| 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. |
| """ |
| |
| |
| 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 |
| ''' |
|
|
