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universe.json
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universe.json
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{
"resources": [
{
"id": "spacy-vscode",
"title": "spaCy Visual Studio Code Extension",
"thumb": "https://raw.githubusercontent.com/explosion/spacy-vscode/main/icon.png",
"slogan": "Work with spaCy's config files in VS Code",
"description": "The spaCy VS Code Extension provides additional tooling and features for working with spaCy's config files. Version 1.0.0 includes hover descriptions for registry functions, variables, and section names within the config as an installable extension.",
"url": "https://marketplace.visualstudio.com/items?itemName=Explosion.spacy-extension",
"github": "explosion/spacy-vscode",
"code_language": "python",
"author": "Explosion",
"author_links": {
"twitter": "@explosion_ai",
"github": "explosion"
},
"category": [
"extension"
],
"tags": []
},
{
"id": "constituent_treelib",
"title": "Constituent Treelib",
"slogan": "Extract constituents with ease!",
"description": "Constituent Treelib (CTL) is a lightweight Python library built on top of benepar (Berkeley Neural Parser) as well as the two well-known NLP frameworks spaCy and NLTK. CTL offers you a convenient way to parse sentences into constituent trees, modify them according to their structure, as well as visualize and export them into various file formats. In addition, you can extract phrases according to their phrasal categories (which can be used e.g., as features for various NLP tasks), validate already parsed sentences in bracket notation or convert them back into sentences.",
"github": "Halvani/Constituent-Treelib",
"pip": "constituent-treelib",
"code_example": [
"from constituent_treelib import ConstituentTree, Language",
"# Define the language for the sentence as well as for the spaCy and benepar models",
"language = Language.English",
"# Define which specific SpaCy model should be used (default is Medium)",
"spacy_model_size = ConstituentTree.SpacyModelSize.Medium",
"# Create the pipeline (note, the required models will be downloaded and installed automatically)",
"nlp = ConstituentTree.create_pipeline(language, spacy_model_size)",
"# Your sentence",
"sentence = 'We try to explicitly describe the geometry of the edges of the images.'",
"# Create the tree from where we are going to extract the desired noun phrases",
"tree = ConstituentTree(sentence, nlp)",
"all_phrases = tree.extract_all_phrases(min_words_in_phrases=1)",
"print(all_phrases)",
"# {'PP': ['of the edges of the images', 'of the images'], 'NP': ['We', 'the geometry of the edges of the images', 'the geometry', 'the edges of the images', 'the edges', 'the images'], 'S': ['We try to explicitly describe the geometry of the edges of the images .', 'to explicitly describe the geometry of the edges of the images'], 'VP': ['try to explicitly describe the geometry of the edges of the images', 'to explicitly describe the geometry of the edges of the images', 'describe the geometry of the edges of the images'], 'ADVP': ['explicitly']}"
],
"code_language": "python",
"url": "https://github.com/Halvani/Constituent-Treelib",
"thumb": "https://github.com/Halvani/Constituent-Treelib/blob/main/assets/images/promo_tree.svg",
"author": "Oren Halvani",
"author_links": {
"github": "Halvani",
"website": "https://www.linkedin.com/in/orenhalvani"
},
"category": [
"apis",
"standalone",
"visualizers"
],
"tags": [
"apis",
"deployment",
"constituency ",
"parsing"
]
},
{
"id": "sayswho",
"title": "SaysWho",
"slogan": "Quote identification, attribution and resolution",
"description": "A Python package for identifying and attributing quotes in text. It uses a combination of spaCy functionality, logic and grammar to find quotes and their speakers, then uses the spaCy coreferencing model to better clarify who is speaking. Currently English only.",
"github": "afriedman412/sayswho",
"pip": "sayswho",
"code_language": "python",
"author": "Andy Friedman",
"author_links": {
"twitter": "@steadynappin",
"github": "afriedman412"
},
"code_example": [
"from sayswho import SaysWho",
"text = open(\"path/to/your/text_file.txt\").read()",
"sw = SaysWho()",
"sw.attribute(text)",
"sw.expand_match() # see quote/cluster matches",
"sw.render_to_html() # output your text, quotes and cluster matches to an html file called \"temp.html\""
],
"category": [
"standalone"
],
"tags": [
"attribution",
"coref",
"text-processing"
]
},
{
"id": "parsigs",
"title": "parsigs",
"slogan": "Structuring prescriptions text made simple using spaCy",
"description": "Parsigs is an open-source project that aims to extract the relevant dosage information from prescriptions text without compromising the patient's privacy.\n\nNotice you also need to install the model in order to use the package: `pip install https://huggingface.co/royashcenazi/en_parsigs/resolve/main/en_parsigs-any-py3-none-any.whl`",
"github": "royashcenazi/parsigs",
"pip": "parsigs",
"code_language": "python",
"author": "Roy Ashcenazi",
"code_example": [
"# You'll need to install the trained model, see instructions in the description section",
"from parsigs.parse_sig_api import StructuredSig, SigParser",
"sig_parser = SigParser()",
"",
"sig = 'Take 1 tablet of ibuprofen 200mg 3 times every day for 3 weeks'",
"parsed_sig = sig_parser.parse(sig)"
],
"author_links": {
"github": "royashcenazi"
},
"category": [
"model",
"research",
"biomedical"
],
"tags": [
"sigs",
"prescription",
"pharma"
]
},
{
"id": "latincy",
"title": "LatinCy",
"thumb": "https://raw.githubusercontent.com/diyclassics/la_core_web_lg/main/latincy-logo.png",
"slogan": "Synthetic trained spaCy pipelines for Latin NLP",
"description": "Set of trained general purpose Latin-language 'core' pipelines for use with spaCy. The models are trained on a large amount of available Latin data, including all five of the Latin Universal Dependency treebanks, which have been preprocessed to be compatible with each other.",
"url": "https://huggingface.co/latincy",
"code_example": [
"# pip install https://huggingface.co/latincy/la_core_web_lg/resolve/main/la_core_web_lg-any-py3-none-any.whl",
"import spacy",
"nlp = spacy.load('la_core_web_lg')",
"doc = nlp('Haec narrantur a poetis de Perseo')",
"",
"print(f'{doc[0].text}, {doc[0].norm_}, {doc[0].lemma_}, {doc[0].pos_}')",
"",
"# > Haec, haec, hic, DET"
],
"code_language": "python",
"author": "Patrick J. Burns",
"author_links": {
"twitter": "@diyclassics",
"github": "diyclassics",
"website": "https://diyclassics.github.io/"
},
"category": [
"pipeline",
"research"
],
"tags": [
"latin"
]
},
{
"id": "odycy",
"title": "OdyCy",
"slogan": "General-purpose language pipelines for premodern Greek.",
"description": "Academically validated modular NLP pipelines for premodern Greek. odyCy achieves state of the art performance on multiple tasks on unseen test data from the Universal Dependencies Perseus treebank, and performs second best on the PROIEL treebank’s test set on even more tasks. In addition performance also seems relatively stable across the two evaluation datasets in comparison with other NLP pipelines. OdyCy is being used at the Center for Humanities Computing for preprocessing and analyzing Ancient Greek corpora for New Testament research, meaning that you can expect consistent maintenance and improvements.",
"github": "centre-for-humanities-computing/odyCy",
"code_example": [
"# To install the high-accuracy transformer-based pipeline",
"# pip install https://huggingface.co/chcaa/grc_odycy_joint_trf/resolve/main/grc_odycy_joint_trf-any-py3-none-any.whl",
"import spacy",
"",
"nlp = spacy.load('grc_odycy_joint_trf')",
"",
"doc = nlp('τὴν γοῦν Ἀττικὴν ἐκ τοῦ ἐπὶ πλεῖστον διὰ τὸ λεπτόγεων ἀστασίαστον οὖσαν ἄνθρωποι ᾤκουν οἱ αὐτοὶ αἰεί.')"
],
"code_language": "python",
"url": "https://centre-for-humanities-computing.github.io/odyCy/",
"thumb": "https://raw.githubusercontent.com/centre-for-humanities-computing/odyCy/7b94fec60679d06272dca88a4dcfe0f329779aea/docs/_static/logo.svg",
"image": "https://github.com/centre-for-humanities-computing/odyCy/raw/main/docs/_static/logo_with_text_below.svg",
"author": "Jan Kostkan, Márton Kardos (Center for Humanities Computing, Aarhus University)",
"author_links": {
"github": "centre-for-humanities-computing",
"website": "https://chc.au.dk/"
},
"category": [
"pipeline",
"standalone",
"research"
],
"tags": [
"ancient Greek"
]
},
{
"id": "spacy-wasm",
"title": "spacy-wasm",
"slogan": "spaCy in the browser using WebAssembly",
"description": "Run spaCy directly in the browser with WebAssembly. Using Pyodide, the application loads the spaCy model and renders the text prompt with displaCy.",
"url": "https://spacy-wasm.vercel.app/",
"github": "SyedAhkam/spacy-wasm",
"code_language": "python",
"author": "Syed Ahkam",
"author_links": {
"twitter": "@SyedAhkam1",
"github": "SyedAhkam"
},
"category": [
"visualizers"
],
"tags": [
"visualization",
"deployment"
]
},
{
"id": "spacysee",
"title": "spaCysee",
"slogan": "Visualize spaCy's Dependency Parsing, POS tagging, and morphological analysis",
"description": "A project that helps you visualize your spaCy docs in Jupyter notebooks. Each of the dependency tags, POS tags and morphological features are clickable. Clicking on a tag will bring up the relevant documentation for that tag.",
"github": "moxley01/spacysee",
"pip": "spacysee",
"code_example": [
"import spacy",
"from spacysee import render",
"",
"nlp = spacy.load('en_core_web_sm')",
"doc = nlp('This is a neat way to visualize your spaCy docs')",
"render(doc, width='500', height='500')"
],
"code_language": "python",
"thumb": "https://www.mattoxley.com/static/images/spacysee_logo.svg",
"image": "https://www.mattoxley.com/static/images/spacysee_logo.svg",
"author": "Matt Oxley",
"author_links": {
"twitter": "matt0xley",
"github": "moxley01",
"website": "https://mattoxley.com"
},
"category": [
"visualizers"
],
"tags": [
"visualization"
]
},
{
"id": "grecy",
"title": "greCy",
"slogan": "Ancient Greek pipelines for spaCy",
"description": "greCy offers state-of-the-art pipelines for ancient Greek NLP. It installs language models available in various sizes, some of them containing either word vectors or the aristoBERTo transformer.",
"github": "jmyerston/greCy",
"pip": "grecy",
"code_example": [
"python -m grecy install grc_proiel_trf",
"",
"#After installing grc_proiel_trf or any other model",
"import spacy",
"",
"nlp = spacy.load('grc_proiel_trf')",
"doc = nlp('δοκῶ μοι περὶ ὧν πυνθάνεσθε οὐκ ἀμελέτητος εἶναι')",
"",
"for token in doc:",
" print(f'{token.text}, lemma: {token.lemma_}, pos: {token.pos_}, dep: {token.dep_}')"
],
"code_language": "python",
"thumb": "https://jacobo-syntax.hf.space/media/03a5317fa660c142e41dd2870b4273ce4e668e6fcdee0a276891f563.png",
"author": "Jacobo Myerston",
"author_links": {
"twitter": "@jcbmyrstn",
"github": "jmyerston",
"website": "https://huggingface.co/spaces/Jacobo/syntax"
},
"category": [
"pipeline",
"research",
"models"
],
"tags": [
"ancient Greek"
]
},
{
"id": "solipcysme",
"title": "solipCysme",
"slogan": "spaCy pipeline for french fictions and first person point of view texts.",
"description": "__solipCysme__ is a pipeline for french language, designed for the analysis of fictions and first person point of view texts, with a focus on personal pronouns.",
"github": "thjbdvlt/solipCysme",
"code_example": [
"pip install https://huggingface.co/thjbdvlt/fr_solipcysme/resolve/main/fr_solipcysme-any-py3-none-any.whl",
"",
"import spacy",
"",
"nlp = spacy.load('fr_solipcysme')",
"for i in nlp(",
"'la MACHINE à (b)rouiller le temps s'est peut-être déraillée..?'",
"):",
" print(",
" i, ",
" i.norm_, ",
" i.pos_, ",
" i.morph, ",
" i.lemma_, ",
" i.dep_, ",
" i._.tokentype,",
" i._.vv_pos,",
" i._.vv_morph",
" )"
],
"code_language": "python",
"author": "thjbdvlt",
"author_links": {
"github": "thjbdvlt"
},
"category": [
"pipeline",
"research",
"models"
],
"tags": [
"french"
]
},
{
"id": "spacy-cleaner",
"title": "spacy-cleaner",
"slogan": "Easily clean text with spaCy!",
"description": "**spacy-cleaner** utilises spaCy `Language` models to replace, remove, and \n mutate spaCy tokens. Cleaning actions available are:\n\n* Remove/replace stopwords.\n* Remove/replace punctuation.\n* Remove/replace numbers.\n* Remove/replace emails.\n* Remove/replace URLs.\n* Perform lemmatisation.\n\nSee our [docs](https://ce11an.github.io/spacy-cleaner/) for more information.",
"github": "Ce11an/spacy-cleaner",
"pip": "spacy-cleaner",
"code_example": [
"import spacy",
"import spacy_cleaner",
"from spacy_cleaner.processing import removers, replacers, mutators",
"",
"model = spacy.load(\"en_core_web_sm\")",
"pipeline = spacy_cleaner.Pipeline(",
" model,",
" removers.remove_stopword_token,",
" replacers.replace_punctuation_token,",
" mutators.mutate_lemma_token,",
")",
"",
"texts = [\"Hello, my name is Cellan! I love to swim!\"]",
"",
"pipeline.clean(texts)",
"# ['hello _IS_PUNCT_ Cellan _IS_PUNCT_ love swim _IS_PUNCT_']"
],
"code_language": "python",
"url": "https://ce11an.github.io/spacy-cleaner/",
"image": "https://raw.githubusercontent.com/Ce11an/spacy-cleaner/main/docs/assets/images/spacemen.png",
"author": "Cellan Hall",
"author_links": {
"twitter": "Ce11an",
"github": "Ce11an",
"website": "https://www.linkedin.com/in/cellan-hall/"
},
"category": [
"extension"
],
"tags": [
"text-processing"
]
},
{
"id": "Zshot",
"title": "Zshot",
"slogan": "Zero and Few shot named entity & relationships recognition",
"github": "ibm/zshot",
"pip": "zshot",
"code_example": [
"import spacy",
"from zshot import PipelineConfig, displacy",
"from zshot.linker import LinkerRegen",
"from zshot.mentions_extractor import MentionsExtractorSpacy",
"from zshot.utils.data_models import Entity",
"",
"nlp = spacy.load('en_core_web_sm')",
"# zero shot definition of entities",
"nlp_config = PipelineConfig(",
" mentions_extractor=MentionsExtractorSpacy(),",
" linker=LinkerRegen(),",
" entities=[",
" Entity(name='Paris',",
" description='Paris is located in northern central France, in a north-bending arc of the river Seine'),",
" Entity(name='IBM',",
" description='International Business Machines Corporation (IBM) is an American multinational technology corporation headquartered in Armonk, New York'),",
" Entity(name='New York', description='New York is a city in U.S. state'),",
" Entity(name='Florida', description='southeasternmost U.S. state'),",
" Entity(name='American',",
" description='American, something of, from, or related to the United States of America, commonly known as the United States or America'),",
" Entity(name='Chemical formula',",
" description='In chemistry, a chemical formula is a way of presenting information about the chemical proportions of atoms that constitute a particular chemical compound or molecul'),",
" Entity(name='Acetamide',",
" description='Acetamide (systematic name: ethanamide) is an organic compound with the formula CH3CONH2. It is the simplest amide derived from acetic acid. It finds some use as a plasticizer and as an industrial solvent.'),",
" Entity(name='Armonk',",
" description='Armonk is a hamlet and census-designated place (CDP) in the town of North Castle, located in Westchester County, New York, United States.'),",
" Entity(name='Acetic Acid',",
" description='Acetic acid, systematically named ethanoic acid, is an acidic, colourless liquid and organic compound with the chemical formula CH3COOH'),",
" Entity(name='Industrial solvent',",
" description='Acetamide (systematic name: ethanamide) is an organic compound with the formula CH3CONH2. It is the simplest amide derived from acetic acid. It finds some use as a plasticizer and as an industrial solvent.'),",
" ]",
")",
"nlp.add_pipe('zshot', config=nlp_config, last=True)",
"",
"text = 'International Business Machines Corporation (IBM) is an American multinational technology corporation' \\",
" ' headquartered in Armonk, New York, with operations in over 171 countries.'",
"",
"doc = nlp(text)",
"displacy.serve(doc, style='ent')"
],
"thumb": "https://ibm.github.io/zshot/img/graph.png",
"url": "https://ibm.github.io/zshot/",
"author": "IBM Research",
"author_links": {
"github": "ibm",
"twitter": "IBMResearch",
"website": "https://research.ibm.com/labs/ireland/"
},
"category": [
"scientific",
"models",
"research"
]
},
{
"id": "concepcy",
"title": "concepCy",
"slogan": "A multilingual knowledge graph in spaCy",
"description": "A spaCy wrapper for ConceptNet, a freely-available semantic network designed to help computers understand the meaning of words.",
"github": "JulesBelveze/concepcy",
"pip": "concepcy",
"code_example": [
"import spacy",
"import concepcy",
"",
"nlp = spacy.load('en_core_web_sm')",
"# Using default concepCy configuration",
"nlp.add_pipe('concepcy')",
"",
"doc = nlp('WHO is a lovely company')",
"",
"# Access all the 'RelatedTo' relations from the Doc",
"for word, relations in doc._.relatedto.items():",
" print(f'Word: {word}\n{relations}')",
"",
"# Access the 'RelatedTo' relations word by word",
"for token in doc:",
" print(f'Word: {token}\n{token._.relatedto}')"
],
"category": [
"pipeline"
],
"image": "https://github.com/JulesBelveze/concepcy/blob/main/figures/concepcy.png",
"tags": [
"semantic",
"ConceptNet"
],
"author": "Jules Belveze",
"author_links": {
"github": "JulesBelveze",
"website": "https://www.linkedin.com/in/jules-belveze/"
}
},
{
"id": "spacyfishing",
"title": "spaCy fishing",
"slogan": "Named entity disambiguation and linking on Wikidata in spaCy with Entity-Fishing.",
"description": "A spaCy wrapper of Entity-Fishing for named entity disambiguation and linking against a Wikidata knowledge base.",
"github": "Lucaterre/spacyfishing",
"pip": "spacyfishing",
"code_example": [
"import spacy",
"text = 'Victor Hugo and Honoré de Balzac are French writers who lived in Paris.'",
"nlp = spacy.load('en_core_web_sm')",
"nlp.add_pipe('entityfishing')",
"doc = nlp(text)",
"for span in doc.ents:",
" print((ent.text, ent.label_, ent._.kb_qid, ent._.url_wikidata, ent._.nerd_score))",
"# ('Victor Hugo', 'PERSON', 'Q535', 'https://www.wikidata.org/wiki/Q535', 0.972)",
"# ('Honoré de Balzac', 'PERSON', 'Q9711', 'https://www.wikidata.org/wiki/Q9711', 0.9724)",
"# ('French', 'NORP', 'Q121842', 'https://www.wikidata.org/wiki/Q121842', 0.3739)",
"# ('Paris', 'GPE', 'Q90', 'https://www.wikidata.org/wiki/Q90', 0.5652)",
"## Set parameter `extra_info` to `True` and check also span._.description, span._.src_description, span._.normal_term, span._.other_ids"
],
"category": [
"models",
"pipeline"
],
"image": "https://raw.githubusercontent.com/Lucaterre/spacyfishing/main/docs/spacyfishing-logo-resized.png",
"tags": [
"NER",
"NEL"
],
"author": "Lucas Terriel",
"author_links": {
"twitter": "TerreLuca",
"github": "Lucaterre"
}
},
{
"id": "aim-spacy",
"title": "Aim-spaCy",
"slogan": "Aim-spaCy is an Aim-based spaCy experiment tracker.",
"description": "Aim-spaCy helps to easily collect, store and explore training logs for spaCy, including: hyper-parameters, metrics and displaCy visualizations",
"github": "aimhubio/aim-spacy",
"pip": "aim-spacy",
"code_example": [
"https://github.com/aimhubio/aim-spacy/tree/master/examples"
],
"code_language": "python",
"url": "https://aimstack.io/spacy",
"thumb": "https://user-images.githubusercontent.com/13848158/172912427-ee9327ea-3cd8-47fa-8427-6c0d36cd831f.png",
"image": "https://user-images.githubusercontent.com/13848158/136364717-0939222c-55b6-44f0-ad32-d9ab749546e4.png",
"author": "AimStack",
"author_links": {
"twitter": "aimstackio",
"github": "aimhubio",
"website": "https://aimstack.io"
},
"category": [
"visualizers"
],
"tags": [
"experiment-tracking",
"visualization"
]
},
{
"id": "spacy-report",
"title": "spacy-report",
"slogan": "Generates interactive reports for spaCy models.",
"description": "The goal of spacy-report is to offer static reports for spaCy models that help users make better decisions on how the models can be used.",
"github": "koaning/spacy-report",
"pip": "spacy-report",
"thumb": "https://github.com/koaning/spacy-report/raw/main/icon.png",
"image": "https://raw.githubusercontent.com/koaning/spacy-report/main/gif.gif",
"code_example": [
"python -m spacy report textcat training/model-best/ corpus/train.spacy corpus/dev.spacy"
],
"category": [
"visualizers",
"research"
],
"author": "Vincent D. Warmerdam",
"author_links": {
"twitter": "fishnets88",
"github": "koaning",
"website": "https://koaning.io"
}
},
{
"id": "scrubadub_spacy",
"title": "scrubadub_spacy",
"category": [
"pipeline"
],
"slogan": "Remove personally identifiable information from text using spaCy.",
"description": "scrubadub removes personally identifiable information from text. scrubadub_spacy is an extension that uses spaCy NLP models to remove personal information from text.",
"github": "LeapBeyond/scrubadub_spacy",
"pip": "scrubadub-spacy",
"url": "https://github.com/LeapBeyond/scrubadub_spacy",
"code_language": "python",
"author": "Leap Beyond",
"author_links": {
"github": "LeapBeyond",
"website": "https://leapbeyond.ai"
},
"code_example": [
"import scrubadub, scrubadub_spacy",
"scrubber = scrubadub.Scrubber()",
"scrubber.add_detector(scrubadub_spacy.detectors.SpacyEntityDetector)",
"print(scrubber.clean(\"My name is Alex, I work at LifeGuard in London, and my eMail is alex@lifeguard.com btw. my super secret twitter login is username: alex_2000 password: g-dragon180888\"))",
"# My name is {{NAME}}, I work at {{ORGANIZATION}} in {{LOCATION}}, and my eMail is {{EMAIL}} btw. my super secret twitter login is username: {{USERNAME}} password: {{PASSWORD}}"
]
},
{
"id": "spacy-setfit-textcat",
"title": "spacy-setfit-textcat",
"category": [
"research"
],
"tags": [
"SetFit",
"Few-Shot"
],
"slogan": "spaCy Project: Experiments with SetFit & Few-Shot Classification",
"description": "This project is an experiment with spaCy and few-shot text classification using SetFit",
"github": "pmbaumgartner/spacy-setfit-textcat",
"url": "https://github.com/pmbaumgartner/spacy-setfit-textcat",
"code_language": "python",
"author": "Peter Baumgartner",
"author_links": {
"twitter": "pmbaumgartner",
"github": "pmbaumgartner",
"website": "https://www.peterbaumgartner.com/"
},
"code_example": [
"https://colab.research.google.com/drive/1CvGEZC0I9_v8gWrBxSJQ4Z8JGPJz-HYb?usp=sharing"
]
},
{
"id": "spacy-experimental",
"title": "spacy-experimental",
"category": [
"extension"
],
"slogan": "Cutting-edge experimental spaCy components and features",
"description": "This package includes experimental components and features for spaCy v3.x, for example model architectures, pipeline components and utilities.",
"github": "explosion/spacy-experimental",
"pip": "spacy-experimental",
"url": "https://github.com/explosion/spacy-experimental",
"code_language": "python",
"author": "Explosion",
"author_links": {
"twitter": "explosion_ai",
"github": "explosion",
"website": "https://explosion.ai/"
},
"code_example": [
"python -m pip install -U pip setuptools wheel",
"python -m pip install spacy-experimental"
]
},
{
"id": "spacypdfreader",
"title": "spacypdfreader",
"category": [
"pipeline"
],
"tags": [
"PDF"
],
"slogan": "Easy PDF to text to spaCy text extraction in Python.",
"description": "*spacypdfreader* is a Python library that allows you to convert PDF files directly into *spaCy* `Doc` objects. The library provides several built in parsers or bring your own parser. `Doc` objects are annotated with several custom attributes including: `token._.page_number`, `doc._.page_range`, `doc._.first_page`, `doc._.last_page`, `doc._.pdf_file_name`, and `doc._.page(int)`.",
"github": "SamEdwardes/spacypdfreader",
"pip": "spacypdfreader",
"url": "https://samedwardes.github.io/spacypdfreader/",
"code_language": "python",
"author": "Sam Edwardes",
"author_links": {
"twitter": "TheReaLSamlam",
"github": "SamEdwardes",
"website": "https://samedwardes.com"
},
"code_example": [
"import spacy",
"from spacypdfreader.spacypdfreader import pdf_reader",
"",
"nlp = spacy.load('en_core_web_sm')",
"doc = pdf_reader('tests/data/test_pdf_01.pdf', nlp)",
"",
"# Get the page number of any token.",
"print(doc[0]._.page_number) # 1",
"print(doc[-1]._.page_number) # 4",
"",
"# Get page meta data about the PDF document.",
"print(doc._.pdf_file_name) # 'tests/data/test_pdf_01.pdf'",
"print(doc._.page_range) # (1, 4)",
"print(doc._.first_page) # 1",
"print(doc._.last_page) # 4",
"",
"# Get all of the text from a specific PDF page.",
"print(doc._.page(4)) # 'able to display the destination page (unless...'"
]
},
{
"id": "nlpcloud",
"title": "NLPCloud.io",
"slogan": "Production-ready API for spaCy models in production",
"description": "A highly-available hosted API to easily deploy and use spaCy models in production. Supports NER, POS tagging, dependency parsing, and tokenization.",
"github": "nlpcloud",
"pip": "nlpcloud",
"code_example": [
"import nlpcloud",
"",
"client = nlpcloud.Client('en_core_web_lg', '4eC39HqLyjWDarjtT1zdp7dc')",
"client.entities('John Doe is a Go Developer at Google')",
"# [{'end': 8, 'start': 0, 'text': 'John Doe', 'type': 'PERSON'}, {'end': 25, 'start': 13, 'text': 'Go Developer', 'type': 'POSITION'}, {'end': 35,'start': 30, 'text': 'Google', 'type': 'ORG'}]"
],
"thumb": "https://avatars.githubusercontent.com/u/77671902",
"image": "https://nlpcloud.io/assets/images/logo.svg",
"code_language": "python",
"author": "NLPCloud.io",
"author_links": {
"github": "nlpcloud",
"twitter": "cloud_nlp",
"website": "https://nlpcloud.io"
},
"category": [
"apis",
"nonpython",
"standalone"
],
"tags": [
"api",
"deploy",
"production"
]
},
{
"id": "eMFDscore",
"title": "eMFDscore : Extended Moral Foundation Dictionary Scoring for Python",
"slogan": "Extended Moral Foundation Dictionary Scoring for Python",
"description": "eMFDscore is a library for the fast and flexible extraction of various moral information metrics from textual input data. eMFDscore is built on spaCy for faster execution and performs minimal preprocessing consisting of tokenization, syntactic dependency parsing, lower-casing, and stopword/punctuation/whitespace removal. eMFDscore lets users score documents with multiple Moral Foundations Dictionaries, provides various metrics for analyzing moral information, and extracts moral patient, agent, and attribute words related to entities.",
"github": "medianeuroscience/emfdscore",
"code_example": [
"from emfdscore.scoring import score_docs",
"import pandas as pd",
"template_input = pd.read_csv('emfdscore/template_input.csv', header=None)",
"DICT_TYPE = 'emfd'",
"PROB_MAP = 'single'",
"SCORE_METHOD = 'bow'",
"OUT_METRICS = 'vice-virtue'",
"OUT_CSV_PATH = 'single-vv.csv'",
"df = score_docs(template_input,DICT_TYPE,PROB_MAP,SCORE_METHOD,OUT_METRICS,num_docs)"
],
"code_language": "python",
"author": "Media Neuroscience Lab",
"author_links": {
"github": "medianeuroscience",
"twitter": "medianeuro"
},
"category": [
"research",
"teaching"
],
"tags": [
"morality",
"dictionary",
"sentiment"
]
},
{
"id": "skweak",
"title": "skweak",
"slogan": "Weak supervision for NLP",
"description": "`skweak` brings the power of weak supervision to NLP tasks, and in particular sequence labelling and text classification. Instead of annotating documents by hand, `skweak` allows you to define *labelling functions* to automatically label your documents, and then aggregate their results using a statistical model that estimates the accuracy and confusions of each labelling function.",
"github": "NorskRegnesentral/skweak",
"pip": "skweak",
"code_example": [
"import spacy, re",
"from skweak import heuristics, gazetteers, aggregation, utils",
"",
"# LF 1: heuristic to detect occurrences of MONEY entities",
"def money_detector(doc):",
" for tok in doc[1:]:",
" if tok.text[0].isdigit() and tok.nbor(-1).is_currency:",
" yield tok.i-1, tok.i+1, 'MONEY'",
"lf1 = heuristics.FunctionAnnotator('money', money_detector)",
"",
"# LF 2: detection of years with a regex",
"lf2= heuristics.TokenConstraintAnnotator ('years', lambda tok: re.match('(19|20)\\d{2}$', tok.text), 'DATE')",
"",
"# LF 3: a gazetteer with a few names",
"NAMES = [('Barack', 'Obama'), ('Donald', 'Trump'), ('Joe', 'Biden')]",
"trie = gazetteers.Trie(NAMES)",
"lf3 = gazetteers.GazetteerAnnotator('presidents', {'PERSON':trie})",
"",
"# We create a corpus (here with a single text)",
"nlp = spacy.load('en_core_web_sm')",
"doc = nlp('Donald Trump paid $750 in federal income taxes in 2016')",
"",
"# apply the labelling functions",
"doc = lf3(lf2(lf1(doc)))",
"",
"# and aggregate them",
"hmm = aggregation.HMM('hmm', ['PERSON', 'DATE', 'MONEY'])",
"hmm.fit_and_aggregate([doc])",
"",
"# we can then visualise the final result (in Jupyter)",
"utils.display_entities(doc, 'hmm')"
],
"code_language": "python",
"url": "https://github.com/NorskRegnesentral/skweak",
"thumb": "https://raw.githubusercontent.com/NorskRegnesentral/skweak/main/data/skweak_logo_thumbnail.jpg",
"image": "https://raw.githubusercontent.com/NorskRegnesentral/skweak/main/data/skweak_logo.jpg",
"author": "Pierre Lison",
"author_links": {
"twitter": "plison2",
"github": "plison",
"website": "https://www.nr.no/~plison"
},
"category": [
"pipeline",
"standalone",
"research",
"training"
],
"tags": [],
"spacy_version": 3
},
{
"id": "numerizer",
"title": "numerizer",
"slogan": "Convert natural language numerics into ints and floats.",
"description": "A SpaCy extension for Docs, Spans and Tokens that converts numerical words and quantitative named entities into numeric strings.",
"github": "jaidevd/numerizer",
"pip": "numerizer",
"code_example": [
"from spacy import load",
"import numerizer",
"nlp = load('en_core_web_sm') # or any other model",
"doc = nlp('The Hogwarts Express is at platform nine and three quarters')",
"doc._.numerize()",
"# {nine and three quarters: '9.75'}"
],
"author": "Jaidev Deshpande",
"author_links": {
"github": "jaidevd",
"twitter": "jaidevd"
},
"category": [
"standalone"
]
},
{
"id": "spacy-dbpedia-spotlight",
"title": "DBpedia Spotlight for SpaCy",
"slogan": "Use DBpedia Spotlight to link entities inside SpaCy",
"description": "This library links SpaCy with [DBpedia Spotlight](https://www.dbpedia-spotlight.org/). You can easily get the DBpedia entities from your documents, using the public web service or by using your own instance of DBpedia Spotlight. The `doc.ents` are populated with the entities and all their details (URI, type, ...).",
"github": "MartinoMensio/spacy-dbpedia-spotlight",
"pip": "spacy-dbpedia-spotlight",
"code_example": [
"import spacy_dbpedia_spotlight",
"# load your model as usual",
"nlp = spacy.load('en_core_web_lg')",
"# add the pipeline stage",
"nlp.add_pipe('dbpedia_spotlight')",
"# get the document",
"doc = nlp('The president of USA is calling Boris Johnson to decide what to do about coronavirus')",
"# see the entities",
"print('Entities', [(ent.text, ent.label_, ent.kb_id_) for ent in doc.ents])",
"# inspect the raw data from DBpedia spotlight",
"print(doc.ents[0]._.dbpedia_raw_result)"
],
"category": [
"models",
"pipeline"
],
"author": "Martino Mensio",
"author_links": {
"twitter": "MartinoMensio",
"github": "MartinoMensio",
"website": "https://martinomensio.github.io"
}
},
{
"id": "spacy-textblob",
"title": "spacytextblob",
"slogan": "A TextBlob sentiment analysis pipeline component for spaCy.",
"thumb": "https://github.com/SamEdwardes/spacytextblob/raw/main/docs/static/img/logo-thumb-square-250x250.png",
"description": "spacytextblob is a pipeline component that enables sentiment analysis using the [TextBlob](https://github.com/sloria/TextBlob) library. It will add the additional extension `._.blob` to `Doc`, `Span`, and `Token` objects.",
"github": "SamEdwardes/spacytextblob",
"pip": "spacytextblob",
"code_example": [
"# the following installations are required",
"# python -m textblob.download_corpora",
"# python -m spacy download en_core_web_sm",
"",
"import spacy",
"from spacytextblob.spacytextblob import SpacyTextBlob",
"",
"nlp = spacy.load('en_core_web_sm')",
"nlp.add_pipe('spacytextblob')",
"text = 'I had a really horrible day. It was the worst day ever! But every now and then I have a really good day that makes me happy.'",
"doc = nlp(text)",
"doc._.blob.polarity # Polarity: -0.125",
"doc._.blob.subjectivity # Subjectivity: 0.9",
"doc._.blob.sentiment_assessments.assessments # Assessments: [(['really', 'horrible'], -1.0, 1.0, None), (['worst', '!'], -1.0, 1.0, None), (['really', 'good'], 0.7, 0.6000000000000001, None), (['happy'], 0.8, 1.0, None)]",
"doc._.blob.ngrams() # [WordList(['I', 'had', 'a']), WordList(['had', 'a', 'really']), WordList(['a', 'really', 'horrible']), WordList(['really', 'horrible', 'day']), WordList(['horrible', 'day', 'It']), WordList(['day', 'It', 'was']), WordList(['It', 'was', 'the']), WordList(['was', 'the', 'worst']), WordList(['the', 'worst', 'day']), WordList(['worst', 'day', 'ever']), WordList(['day', 'ever', 'But']), WordList(['ever', 'But', 'every']), WordList(['But', 'every', 'now']), WordList(['every', 'now', 'and']), WordList(['now', 'and', 'then']), WordList(['and', 'then', 'I']), WordList(['then', 'I', 'have']), WordList(['I', 'have', 'a']), WordList(['have', 'a', 'really']), WordList(['a', 'really', 'good']), WordList(['really', 'good', 'day']), WordList(['good', 'day', 'that']), WordList(['day', 'that', 'makes']), WordList(['that', 'makes', 'me']), WordList(['makes', 'me', 'happy'])]"
],
"code_language": "python",
"url": "https://spacytextblob.netlify.app/",
"author": "Sam Edwardes",
"author_links": {
"twitter": "TheReaLSamlam",
"github": "SamEdwardes",
"website": "https://samedwardes.com"
},
"category": [
"pipeline"
],
"tags": [
"sentiment",
"textblob"
],
"spacy_version": 3
},
{
"id": "spacy-sentence-bert",
"title": "spaCy - sentence-transformers",
"slogan": "Pipelines for pretrained sentence-transformers (BERT, RoBERTa, XLM-RoBERTa & Co.) directly within spaCy",
"description": "This library lets you use the embeddings from [sentence-transformers](https://github.com/UKPLab/sentence-transformers) of Docs, Spans and Tokens directly from spaCy. Most models are for the english language but three of them are multilingual.",
"github": "MartinoMensio/spacy-sentence-bert",
"pip": "spacy-sentence-bert",
"code_example": [
"import spacy_sentence_bert",
"# load one of the models listed at https://github.com/MartinoMensio/spacy-sentence-bert/",
"nlp = spacy_sentence_bert.load_model('en_roberta_large_nli_stsb_mean_tokens')",
"# get two documents",
"doc_1 = nlp('Hi there, how are you?')",
"doc_2 = nlp('Hello there, how are you doing today?')",
"# use the similarity method that is based on the vectors, on Doc, Span or Token",
"print(doc_1.similarity(doc_2[0:7]))"
],
"category": [
"models",
"pipeline"
],
"author": "Martino Mensio",
"author_links": {
"twitter": "MartinoMensio",
"github": "MartinoMensio",
"website": "https://martinomensio.github.io"
}
},
{
"id": "spacy-streamlit",
"title": "spacy-streamlit",
"slogan": "spaCy building blocks for Streamlit apps",
"github": "explosion/spacy-streamlit",
"description": "This package contains utilities for visualizing spaCy models and building interactive spaCy-powered apps with [Streamlit](https://streamlit.io). It includes various building blocks you can use in your own Streamlit app, like visualizers for **syntactic dependencies**, **named entities**, **text classification**, **semantic similarity** via word vectors, token attributes, and more.",
"pip": "spacy-streamlit",
"category": [
"visualizers"
],
"thumb": "https://i.imgur.com/mhEjluE.jpg",
"image": "https://user-images.githubusercontent.com/13643239/85388081-f2da8700-b545-11ea-9bd4-e303d3c5763c.png",
"code_example": [
"import spacy_streamlit",
"",
"models = [\"en_core_web_sm\", \"en_core_web_md\"]",
"default_text = \"Sundar Pichai is the CEO of Google.\"",
"spacy_streamlit.visualize(models, default_text)"
],
"author": "Ines Montani",
"author_links": {
"twitter": "_inesmontani",
"github": "ines",
"website": "https://ines.io"
}
},
{
"id": "spaczz",
"title": "spaczz",
"slogan": "Fuzzy matching and more for spaCy.",
"description": "Spaczz provides fuzzy matching and multi-token regex matching functionality for spaCy. Spaczz's components have similar APIs to their spaCy counterparts and spaczz pipeline components can integrate into spaCy pipelines where they can be saved/loaded as models.",
"github": "gandersen101/spaczz",
"pip": "spaczz",
"code_example": [
"import spacy",
"from spaczz.matcher import FuzzyMatcher",
"",
"nlp = spacy.blank(\"en\")",
"text = \"\"\"Grint Anderson created spaczz in his home at 555 Fake St,",
"Apt 5 in Nashv1le, TN 55555-1234 in the US.\"\"\" # Spelling errors intentional.",
"doc = nlp(text)",
"",
"matcher = FuzzyMatcher(nlp.vocab)",
"matcher.add(\"NAME\", [nlp(\"Grant Andersen\")])",
"matcher.add(\"GPE\", [nlp(\"Nashville\")])",
"matches = matcher(doc)",
"",
"for match_id, start, end, ratio in matches:",
" print(match_id, doc[start:end], ratio)"
],
"code_language": "python",
"url": "https://spaczz.readthedocs.io/en/latest/",
"author": "Grant Andersen",
"author_links": {
"twitter": "gandersen101",
"github": "gandersen101"
},
"category": [
"pipeline"
],
"tags": [
"fuzzy-matching",
"regex"
]
},
{
"id": "spacy-universal-sentence-encoder",
"title": "spaCy - Universal Sentence Encoder",
"slogan": "Make use of Google's Universal Sentence Encoder directly within spaCy",
"description": "This library lets you use Universal Sentence Encoder embeddings of Docs, Spans and Tokens directly from TensorFlow Hub",
"github": "MartinoMensio/spacy-universal-sentence-encoder",
"pip": "spacy-universal-sentence-encoder",
"code_example": [
"import spacy_universal_sentence_encoder",
"# load one of the models: ['en_use_md', 'en_use_lg', 'xx_use_md', 'xx_use_lg']",
"nlp = spacy_universal_sentence_encoder.load_model('en_use_lg')",
"# get two documents",
"doc_1 = nlp('Hi there, how are you?')",
"doc_2 = nlp('Hello there, how are you doing today?')",
"# use the similarity method that is based on the vectors, on Doc, Span or Token",
"print(doc_1.similarity(doc_2[0:7]))"
],
"category": [
"models",
"pipeline"
],
"author": "Martino Mensio",