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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}
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