nyt-ingredients.py

"""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"

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 WNUT 17 Emerging Entities 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."""
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": "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)