Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
100K<n<1M
License:
"""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 NYTIngedientsConfig(datasets.BuilderConfig): | |
"""The NYTIngedients Dataset.""" | |
def __init__(self, **kwargs): | |
"""BuilderConfig for NYTIngedients. | |
Args: | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
super(NYTIngedientsConfig, self).__init__(**kwargs) | |
class NYTIngedients(datasets.GeneratorBasedBuilder): | |
"""The NYTIngedients Dataset.""" | |
BUILDER_CONFIGS = [ | |
NYTIngedientsConfig( | |
name="nyt_ingredients", | |
version=datasets.Version("1.0.0"), | |
description="The NYTIngedients 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.Value("string")), | |
"insideParenthesis": datasets.Sequence(datasets.Value("string")), | |
"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.""" | |
urls = _URLS[self.config.name] | |
data_dir = dl_manager.download_and_extract(urls) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={"filepath": f"{data_dir}/nyt-ingredients.crf.jsonl"}, | |
), | |
] | |
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) | |