Create parashoot.py

parashoot.py

@@ -0,0 +1,134 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+import json
+import os
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@inproceedings{keren2021parashoot,
+  title={ParaShoot: A Hebrew Question Answering Dataset},
+  author={Keren, Omri and Levy, Omer},
+  booktitle={Proceedings of the 3rd Workshop on Machine Reading for Question Answering},
+  pages={106--112},
+  year={2021}
+}
+"""
+
+_DESCRIPTION = """
+A Hebrew question and answering dataset in the style of SQuAD, based on articles scraped from Wikipedia. The dataset contains a few thousand crowdsource-annotated pairs of questions and answers, in a setting suitable for few-shot learning.
+"""
+
+_URLS = {
+    "train": "data/train.tar.gz",
+    "validation": "data/dev.tar.gz",
+    "test": "data/test.tar.gz",
+}
+
+
+class ParashootConfig(datasets.BuilderConfig):
+    """BuilderConfig for Parashoot."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for Parashoot.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(ParashootConfig, self).__init__(**kwargs)
+
+
+class Parashoot(datasets.GeneratorBasedBuilder):
+    """Parashoot: The Hebrew Question Answering Dataset. Version 1.1."""
+
+    BUILDER_CONFIGS = [
+        ParashootConfig(
+            version=datasets.Version("1.1.0", ""),
+            description=_DESCRIPTION,
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "context": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answers": datasets.features.Sequence(
+                        {
+                            "text": datasets.Value("string"),
+                            "answer_start": datasets.Value("int32"),
+                        }
+                    ),
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage="https://github.com/omrikeren/ParaShoot",
+            citation=_CITATION,
+            task_templates=[
+                QuestionAnsweringExtractive(
+                    question_column="question",
+                    context_column="context",
+                    answers_column="answers",
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": downloaded_files["train"],
+                    "basename": "train.jsonl",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": downloaded_files["validation"],
+                    "basename": "dev.jsonl",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": downloaded_files["test"],
+                    "basename": "test.jsonl",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, basename):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+        key = 0
+        with open(os.path.join(filepath, basename), encoding="utf-8") as f:
+            for line in f:
+                article = json.loads(line)
+                title = article.get("title", "")
+                context = article["context"]
+                answer_starts = article["answers"]["answer_start"]
+                answers = article["answers"]["text"]
+                yield key, {
+                    "title": title,
+                    "context": context,
+                    "question": article["question"],
+                    "id": article["id"],
+                    "answers": {
+                        "answer_start": answer_starts,
+                        "text": answers,
+                    },
+                }
+                key += 1