| import csv | |
| import os | |
| import datasets | |
| _CITATION = """ | |
| @misc{RecipeNLGLite, | |
| author = {Mehrdad Farahani}, | |
| title = {RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation (Lite)}, | |
| year = 2021, | |
| publisher = {GitHub}, | |
| journal = {GitHub repository}, | |
| howpublished = {url{https://github.com/m3hrdadfi/recipe-nlg-lite}}, | |
| } | |
| """ | |
| _DESCRIPTION = """ | |
| RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation - Lite version | |
| The dataset we publish contains 7,198 cooking recipes (>7K). | |
| It's processed in more careful way and provides more samples than any other dataset in the area.""" | |
| _HOMEPAGE = "https://github.com/m3hrdadfi/recipe-nlg-lite" | |
| _LICENSE = "MIT License" | |
| _URLs = { | |
| "1.0.0": { | |
| "data": "https://drive.google.com/uc?id=1PGH5H_oW7wUvMw_5xaXvbEN7DFll-wDX", | |
| "features": [ | |
| {"name": "uid", "type": datasets.Value("string")}, | |
| {"name": "name", "type": datasets.Value("string")}, | |
| {"name": "description", "type": datasets.Value("string")}, | |
| {"name": "link", "type": datasets.Value("string")}, | |
| {"name": "ner", "type": datasets.Value("string")}, | |
| {"name": "ingredients", "type": datasets.Value("string")}, | |
| {"name": "steps", "type": datasets.Value("string")}, | |
| ], | |
| } | |
| } | |
| class RecipeNLGLiteConfig(datasets.BuilderConfig): | |
| """BuilderConfig for RecipeNLGLite.""" | |
| def __init__(self, **kwargs): | |
| """BuilderConfig for RecipeNLGLite. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(RecipeNLGLiteConfig, self).__init__(**kwargs) | |
| class RecipeNLGLite(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIGS = [ | |
| RecipeNLGLiteConfig( | |
| name="1.0.0", version=datasets.Version("1.0.0"), description="The first version of recipe_nlg_lite" | |
| ), | |
| ] | |
| def _info(self): | |
| feature_names_types = _URLs[self.config.name]["features"] | |
| features = datasets.Features({f["name"]: f["type"] for f in feature_names_types}) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, citation=_CITATION | |
| ) | |
| def _split_generators(self, dl_manager): | |
| my_urls = _URLs[self.config.name] | |
| data_dir = dl_manager.download_and_extract(my_urls["data"]) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir, "recipe_nlg_lite", "train.csv"), | |
| "split": "train", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir, "recipe_nlg_lite", "test.csv"), | |
| "split": "test", | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, filepath, split): | |
| feature_names_types = _URLs[self.config.name]["features"] | |
| features = [f["name"] for f in feature_names_types] | |
| with open(filepath, encoding="utf-8") as csv_file: | |
| reader = csv.DictReader(csv_file, quotechar='"', delimiter="\t", quoting=csv.QUOTE_MINIMAL) | |
| for _id, row in enumerate(reader): | |
| if len(row) == len(features): | |
| yield _id, {f: row[f] for f in features} | |