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 months 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 (over 4 years ago)
- Default Branch: master
- Last Pushed: 2026-01-29T20:45:14.000Z (5 months ago)
- Last Synced: 2026-01-30T08:53:21.042Z (5 months ago)
- Topics: ingredients, natural-language-processing, nlp, python, recipes
- Language: Python
- Homepage: https://ingredient-parser.readthedocs.io/en/latest/
- Size: 262 MB
- Stars: 125
- Watchers: 3
- Forks: 19
- Open Issues: 2
-
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.
## 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=0.996867, starting_index=2)],
size=None,
amount=[IngredientAmount(quantity=Fraction(3, 1),
quantity_max=Fraction(3, 1),
unit=,
text='3 pounds',
confidence=0.999982,
starting_index=0,
unit_system=,
APPROXIMATE=False,
SINGULAR=False,
RANGE=False,
MULTIPLIER=False,
PREPARED_INGREDIENT=False)],
preparation=IngredientText(text='cut into 2 inch chunks',
confidence=0.999946,
starting_index=5),
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/tutorials/options.html) 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 81,000 example sentences is used to train and evaluate the model. See the [Explanation](https://ingredient-parser.readthedocs.io/en/latest/explanation/index.html) section of the documentation for more details.
The model has the following accuracy on a test data set of 20% of the total data used:
```
╒══════════════════════════╤══════════════════════════╕
│ Sentence-level results │ Word-level results │
╞══════════════════════════╪══════════════════════════╡
│ Accuracy: 95.25% │ Accuracy: 98.09% │
│ │ Precision (micro) 98.07% │
│ │ Recall (micro) 98.09% │
│ │ F1 score (micro) 98.08% │
╘══════════════════════════╧══════════════════════════╛
```
## Development
**Basic**
Train and fine-tune new ingredient datasets to expand beyond the existing trained model provided in the library. The development dependencies are in the `requirements-dev.txt` file. Details on the training process can be found in the [Explanation](https://ingredient-parser.readthedocs.io/en/latest/explanation/index.html) documentation.
**Web App**
The ingredient parser library provides a convenient web interface that you can run locally to access most of the library's functionality, including using the parser, browsing the database, labelling entries, and training the model(s). View the [specific README](webtools/README.md) in webtools for a detailed overview.
| Parser | Labeller | Trainer |
| :------- | :------- | :------- |
|  |  |  |
**Documentation**
The dependencies for building the documentation are in the `requirements-doc.txt` file.
**Contribution**
Please target the **develop** branch for pull requests. The **main** branch is used for stable releases and hotfixes only.
Before committing anything, install [pre-commit](https://pre-commit.com/) and run the following to install the hooks:
```bash
$ pre-commit install
```
Pre-commit hooks cover both the main python library code and the web app (webtools) code.