Upload nyt_ingredients.py

nyt_ingredients.py

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+
+"""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)