Delete nyt-ingredients.py

nyt-ingredients.py

@@ -1,109 +0,0 @@
-"""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)