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casa.py

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+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks
+
+_CITATION = """
+@INPROCEEDINGS{8629181,
+    author={Ilmania, Arfinda and Abdurrahman and Cahyawijaya, Samuel and Purwarianti, Ayu},
+    booktitle={2018 International Conference on Asian Language Processing (IALP)},
+    title={Aspect Detection and Sentiment Classification Using Deep Neural Network for Indonesian Aspect-Based Sentiment Analysis},
+    year={2018},
+    volume={},
+    number={},
+    pages={62-67},
+    doi={10.1109/IALP.2018.8629181
+}
+"""
+
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LOCAL = False
+
+_DATASETNAME = "casa"
+
+_DESCRIPTION = """
+CASA: An aspect-based sentiment analysis dataset consisting of around a thousand car reviews collected from multiple Indonesian online automobile platforms (Ilmania et al., 2018).
+The dataset covers six aspects of car quality.
+We define the task to be a multi-label classification task,
+where each label represents a sentiment for a single aspect with three possible values: positive, negative, and neutral.
+"""
+
+_HOMEPAGE = "https://github.com/IndoNLP/indonlu"
+
+_LICENSE = "CC-BY-SA 4.0"
+
+_URLS = {
+    "train": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/casa_absa-prosa/train_preprocess.csv",
+    "validation": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/casa_absa-prosa/valid_preprocess.csv",
+    "test": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/casa_absa-prosa/test_preprocess.csv",
+}
+
+_SUPPORTED_TASKS = [Tasks.ASPECT_BASED_SENTIMENT_ANALYSIS]
+
+_SOURCE_VERSION = "1.0.0"
+
+_NUSANTARA_VERSION = "1.0.0"
+
+
+class CASA(datasets.GeneratorBasedBuilder):
+    """CASA is an aspect based sentiment analysis dataset"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="casa_source",
+            version=SOURCE_VERSION,
+            description="CASA source schema",
+            schema="source",
+            subset_id="casa",
+        ),
+        NusantaraConfig(
+            name="casa_nusantara_text_multi",
+            version=NUSANTARA_VERSION,
+            description="CASA Nusantara schema",
+            schema="nusantara_text_multi",
+            subset_id="casa",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "casa_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "index": datasets.Value("int64"),
+                    "sentence": datasets.Value("string"),
+                    "fuel": datasets.Value("string"),
+                    "machine": datasets.Value("string"),
+                    "others": datasets.Value("string"),
+                    "part": datasets.Value("string"),
+                    "price": datasets.Value("string"),
+                    "service": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "nusantara_text_multi":
+            features = schemas.text_multi_features(["positive", "neutral", "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]:
+        train_csv_path = Path(dl_manager.download_and_extract(_URLS["train"]))
+        validation_csv_path = Path(dl_manager.download_and_extract(_URLS["validation"]))
+        test_csv_path = Path(dl_manager.download_and_extract(_URLS["test"]))
+
+        data_dir = {
+            "train": train_csv_path,
+            "validation": validation_csv_path,
+            "test": test_csv_path,
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir["test"],
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_dir["validation"],
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        df = pd.read_csv(filepath, sep=",", header="infer").reset_index()
+        if self.config.schema == "source":
+            for row in df.itertuples():
+                entry = {"index": row.index, "sentence": row.sentence, "fuel": row.fuel, "machine": row.machine, "others": row.others, "part": row.part, "price": row.price, "service": row.service}
+                yield row.index, entry
+
+        elif self.config.schema == "nusantara_text_multi":
+            for row in df.itertuples():
+                entry = {
+                    "id": str(row.index),
+                    "text": row.sentence,
+                    "labels": [label for label in row[3:]],
+                }
+                yield row.index, entry