment": datasets.ClassLabel(names=["positive", "negative"]),
+                }
+            )
+        elif self.config.schema == _SEACROWD_SCHEMA:
+            features = schemas.text_features(label_names=["positive", "negative"])
+
+        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."""
+        train_urls = [_BASE_URL.format(split="train", index=i) for i in range(5)]
+        test_urls = [_BASE_URL.format(split="test", index=i) for i in range(5)]
+        dev_urls = [_BASE_URL.format(split="dev", index=i) for i in range(5)]
+
+        train_paths = [Path(dl_manager.download(url)) for url in train_urls]
+        test_paths = [Path(dl_manager.download(url)) for url in test_urls]
+        dev_paths = [Path(dl_manager.download(url)) for url in dev_urls]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": train_paths,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": test_paths,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": dev_paths,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        key = 0
+        for file in filepath:
+            data = read_excel(file)
+            for _, row in data.iterrows():
+                if self.config.schema == "source":
+                    yield key, {
+                        "minang": row["minang"],
+                        "indo": row["indo"],
+                        "sentiment": row["sentiment"],
+                    }
+                elif self.config.schema == _SEACROWD_SCHEMA:
+                    yield key, {
+                        "id": str(key),
+                        "text": row["minang"] if self.config.subset_id == "min" else row["indo"],
+                        "label": row["sentiment"],
+                    }
+                key += 1