https://github.com/strangetom/ingredient-parser

A tool to parse recipe ingredients into structured data
https://github.com/strangetom/ingredient-parser

ingredients natural-language-processing nlp python recipes

Last synced: 5 days ago
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A tool to parse recipe ingredients into structured data

• Host: GitHub
• URL: https://github.com/strangetom/ingredient-parser
• Owner: strangetom
• License: mit
• Created: 2021-10-17T15:52:12.000Z (about 3 years ago)
• Default Branch: master
• Last Pushed: 2024-12-06T07:27:43.000Z (16 days ago)
• Last Synced: 2024-12-06T21:58:29.370Z (16 days ago)
• Topics: ingredients, natural-language-processing, nlp, python, recipes
• Language: Python
• Homepage: https://ingredient-parser.readthedocs.io/en/latest/
• Size: 159 MB
• Stars: 74
• Watchers: 3
• Forks: 12
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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README

        
# Ingredient Parser

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

![](docs/source/_static/logo.png)

## 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

```bash

$ python -m pip install ingredient-parser-nlp

```

Import the ```parse_ingredient``` function and pass it an ingredient sentence.

```python

>>> 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=,

                             text='3 pounds',

                             confidence=0.999906,,

                             APPROXIMATE=False,

                             SINGULAR=False)],

    preparation=IngredientText(text='cut into 2 inch chunks', confidence=0.999193),

    comment=None,

    purpose=None,

    foundation_foods=[],

    sentence='3 pounds pork shoulder, cut into 2-inch chunks'

)

```

Refer to the documentation [here](https://ingredient-parser.readthedocs.io/en/latest/start/index.html#optional-parameters) for the optional parameters that can be used with `parse_ingredient` .

## Model

The core of the library is a sequence labelling model that is used to label each token in the sentence with the part of the sentence it belongs to. A data set of 75,000 example sentences is used to train and evaluate the model. See the [Model Guide](https://ingredient-parser.readthedocs.io/en/latest/guide/index.html) in the documentation for mode details.

The model has the following accuracy on a test data set of 20% of the total data used:

```

Sentence-level results:

	Accuracy: 95.87%

Word-level results:

	Accuracy 98.29%

	Precision (micro) 98.29%

	Recall (micro) 98.29%

	F1 score (micro) 98.29%

```

## Development

The development dependencies are in the ```requirements-dev.txt``` file. Details on the training process can be found in the [Model Guide](https://ingredient-parser.readthedocs.io/en/latest/guide/index.html) documentation.

Before committing anything, install [pre-commit](https://pre-commit.com/) 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

```bash

$ flask --app webapp run

```

![Screen shot of web app](docs/source/_static/app-screenshot.png)

This requires the development dependencies to be installed.

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