# lambeq
[](//cqcl.github.io/lambeq)
[](LICENSE)
[](//pypi.org/project/lambeq)
[](//pypi.org/project/lambeq)
[](//arxiv.org/abs/2110.04236)
## About
lambeq is a toolkit for quantum natural language processing (QNLP).
- Documentation: https://cqcl.github.io/lambeq/
- User support: <lambeq-support@cambridgequantum.com>
- Contributions: Please read [our guide](https://cqcl.github.io/lambeq/CONTRIBUTING.html).
- If you want to subscribe to lambeq's mailing list, let us know by sending an email to <lambeq-support@cambridgequantum.com>.
---
**Note:** Please do not try to read the documentation directly from the preview provided in the [repository](https://github.com/CQCL/lambeq/tree/main/docs), since some of the pages will not be rendered properly.
---
## Getting started
### Prerequisites
- Python 3.9+
### Installation
lambeq can be installed with the command:
```bash
pip install lambeq
```
The default installation of lambeq includes Bobcat parser, a state-of-the-art statistical parser (see [related paper](https://arxiv.org/abs/2109.10044)) fully integrated with the toolkit.
To install lambeq with optional dependencies for extra features, run:
```bash
pip install lambeq[extras]
```
To enable DepCCG support, you will need to install the external parser separately.
---
**Note:** The DepCCG-related functionality is no longer actively supported in `lambeq`, and may not work as expected. We strongly recommend using the default Bobcat parser which comes as part of `lambeq`.
---
If you still want to use DepCCG, for example because you plan to apply ``lambeq`` on Japanese, you can install DepCCG separately following the instructions on the [DepCCG homepage](//github.com/masashi-y/depccg). After installing DepCCG, you can download its model by using the script provided in the `contrib` folder of this repository:
```bash
python contrib/download_depccg_model.py
```
## Usage
The [docs/examples](//github.com/CQCL/lambeq/tree/main/docs/examples)
directory contains notebooks demonstrating usage of the various tools in
lambeq.
Example - parsing a sentence into a diagram (see
[docs/examples/ccg2discocat.ipynb](//github.com/CQCL/lambeq/blob/main/docs/examples/ccg2discocat.ipynb)):
```python
from lambeq import BobcatParser
parser = BobcatParser()
diagram = parser.sentence2diagram('This is a test sentence')
diagram.draw()
```
## Testing
Run all tests with the command:
```bash
pytest
```
Note: if you have installed in a virtual environment, remember to
install pytest in the same environment using pip.
## Building documentation
To build the documentation, first install the required dependencies:
```bash
pip install -r docs/requirements.txt
```
then run the commands:
```bash
cd docs
make clean
make html
```
the docs will be under `docs/_build`.
## License
Distributed under the Apache 2.0 license. See [`LICENSE`](LICENSE) for
more details.
## Citation
If you wish to attribute our work, please cite
[the accompanying paper](//arxiv.org/abs/2110.04236):
```
@article{kartsaklis2021lambeq,
title={lambeq: {A}n {E}fficient {H}igh-{L}evel {P}ython {L}ibrary for {Q}uantum {NLP}},
author={Dimitri Kartsaklis and Ian Fan and Richie Yeung and Anna Pearson and Robin Lorenz and Alexis Toumi and Giovanni de Felice and Konstantinos Meichanetzidis and Stephen Clark and Bob Coecke},
year={2021},
journal={arXiv preprint arXiv:2110.04236},
}
```
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