# 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=0.999193)],
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,
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 81,000 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 │ Word-level results │
╞══════════════════════════╪══════════════════════════╡
│ Accuracy: 95.27% │ Accuracy: 98.10% │
│ │ Precision (micro) 98.08% │
│ │ Recall (micro) 98.10% │
│ │ F1 score (micro) 98.09% │
╘══════════════════════════╧══════════════════════════╛
```
## 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 [Model Guide](https://ingredient-parser.readthedocs.io/en/latest/guide/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.
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"description": "# Ingredient Parser\n\nThe Ingredient Parser package is a Python package for parsing structured information out of recipe ingredient sentences.\n\n\n\n## Documentation\n\nDocumentation on using the package and training the model can be found at https://ingredient-parser.readthedocs.io/.\n\n## Quick Start\n\nInstall the package using pip\n\n```bash\n$ python -m pip install ingredient-parser-nlp\n```\n\nImport the ```parse_ingredient``` function and pass it an ingredient sentence.\n\n```python\n>>> from ingredient_parser import parse_ingredient\n>>> parse_ingredient(\"3 pounds pork shoulder, cut into 2-inch chunks\")\nParsedIngredient(\n name=[IngredientText(text='pork shoulder', confidence=0.999193)],\n size=None,\n amount=[IngredientAmount(quantity='3',\n unit=<Unit('pound')>,\n text='3 pounds',\n confidence=0.999906,,\n APPROXIMATE=False,\n SINGULAR=False)],\n preparation=IngredientText(text='cut into 2 inch chunks', confidence=0.999193),\n comment=None,\n purpose=None,\n foundation_foods=[],\n sentence='3 pounds pork shoulder, cut into 2-inch chunks'\n)\n```\n\nRefer 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` .\n\n## Model\n\nThe 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 [Model Guide](https://ingredient-parser.readthedocs.io/en/latest/guide/index.html) in the documentation for mode details.\n\nThe model has the following accuracy on a test data set of 20% of the total data used:\n\n```\n\u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n\u2502 Sentence-level results \u2502 Word-level results \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 Accuracy: 95.27% \u2502 Accuracy: 98.10% \u2502\n\u2502 \u2502 Precision (micro) 98.08% \u2502\n\u2502 \u2502 Recall (micro) 98.10% \u2502\n\u2502 \u2502 F1 score (micro) 98.09% \u2502\n\u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n```\n\n## Development\n\n**Basic**\n\nTrain 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 [Model Guide](https://ingredient-parser.readthedocs.io/en/latest/guide/index.html) documentation.\n\n**Web App**\n\nThe 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.\n\n| Parser | Labeller | Trainer |\n| :------- | :------- | :------- |\n|  |  |  |\n\n**Documentation**\n\nThe dependencies for building the documentation are in the `requirements-doc.txt` file.\n\n**Contribution**\n\nPlease target the **develop** branch for pull requests. The **main** branch is used for stable releases and hotfixes only.\n\nBefore committing anything, install [pre-commit](https://pre-commit.com/) and run the following to install the hooks:\n\n```bash\n$ pre-commit install\n```\n\nPre-commit hooks cover both the main python library code and the web app (webtools) code.\n",
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