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Add projects to spaCy Universe (#9269)
* Added spaCy Universe projects * Added user license agreement Philip Vollet * Update website/meta/universe.json Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update website/meta/universe.json Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update website/meta/universe.json Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
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# spaCy contributor agreement
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This spaCy Contributor Agreement (**"SCA"**) is based on the
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[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
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The SCA applies to any contribution that you make to any product or project
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managed by us (the **"project"**), and sets out the intellectual property rights
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you grant to us in the contributed materials. The term **"us"** shall mean
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[ExplosionAI GmbH](https://explosion.ai/legal). The term
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**"you"** shall mean the person or entity identified below.
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If you agree to be bound by these terms, fill in the information requested
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below and include the filled-in version with your first pull request, under the
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folder [`.github/contributors/`](/.github/contributors/). The name of the file
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should be your GitHub username, with the extension `.md`. For example, the user
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example_user would create the file `.github/contributors/example_user.md`.
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Read this agreement carefully before signing. These terms and conditions
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constitute a binding legal agreement.
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## Contributor Agreement
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1. The term "contribution" or "contributed materials" means any source code,
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object code, patch, tool, sample, graphic, specification, manual,
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documentation, or any other material posted or submitted by you to the project.
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2. With respect to any worldwide copyrights, or copyright applications and
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registrations, in your contribution:
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* you hereby assign to us joint ownership, and to the extent that such
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assignment is or becomes invalid, ineffective or unenforceable, you hereby
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grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
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royalty-free, unrestricted license to exercise all rights under those
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copyrights. This includes, at our option, the right to sublicense these same
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rights to third parties through multiple levels of sublicensees or other
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licensing arrangements;
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* you agree that each of us can do all things in relation to your
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contribution as if each of us were the sole owners, and if one of us makes
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a derivative work of your contribution, the one who makes the derivative
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work (or has it made will be the sole owner of that derivative work;
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* you agree that you will not assert any moral rights in your contribution
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against us, our licensees or transferees;
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* you agree that we may register a copyright in your contribution and
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exercise all ownership rights associated with it; and
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* you agree that neither of us has any duty to consult with, obtain the
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consent of, pay or render an accounting to the other for any use or
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distribution of your contribution.
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3. With respect to any patents you own, or that you can license without payment
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to any third party, you hereby grant to us a perpetual, irrevocable,
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non-exclusive, worldwide, no-charge, royalty-free license to:
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* make, have made, use, sell, offer to sell, import, and otherwise transfer
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your contribution in whole or in part, alone or in combination with or
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included in any product, work or materials arising out of the project to
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which your contribution was submitted, and
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* at our option, to sublicense these same rights to third parties through
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multiple levels of sublicensees or other licensing arrangements.
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4. Except as set out above, you keep all right, title, and interest in your
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contribution. The rights that you grant to us under these terms are effective
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on the date you first submitted a contribution to us, even if your submission
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took place before the date you sign these terms.
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5. You covenant, represent, warrant and agree that:
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* Each contribution that you submit is and shall be an original work of
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authorship and you can legally grant the rights set out in this SCA;
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* to the best of your knowledge, each contribution will not violate any
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third party's copyrights, trademarks, patents, or other intellectual
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property rights; and
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* each contribution shall be in compliance with U.S. export control laws and
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other applicable export and import laws. You agree to notify us if you
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become aware of any circumstance which would make any of the foregoing
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representations inaccurate in any respect. We may publicly disclose your
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participation in the project, including the fact that you have signed the SCA.
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6. This SCA is governed by the laws of the State of California and applicable
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U.S. Federal law. Any choice of law rules will not apply.
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7. Please place an “x” on one of the applicable statement below. Please do NOT
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mark both statements:
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* [x] I am signing on behalf of myself as an individual and no other person
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or entity, including my employer, has or will have rights with respect to my
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contributions.
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* [ ] I am signing on behalf of my employer or a legal entity and I have the
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actual authority to contractually bind that entity.
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## Contributor Details
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| Field | Entry |
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|------------------------------- | -------------------- |
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| Name | Philip Vollet |
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| Company name (if applicable) | |
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| Title or role (if applicable) | |
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| Date | 22.09.2021 |
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| GitHub username | philipvollet |
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| Website (optional) | |
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@ -516,12 +516,12 @@
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"title": "NeuroNER",
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"slogan": "Named-entity recognition using neural networks",
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"github": "Franck-Dernoncourt/NeuroNER",
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"category": ["ner"],
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"pip": "pyneuroner[cpu]",
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"code_example": [
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"from neuroner import neuromodel",
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"nn = neuromodel.NeuroNER(train_model=False, use_pretrained_model=True)"
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],
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"category": ["ner"],
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"tags": ["standalone"]
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},
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{
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@ -1135,6 +1135,26 @@
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},
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"category": ["visualizers"]
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},
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{
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"id": "deplacy",
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"slogan": "CUI-based Tree Visualizer for Universal Dependencies and Immediate Catena Analysis",
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"discreption": "Simple dependency visualizer for [spaCy](https://spacy.io/), [UniDic2UD](https://pypi.org/project/unidic2ud), [Stanza](https://stanfordnlp.github.io/stanza/), [NLP-Cube](https://github.com/Adobe/NLP-Cube), [Trankit](https://github.com/nlp-uoregon/trankit), etc.",
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"github": "KoichiYasuoka/deplacy",
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"image": "https://i.imgur.com/6uOI4Op.png",
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"code_example": [
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"import spacy",
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"import deplacy",
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"",
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"nlp=spacy.load('en_core_web_sm')",
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"doc=nlp('I saw a horse yesterday which had no name.')",
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"deplacy.render(doc)"
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],
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"author": "Koichi Yasuoka",
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"author_links": {
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"github": "KoichiYasuoka"
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},
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"category": ["visualizers"]
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},
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{
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"id": "scattertext",
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"slogan": "Beautiful visualizations of how language differs among document types",
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@ -1679,6 +1699,22 @@
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},
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"category": ["books"]
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},
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{
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"type": "education",
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"id": "applied-nlp-in-enterprise",
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"title": "Applied Natural Language Processing in the Enterprise: Teaching Machines to Read, Write, and Understand",
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"slogan": "O'Reilly, 2021",
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"description": "Natural language processing (NLP) is one of the hottest topics in AI today. Having lagged behind other deep learning fields such as computer vision for years, NLP only recently gained mainstream popularity. Even though Google, Facebook, and OpenAI have open sourced large pretrained language models to make NLP easier, many organizations today still struggle with developing and productionizing NLP applications. This hands-on guide helps you learn the field quickly.",
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"github": "nlpbook/nlpbook",
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"cover": "https://i.imgur.com/6RxLBvf.jpg",
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"url": "https://www.amazon.com/dp/149206257X",
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"author": "Ankur A. Patel",
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"author_links": {
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"github": "aapatel09",
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"website": "https://www.ankurapatel.io"
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},
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"category": ["books"]
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},
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{
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"type": "education",
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"id": "learning-path-spacy",
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@ -1690,6 +1726,16 @@
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"author": "Aaron Kramer",
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"category": ["courses"]
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},
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{
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"type": "education",
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"id": "introduction-into-spacy-3",
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"title": "Introduction to spaCy 3",
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"slogan": "A free course for beginners by Dr. W.J.B. Mattingly",
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"url": "http://spacy.pythonhumanities.com/",
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"thumb": "https://spacy.pythonhumanities.com/_static/freecodecamp_small.jpg",
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"author": "Dr. W.J.B. Mattingly",
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"category": ["courses"]
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},
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{
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"type": "education",
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"id": "spacy-course",
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@ -2556,6 +2602,75 @@
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"website": "https://explosion.ai"
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}
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},
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{
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"id": "spacy-huggingface-hub",
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"title": "spacy-huggingface-hub",
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"slogan": "Push your spaCy pipelines to the Hugging Face Hub",
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"description": "This package provides a CLI command for uploading any trained spaCy pipeline packaged with [`spacy package`](https://spacy.io/api/cli#package) to the [Hugging Face Hub](https://huggingface.co). It auto-generates all meta information for you, uploads a pretty README (requires spaCy v3.1+) and handles version control under the hood.",
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"github": "explosion/spacy-huggingface-hub",
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"thumb": "https://i.imgur.com/j6FO9O6.jpg",
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"url": "https://github.com/explosion/spacy-huggingface-hub",
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"pip": "spacy-huggingface-hub",
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"category": ["pipeline", "models"],
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"author": "Explosion",
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"author_links": {
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"twitter": "explosion_ai",
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"github": "explosion",
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"website": "https://explosion.ai"
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}
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},
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{
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"id": "spacy-clausie",
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"title": "spacy-clausie",
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"slogan": "Implementation of the ClausIE information extraction system for Python+spaCy",
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"github": "mmxgn/spacy-clausie",
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"url": "https://github.com/mmxgn/spacy-clausie",
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"description": "ClausIE, a novel, clause-based approach to open information extraction, which extracts relations and their arguments from natural language text",
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"category": ["pipeline", "scientific", "research"],
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"code_example": [
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"import spacy",
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"import claucy",
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"",
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"nlp = spacy.load(\"en\")",
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"claucy.add_to_pipe(nlp)",
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"",
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"doc = nlp(\"AE died in Princeton in 1955.\")",
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"",
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"print(doc._.clauses)",
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"# Output:",
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"# <SV, AE, died, None, None, None, [in Princeton, in 1955]>",
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"",
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"propositions = doc._.clauses[0].to_propositions(as_text=True)",
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"",
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"print(propositions)",
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"# Output:",
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"# [AE died in Princeton in 1955, AE died in 1955, AE died in Princeton"
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],
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"author": "Emmanouil Theofanis Chourdakis",
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"author_links": {
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"github": "mmxgn"
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}
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},
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{
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"id": "ipymarkup",
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"slogan": "NER, syntax markup visualizations",
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"description": "Collection of NLP visualizations for NER and syntax tree markup. Similar to [displaCy](https://explosion.ai/demos/displacy) and [displaCy ENT](https://explosion.ai/demos/displacy-ent).",
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"github": "natasha/ipymarkup",
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"image": "https://github.com/natasha/ipymarkup/blob/master/table.png?raw=true",
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"pip":"pip install ipymarkup",
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"code_example": [
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"from ipymarkup import show_span_ascii_markup, show_dep_ascii_markup",
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"",
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"text = 'В мероприятии примут участие не только российские учёные, но и зарубежные исследователи, в том числе, Крис Хелмбрехт - управляющий директор и совладелец креативного агентства Kollektiv (Германия, США), Ннека Угбома - руководитель проекта Mushroom works (Великобритания), Гергей Ковач - политик и лидер субкультурной партии «Dog with two tails» (Венгрия), Георг Жено - немецкий режиссёр, один из создателей экспериментального театра «Театр.doc», Театра им. Йозефа Бойса (Германия).'",
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"spans = [(102, 116, 'PER'), (186, 194, 'LOC'), (196, 199, 'LOC'), (202, 214, 'PER'), (254, 268, 'LOC'), (271, 283, 'PER'), (324, 342, 'ORG'), (345, 352, 'LOC'), (355, 365, 'PER'), (445, 455, 'ORG'), (456, 468, 'PER'), (470, 478, 'LOC')]",
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"show_span_ascii_markup(text, spans)"
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],
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"author": "Alexander Kukushkin",
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"author_links": {
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"github": "kuk"
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},
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"category": ["visualizers"]
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},
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{
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"id": "negspacy",
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"title": "negspaCy",
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