ingredient-parser-nlp 0.1.0b9

A Python package to parse structured information from recipe ingredient sentences

Ingredient Parser

The Ingredient Parser package is a Python package for parsing structured information out of recipe ingredient sentences.

Documentation

Documentation on using the package and training the model can be found at https://ingredient-parser.readthedocs.io/.

Quick Start

Install the package using pip

$ python -m pip install ingredient-parser-nlp

Import the parse_ingredient function and pass it an ingredient sentence.

>>> from ingredient_parser import parse_ingredient
>>> parse_ingredient("3 pounds pork shoulder, cut into 2-inch chunks")
ParsedIngredient(
    name=IngredientText(text='pork shoulder', confidence=0.999193),
    size=None,
    amount=[IngredientAmount(quantity='3',
                             unit=<Unit('pound')>,
                             text='3 pounds',
                             confidence=0.999906,,
                             APPROXIMATE=False,
                             SINGULAR=False)],
    preparation=IngredientText(text='cut into 2 inch chunks', confidence=0.999193),
    comment=None,
    sentence='3 pounds pork shoulder, cut into 2-inch chunks'
)

Model accuracy

The model used for labelling tokens in sentences, provided in the ingredient-parser/ directory, has the following accuracy on a test data set of 25% of the total data used:

Sentence-level results:
	Accuracy: 94.24%

Word-level results:
	Accuracy 97.73%
	Precision (micro) 97.72%
	Recall (micro) 97.73%
	F1 score (micro) 97.72%

Development

The development dependencies are in the requirements-dev.txt file. Details on the training process can be found in the Model Guide documentation.

Before committing anything, install pre-commit and run

pre-commit install

to install the pre-commit hooks.

There is a simple web app for testing the parser with ingredient sentences and showing the parsed output. To run the web app, run the command

$ flask --app webapp run

This requires the development dependencies to be installed.

The dependencies for building the documentation are in the requirements-doc.txt file.