Update parquet files

README.md

@@ -1,34 +0,0 @@
----
-task_categories:
-- question-answering
-language:
-- he
----
-
-# ParaShoot
-
-[ParaShoot](https://github.com/omrikeren/ParaShoot): A Hebrew question and answering dataset in the style of [SQuAD](https://arxiv.org/abs/1606.05250), 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.
-
-For more details and quality analysis, see the [paper](https://arxiv.org/abs/2109.11314).
-
-## Dataset Statistics
-
-| **#Items** | **#Articles** | **#Paragraphs** |         |
-| ---------- | ------------- | --------------- | ------- |
-| Train      | 1792          | 295             | 565     |
-| Dev        | 221           | 33              | 63      |
-| Test       | 1025          | 165             | 319     |
-| **Total**  | **3038**      | **493**         | **947** |
-
-
-## Citing
-If you use ParaShoot in your research, please cite the ParaShoot paper:
-```bibtex
-@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}
-}
-```

data/test.tar.gz → default/parashoot-test.parquet

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data/train.tar.gz → default/parashoot-train.parquet

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data/dev.tar.gz → default/parashoot-validation.parquet

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parashoot.py

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-#!/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