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facqa.py

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+import ast
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from nusacrowd.nusa_datasets.facqa.utils.facqa_utils import (getAnswerString, listToString)
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks
+
+_CITATION = """
+@inproceedings{purwarianti2007machine,
+  title={A Machine Learning Approach for Indonesian Question Answering System},
+  author={Ayu Purwarianti, Masatoshi Tsuchiya, and Seiichi Nakagawa},
+  booktitle={Proceedings of Artificial Intelligence and Applications },
+  pages={573--578},
+  year={2007}
+}
+"""
+
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LOCAL = False
+
+_DATASETNAME = "facqa"
+
+_DESCRIPTION = """
+FacQA: The goal of the FacQA dataset is to find the answer to a question from a provided short passage from a news article.
+Each row in the FacQA dataset consists of a question, a short passage, and a label phrase, which can be found inside the
+corresponding short passage. There are six categories of questions: date, location, name,
+organization, person, and quantitative.
+"""
+
+_HOMEPAGE = "https://github.com/IndoNLP/indonlu"
+
+_LICENSE = "CC-BY-SA 4.0"
+
+_URLS = {
+    _DATASETNAME: {
+        "test": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/facqa_qa-factoid-itb/test_preprocess.csv",
+        "train": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/facqa_qa-factoid-itb/train_preprocess.csv",
+        "validation": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/facqa_qa-factoid-itb/valid_preprocess.csv",
+    }
+}
+
+_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_NUSANTARA_VERSION = "1.0.0"
+
+
+class FacqaDataset(datasets.GeneratorBasedBuilder):
+    """FacQA dataset is a labeled dataset for indonesian question answering task"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="facqa_source",
+            version=SOURCE_VERSION,
+            description="FacQA source schema",
+            schema="source",
+            subset_id="facqa",
+        ),
+        NusantaraConfig(
+            name="facqa_nusantara_qa",
+            version=NUSANTARA_VERSION,
+            description="FacQA Nusantara schema",
+            schema="nusantara_qa",
+            subset_id="facqa",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "facqa_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "index": datasets.Value("int64"),
+                    "question": [datasets.Value("string")],
+                    "passage": [datasets.Value("string")],
+                    "seq_label": [datasets.Value("string")],
+                }
+            )
+        elif self.config.schema == "nusantara_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."""
+        urls = _URLS[_DATASETNAME]
+        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_files = {
+            "train": train_csv_path,
+            "validation": validation_csv_path,
+            "test": test_csv_path,
+        }
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_files["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_files["test"],
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_files["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, "question": ast.literal_eval(row.question), "passage": ast.literal_eval(row.passage), "seq_label": ast.literal_eval(row.seq_label)}
+                yield row.index, entry
+
+        elif self.config.schema == "nusantara_qa":
+            for row in df.itertuples():
+                entry = {
+                    "id": str(row.index),
+                    "question_id": str(row.index),
+                    "document_id": str(row.index),
+                    "question": listToString(ast.literal_eval(row.question)),
+                    "type": "extractive",
+                    "choices": [],
+                    "context": listToString(ast.literal_eval(row.passage)),
+                    "answer": [getAnswerString(ast.literal_eval(row.passage), ast.literal_eval(row.seq_label))],
+                }
+                yield row.index, entry