{"id":13766527,"url":"https://github.com/CQCL/lambeq","last_synced_at":"2025-05-10T22:31:11.758Z","repository":{"id":37010871,"uuid":"415642057","full_name":"CQCL/lambeq","owner":"CQCL","description":"A high-level Python library for Quantum Natural Language Processing","archived":false,"fork":false,"pushed_at":"2025-04-11T14:14:20.000Z","size":34126,"stargazers_count":477,"open_issues_count":25,"forks_count":118,"subscribers_count":30,"default_branch":"main","last_synced_at":"2025-04-14T20:57:59.573Z","etag":null,"topics":["python","qnlp"],"latest_commit_sha":null,"homepage":"https://docs.quantinuum.com/lambeq/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/CQCL.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2021-10-10T16:38:24.000Z","updated_at":"2025-04-12T05:35:51.000Z","dependencies_parsed_at":"2024-01-11T18:09:53.904Z","dependency_job_id":"d6448f3f-d06e-4b6c-842a-2dcac741d59c","html_url":"https://github.com/CQCL/lambeq","commit_stats":{"total_commits":31,"total_committers":8,"mean_commits":3.875,"dds":0.6451612903225806,"last_synced_commit":"70a1fe8490741f98893d8eb2371a617aa0e0b381"},"previous_names":[],"tags_count":20,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CQCL%2Flambeq","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CQCL%2Flambeq/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CQCL%2Flambeq/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CQCL%2Flambeq/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/CQCL","download_url":"https://codeload.github.com/CQCL/lambeq/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253492529,"owners_count":21916959,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["python","qnlp"],"created_at":"2024-08-03T16:00:56.839Z","updated_at":"2025-05-10T22:31:11.751Z","avatar_url":"https://github.com/CQCL.png","language":"Python","readme":"# λambeq\n\n![Build status](https://github.com/CQCL/lambeq/actions/workflows/build_test.yml/badge.svg)\n[![License](https://img.shields.io/github/license/CQCL/lambeq)](LICENSE)\n[![PyPI version](https://img.shields.io/pypi/v/lambeq)](//pypi.org/project/lambeq)\n[![PyPI downloads](https://img.shields.io/pypi/dm/lambeq)](//pypi.org/project/lambeq)\n[![arXiv](https://img.shields.io/badge/arXiv-2110.04236-green)](//arxiv.org/abs/2110.04236)\n\n## About\n\nlambeq is a toolkit for quantum natural language processing (QNLP).\n\n- Documentation: https://docs.quantinuum.com/lambeq/.\n- User support: \u003clambeq-support@quantinuum.com\u003e.\n- Contributions: Please read [our guide](https://docs.quantinuum.com/lambeq/CONTRIBUTING.html).\n- If you want to subscribe to lambeq's mailing list, let us know by sending an email to \u003clambeq-support@quantinuum.com\u003e.\n\n## Getting started\n\n### Prerequisites\n\n- Python 3.10+\n\n### Installation\n\nlambeq can be installed with the command:\n\n```bash\npip install lambeq\n```\n\nThe 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.\n\nTo install lambeq with optional dependencies for extra features, run:\n\n```bash\npip install lambeq[extras]\n```\n\nTo install lambeq with optional dependencies for experimental features, run:\n\n```bash\npip install lambeq[experimental]\n```\n\nTo enable DepCCG support, you will need to install the external parser separately.\n\n---\n**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`.\n\n---\n\nIf 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:\n\n```bash\npython contrib/download_depccg_model.py\n```\n\n## Usage\n\nThe [docs/examples](//github.com/CQCL/lambeq-docs/tree/main/docs/examples)\ndirectory in lambeq's [documentation repository](https://github.com/CQCL/lambeq-docs) contains notebooks demonstrating usage of the various tools in lambeq.\n\nExample - parsing a sentence into a diagram (see\n[docs/examples/parser.ipynb](//github.com/CQCL/lambeq-docs/blob/main/docs/examples/parser.ipynb)):\n\n```python\nfrom lambeq import BobcatParser\n\nparser = BobcatParser()\ndiagram = parser.sentence2diagram('This is a test sentence')\ndiagram.draw()\n```\n\n## Testing\n\nRun all tests with the command:\n\n```bash\npytest\n```\n\nNote: if you have installed lambeq in a virtual environment, remember to\ninstall pytest in the same environment using pip.\n\n## License\n\nDistributed under the Apache 2.0 license. See [`LICENSE`](LICENSE) for\nmore details.\n\n## Citation\n\nIf you wish to attribute our work, please cite\n[the accompanying paper](//arxiv.org/abs/2110.04236):\n\n```\n@article{kartsaklis2021lambeq,\n   title={lambeq: {A}n {E}fficient {H}igh-{L}evel {P}ython {L}ibrary for {Q}uantum {NLP}},\n   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},\n   year={2021},\n   journal={arXiv preprint arXiv:2110.04236},\n}\n```\n","funding_links":[],"categories":["Programming language","Toolboxes"],"sub_categories":["Python","Quantum Tensor Simulation"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FCQCL%2Flambeq","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FCQCL%2Flambeq","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FCQCL%2Flambeq/lists"}