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import datasets |
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import logging |
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import csv |
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import sys |
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from csv import DictReader |
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csv.field_size_limit(sys.maxsize) |
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logger = logging.getLogger(__name__) |
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class FFV4Config(datasets.BuilderConfig): |
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"""BuilderConfig for SuperGLUE.""" |
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def __init__(self, filename: str, info: str, **kwargs): |
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"""BuilderConfig for SuperGLUE. |
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Args: |
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features: *list[string]*, list of the features that will appear in the |
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feature dict. Should not include "label". |
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filename: *string*, csvfile for the dataset. |
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info: *string*, for information about the data set. |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super().__init__(version=datasets.Version("0.0.1"), **kwargs) |
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self.filename = filename |
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self.info = info |
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class FFV4(datasets.GeneratorBasedBuilder): |
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"""The thing""" |
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BUILDER_CONFIGS = [ |
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FFV4Config( |
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name="notebook_defaults", |
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filename="notebook_defaults.csv", |
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info="the result of using the default values in the V4 ffarchive notebook, except without the TS/RD filter", |
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), |
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FFV4Config( |
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name="notebook_defaults_ratio0.8_likes10", |
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filename="ratio0.8_likes10.csv", |
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info="default filter, but with the score filter replaced with '.ratio > 0.8, .likes > 10'", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "notebook_defaults" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description="Garbage datasets for LLM training", |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("int32"), |
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"header": datasets.Value("string"), |
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"story": datasets.Value("string"), |
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} |
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), |
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homepage="https://main.horse", |
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) |
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def _split_generators(self, x): |
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return [ |
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datasets.SplitGenerator('everything', gen_kwargs={"filepath": self.config.filename}), |
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] |
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''' |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), |
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''' |
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def _generate_examples(self, filepath): |
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"""This function returns the examples in the raw (text) form.""" |
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logger.info("generating examples from = %s", filepath) |
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with open(filepath, encoding="utf-8") as f: |
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dr = DictReader(f) |
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for d in dr: |
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yield d['id'],d |
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''' |
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squad = json.load(f) |
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for article in squad["data"]: |
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title = article.get("title", "") |
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for paragraph in article["paragraphs"]: |
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context = paragraph["context"] # do not strip leading blank spaces GH-2585 |
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for qa in paragraph["qas"]: |
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answer_starts = [answer["answer_start"] for answer in qa["answers"]] |
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answers = [answer["text"] for answer in qa["answers"]] |
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# Features currently used are "context", "question", and "answers". |
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# Others are extracted here for the ease of future expansions. |
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yield key, { |
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"title": title, |
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"context": context, |
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"question": qa["question"], |
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"id": qa["id"], |
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"answers": { |
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"answer_start": answer_starts, |
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"text": answers, |
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}, |
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} |
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key += 1 |
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''' |
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