dataset_code.py

import datasets
import logging
import csv
import sys
from csv import DictReader
csv.field_size_limit(sys.maxsize)

logger = logging.getLogger(__name__)

class FFV4Config(datasets.BuilderConfig):
    """BuilderConfig for SuperGLUE."""

    def __init__(self, filename: str, info: str, **kwargs):
        """BuilderConfig for SuperGLUE.

        Args:
        features: *list[string]*, list of the features that will appear in the
            feature dict. Should not include "label".
        filename: *string*, csvfile for the dataset.
        info: *string*, for information about the data set.
        **kwargs: keyword arguments forwarded to super.
        """
        # Version history:
        # 0.0.1: Initial version
        super().__init__(version=datasets.Version("0.0.1"), **kwargs)
        self.filename = filename
        self.info = info

class FFV4(datasets.GeneratorBasedBuilder):
    """The thing"""

    BUILDER_CONFIGS = [
        FFV4Config(
            name="notebook_defaults",
            filename="notebook_defaults.csv",
            info="the result of using the default values in the V4 ffarchive notebook, except without the TS/RD filter",
        ),
        FFV4Config(
            name="notebook_defaults_ratio0.8_likes10",
            filename="ratio0.8_likes10.csv",
            info="default filter, but with the score filter replaced with '.ratio > 0.8, .likes > 10'",
        ),
    ]
    DEFAULT_CONFIG_NAME = "notebook_defaults"


    def _info(self):
        return datasets.DatasetInfo(
            description="Garbage datasets for LLM training",
            features=datasets.Features(
                {
                    "id": datasets.Value("int32"),
                    "header": datasets.Value("string"),
                    "story": datasets.Value("string"),
                }
            ),
            homepage="https://main.horse",
        )


    def _split_generators(self, x):
        return [
            datasets.SplitGenerator('everything', gen_kwargs={"filepath": self.config.filename}),
        ]
        '''
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
        '''

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        logger.info("generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            dr = DictReader(f)
            for d in dr:
                yield d['id'],d
            '''
            squad = json.load(f)
            for article in squad["data"]:
                title = article.get("title", "")
                for paragraph in article["paragraphs"]:
                    context = paragraph["context"]  # do not strip leading blank spaces GH-2585
                    for qa in paragraph["qas"]:
                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
                        answers = [answer["text"] for answer in qa["answers"]]
                        # Features currently used are "context", "question", and "answers".
                        # Others are extracted here for the ease of future expansions.
                        yield key, {
                            "title": title,
                            "context": context,
                            "question": qa["question"],
                            "id": qa["id"],
                            "answers": {
                                "answer_start": answer_starts,
                                "text": answers,
                            },
                        }
                        key += 1
            '''