Datasets:

napsternxg
/

nyt_ingredients

	"""New York Times Ingredient Phrase Tagger Dataset"""


	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@misc{nytimesTaggedIngredients,
	author = {Erica Greene and Adam Mckaig},
	title = {{O}ur {T}agged {I}ngredients {D}ata is {N}ow on {G}it{H}ub --- archive.nytimes.com},
	howpublished = {\\url{https://archive.nytimes.com/open.blogs.nytimes.com/2016/04/27/structured-ingredients-data-tagging/}},
	year = {},
	note = {[Accessed 03-10-2023]},
	}
	"""


	_DESCRIPTION = """\
	New York Times Ingredient Phrase Tagger Dataset
	We use a conditional random field model (CRF) to extract tags from labelled training data, which was tagged by human news assistants.
	e wrote about our approach on the [New York Times Open blog](http://open.blogs.nytimes.com/2015/04/09/extracting-structured-data-from-recipes-using-conditional-random-fields/).
	This repo contains scripts to extract the Quantity, Unit, Name, and Comments from unstructured ingredient phrases.
	We use it on Cooking to format incoming recipes. Given the following input:

	```
	1 pound carrots, young ones if possible
	Kosher salt, to taste
	2 tablespoons sherry vinegar
	2 tablespoons honey
	2 tablespoons extra-virgin olive oil
	1 medium-size shallot, peeled and finely diced
	1/2 teaspoon fresh thyme leaves, finely chopped
	Black pepper, to taste
	```
	"""

	_URL = "https://github.com/nytimes/ingredient-phrase-tagger"


	_URLS = {
	"train": "https://huggingface.co/datasets/napsternxg/nyt_ingredients/resolve/main/nyt-ingredients.crf.jsonl"
	}

	import json


	class NYTIngredientsConfig(datasets.BuilderConfig):
	"""The NYT Ingredients Dataset."""

	def __init__(self, **kwargs):
	"""BuilderConfig for NYT Ingredients.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(NYTIngredientsConfig, self).__init__(**kwargs)


	class NYTIngredients(datasets.GeneratorBasedBuilder):
	"""The NYT Ingredients Dataset."""

	BUILDER_CONFIGS = [
	NYTIngredientsConfig(
	name="nyt_ingredients",
	version=datasets.Version("1.0.0"),
	description="The NYT Ingredients Dataset",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"input": datasets.Value("string"),
	"display_input": datasets.Value("string"),
	"tokens": datasets.Sequence(datasets.Value("string")),
	"index": datasets.Sequence(datasets.Value("string")),
	"lengthGroup": datasets.Sequence(datasets.Value("string")),
	"isCapitalized": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"NoCAP",
	"YesCAP"
	]
	)
	),
	"insideParenthesis": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"NoPAREN",
	"YesPAREN",
	]
	)
	),
	"label": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"O",
	"B-COMMENT",
	"I-COMMENT",
	"B-NAME",
	"I-NAME",
	"B-RANGE_END",
	"I-RANGE_END",
	"B-QTY",
	"I-QTY",
	"B-UNIT",
	"I-UNIT",
	]
	)
	),
	}
	),
	supervised_keys=None,
	homepage="https://github.com/nytimes/ingredient-phrase-tagger",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	downloaded_files = dl_manager.download_and_extract(_URLS)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": downloaded_files["train"]},
	),
	]

	def _generate_examples(self, filepath):
	logger.info("⏳ Generating examples from = %s", filepath)
	with open(filepath, encoding="utf-8") as fp:
	for i, line in enumerate(fp):
	yield i, json.loads(line)