fs.py

# coding=utf-8
# Lint as: python3
"""The SCROLLS benchmark."""

import json
import os
from abc import abstractmethod
from typing import Union, NoReturn

import datasets


class FewsionConfig(datasets.BuilderConfig):
    """BuilderConfig for SCROLLS."""

    def __init__(self, data_url, **kwargs):
        """BuilderConfig for SCROLLS.
        Args:
          features: `list[string]`, list of the features that will appear in the
            feature dict. Should not include "label".
          data_url: `string`, url to download the zip file from.
          citation: `string`, citation for the data set.
          url: `string`, url for information about the data set.
          label_classes: `list[string]`, the list of classes for the label if the
            label is present as a string. Non-string labels will be cast to either
            'False' or 'True'.
          **kwargs: keyword arguments forwarded to super.
        """
        super(FewsionConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
        self.data_url = data_url
        self.features = [self.source_column_name, self.target_column_name, self.id_column_name]
        if self.question_column_name:
            self.features.append(self.question_column_name)

    @property
    @abstractmethod
    def source_column_name(self) -> str:
        pass

    @property
    @abstractmethod
    def target_column_name(self) -> str:
        pass

    @property
    @abstractmethod
    def question_column_name(self) -> Union[str, NoReturn]:
        pass

    @property
    @abstractmethod
    def id_column_name(self) -> str:
        pass


class ArxivConfig(FewsionConfig):

    @property
    def source_column_name(self) -> str:
        return "article"

    @property
    def target_column_name(self) -> str:
        return "abstract"

    @property
    def question_column_name(self) -> Union[str, NoReturn]:
        return

    @property
    def id_column_name(self) -> str:
        return "article_id"


class RedditTIFUConfig(FewsionConfig):

    @property
    def source_column_name(self) -> str:
        return "document"

    @property
    def target_column_name(self) -> str:
        return "tldr"

    @property
    def question_column_name(self) -> Union[str, NoReturn]:
        return

    @property
    def id_column_name(self) -> str:
        return "id"


class Fewsion(datasets.GeneratorBasedBuilder):

    DEFAULT_WRITER_BATCH_SIZE = 1000  # because Narrative QA is a rather large dataset
    BUILDER_CONFIGS = [
        ArxivConfig(
            name="arxiv",
            data_url="https://fewsion.s3.us-east-2.amazonaws.com/arxiv.zip",
        ),
        RedditTIFUConfig(
            name="reddit_tifu",
            data_url="https://fewsion.s3.us-east-2.amazonaws.com/reddit_tifu.zip",
        )
    ]

    def _info(self):
        features = {feature: datasets.Value("string") for feature in self.config.features}

        return datasets.DatasetInfo(
            description="",
            features=datasets.Features(features),
            homepage="",
            citation="",
        )

    def _split_generators(self, dl_manager):
        dl_dir = dl_manager.download_and_extract(self.config.data_url)
        task_name = _get_task_name_from_data_url(self.config.data_url)
        dl_dir = os.path.join(dl_dir, task_name)

        data_files = {} if self.config.data_files is not None else None
        if data_files is not None:
            for split, paths in self.config.data_files.items():
                data_files[split] = paths[0]

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "train.jsonl"),
                    "split": datasets.Split.TRAIN,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "val.jsonl"),
                    "split": datasets.Split.VALIDATION,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "test.jsonl") if data_files is None else data_files["test"],
                    "split": datasets.Split.TEST,
                },
            ),
        ]

    def _generate_examples(self, data_file, split):
        with open(data_file, encoding="utf-8") as f:
            for line in f:
                row = json.loads(line)
                yield row[self.config.id_column_name], row


def _get_task_name_from_data_url(data_url):
    return data_url.split("/")[-1].split(".")[0]