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{
"resources": [
{
"id": "grecy",
"title": "greCy",
"slogan": "Ancient Greek pipelines for spaCy",
"description": "greCy offers state-of-the-art pipelines for ancient Greek NLP. The repository makes language models available in various sizes, some of them containing floret word vectors and a BERT transformer layer.",
"github": "jmyerston/greCy",
"code_example": [
"import spacy",
"#After installing the grc_ud_proiel_trf wheel package from the greCy repository",
"",
"nlp = spacy.load('grc_ud_proiel_trf')",
"doc = nlp('δοκῶ μοι περὶ ὧν πυνθάνεσθε οὐκ ἀμελέτητος εἶναι.')",
"",
"for token in doc:",
" print(token.text, token.norm_, token.lemma_, token.pos_, token.tag_)"
],
"code_language": "python",
"author": "Jacobo Myerston",
"author_links": {
"twitter": "@jcbmyrstn",
"github": "jmyerston",
"website": "https://huggingface.co/spaces/Jacobo/syntax"
},
"category": ["pipeline", "research"],
"tags": ["ancient Greek"]
},
{
"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 [email protected] 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": "spadypdfreader",
"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 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",
"author_links": {
"twitter": "MartinoMensio",
"github": "MartinoMensio",
"website": "https://martinomensio.github.io"
}
},
{
"id": "whatlies",
"title": "whatlies",
"slogan": "Make interactive visualisations to figure out 'what lies' in word embeddings.",
"description": "This small library offers tools to make visualisation easier of both word embeddings as well as operations on them. It has support for spaCy prebuilt models as a first class citizen but also offers support for sense2vec. There's a convenient API to perform linear algebra as well as support for popular transformations like PCA/UMAP/etc.",
"github": "koaning/whatlies",
"pip": "whatlies",
"thumb": "https://i.imgur.com/rOkOiLv.png",
"image": "https://raw.githubusercontent.com/koaning/whatlies/master/docs/gif-two.gif",
"code_example": [
"from whatlies import EmbeddingSet",
"from whatlies.language import SpacyLanguage",
"",
"lang = SpacyLanguage('en_core_web_md')",
"words = ['cat', 'dog', 'fish', 'kitten', 'man', 'woman', 'king', 'queen', 'doctor', 'nurse']",
"",
"emb = lang[words]",
"emb.plot_interactive(x_axis='man', y_axis='woman')"
],
"category": ["visualizers", "research"],
"author": "Vincent D. Warmerdam",
"author_links": {
"twitter": "fishnets88",
"github": "koaning",
"website": "https://koaning.io"
}
},
{
"id": "bertopic",
"title": "BERTopic",
"slogan": "Leveraging BERT and c-TF-IDF to create easily interpretable topics.",
"description": "BERTopic is a topic modeling technique that leverages embedding models and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions. BERTopic supports guided, (semi-) supervised, hierarchical, and dynamic topic modeling.",
"github": "maartengr/bertopic",
"pip": "bertopic",
"thumb": "https://i.imgur.com/Rx2LfBm.png",
"image": "https://raw.githubusercontent.com/MaartenGr/BERTopic/master/images/topic_visualization.gif",
"code_example": [
"import spacy",
"from bertopic import BERTopic",
"from sklearn.datasets import fetch_20newsgroups",
"",
"docs = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))['data']",
"nlp = spacy.load('en_core_web_md', exclude=['tagger', 'parser', 'ner', 'attribute_ruler', 'lemmatizer'])",
"",
"topic_model = BERTopic(embedding_model=nlp)",
"topics, probs = topic_model.fit_transform(docs)",
"",
"fig = topic_model.visualize_topics()",
"fig.show()"
],
"category": ["visualizers", "training"],
"author": "Maarten Grootendorst",
"author_links": {
"twitter": "maartengr",
"github": "maartengr",
"website": "https://maartengrootendorst.com"
}
},
{
"id": "tokenwiser",
"title": "tokenwiser",
"slogan": "Connect vowpal-wabbit & scikit-learn models to spaCy to run simple classification benchmarks. Comes with many utility functions for spaCy pipelines.",
"github": "koaning/tokenwiser",
"pip": "tokenwiser",
"thumb": "https://koaning.github.io/tokenwiser/token.png",
"image": "https://koaning.github.io/tokenwiser/logo-tokw.png",
"code_example": [
"import spacy",
"",
"from sklearn.pipeline import make_pipeline",
"from sklearn.feature_extraction.text import CountVectorizer",
"from sklearn.linear_model import LogisticRegression",
"",
"from tokenwiser.component import attach_sklearn_categoriser",
"",
"X = [",
" 'i really like this post',",
" 'thanks for that comment',",
" 'i enjoy this friendly forum',",
" 'this is a bad post',",
" 'i dislike this article',",
" 'this is not well written'",
"]",
"",
"y = ['pos', 'pos', 'pos', 'neg', 'neg', 'neg']",
"",
"# Note that we're training a pipeline here via a single-batch `.fit()` method",
"pipe = make_pipeline(CountVectorizer(), LogisticRegression()).fit(X, y)",
"",
"nlp = spacy.load('en_core_web_sm')",
"# This is where we attach our pre-trained model as a pipeline step.",
"attach_sklearn_categoriser(nlp, pipe_name='silly_sentiment', estimator=pipe)"
],
"category": ["pipeline", "training"],
"author": "Vincent D. Warmerdam",
"author_links": {
"twitter": "fishnets88",
"github": "koaning",
"website": "https://koaning.io"
}
},
{
"id": "Klayers",
"title": "Klayers",
"category": ["pipeline"],
"tags": ["AWS"],
"slogan": "spaCy as a AWS Lambda Layer",
"description": "A collection of Python Packages as AWS Lambda(λ) Layers",
"github": "keithrozario/Klayers",
"pip": "",
"url": "https://github.com/keithrozario/Klayers",
"code_language": "python",
"author": "Keith Rozario",
"author_links": {
"twitter": "keithrozario",
"github": "keithrozario",
"website": "https://www.keithrozario.com"
},
"code_example": [
"# SAM Template",
"MyLambdaFunction:",
" Type: AWS::Serverless::Function",
" Handler: 02_pipeline/spaCy.main",
" Description: Name Entity Extraction",
" Runtime: python3.8",
" Layers:",
" - arn:aws:lambda:${self:provider.region}:113088814899:layer:Klayers-python37-spacy:18"
]
},
{
"type": "education",
"id": "video-spacys-ner-model-alt",
"title": "Named Entity Recognition (NER) using spaCy",
"slogan": "",
"description": "In this video, I show you how to do named entity recognition using the spaCy library for Python.",
"youtube": "Gn_PjruUtrc",
"author": "Applied Language Technology",
"author_links": {
"twitter": "HelsinkiNLP",
"github": "Applied-Language-Technology",
"website": "https://applied-language-technology.mooc.fi/"
},
"category": ["videos"]
},
{
"id": "HuSpaCy",
"title": "HuSpaCy",
"category": ["models"],
"tags": ["Hungarian"],
"slogan": "HuSpaCy: industrial-strength Hungarian natural language processing",
"description": "HuSpaCy is a spaCy model and a library providing industrial-strength Hungarian language processing facilities.",
"github": "huspacy/huspacy",
"pip": "huspacy",
"url": "https://github.com/huspacy/huspacy",
"code_language": "python",
"author": "SzegedAI",
"author_links": {
"github": "https://szegedai.github.io/",
"website": "https://u-szeged.hu/english"
},
"code_example": [
"# Load the model using huspacy",
"import huspacy",
"",
"nlp = huspacy.load()",
"",
"# Load the mode using spacy.load()",
"import spacy",
"",
"nlp = spacy.load(\"hu_core_news_lg\")",
"",
"# Load the model directly as a module",
"import hu_core_news_lg",
"",
"nlp = hu_core_news_lg.load()\n",
"# Either way you get the same model and can start processing texts.",
"doc = nlp(\"Csiribiri csiribiri zabszalma - négy csillag közt alszom ma.\")"
]
},
{
"id": "spacy-stanza",
"title": "spacy-stanza",
"slogan": "Use the latest Stanza (StanfordNLP) research models directly in spaCy",
"description": "This package wraps the Stanza (formerly StanfordNLP) library, so you can use Stanford's models as a spaCy pipeline. Using this wrapper, you'll be able to use the following annotations, computed by your pretrained `stanza` model:\n\n- Statistical tokenization (reflected in the `Doc` and its tokens)\n - Lemmatization (`token.lemma` and `token.lemma_`)\n - Part-of-speech tagging (`token.tag`, `token.tag_`, `token.pos`, `token.pos_`)\n - Dependency parsing (`token.dep`, `token.dep_`, `token.head`)\n - Named entity recognition (`doc.ents`, `token.ent_type`, `token.ent_type_`, `token.ent_iob`, `token.ent_iob_`)\n - Sentence segmentation (`doc.sents`)",
"github": "explosion/spacy-stanza",
"pip": "spacy-stanza",
"thumb": "https://i.imgur.com/myhLjMJ.png",
"code_example": [
"import stanza",
"import spacy_stanza",
"",
"stanza.download(\"en\")",
"nlp = spacy_stanza.load_pipeline(\"en\")",
"",
"doc = nlp(\"Barack Obama was born in Hawaii. He was elected president in 2008.\")",
"for token in doc:",
" print(token.text, token.lemma_, token.pos_, token.dep_, token.ent_type_)",
"print(doc.ents)"
],
"category": ["pipeline", "standalone", "models", "research"],
"author": "Explosion",
"author_links": {
"twitter": "explosion_ai",
"github": "explosion",
"website": "https://explosion.ai"
}
},
{
"id": "spacy-udpipe",
"title": "spacy-udpipe",
"slogan": "Use the latest UDPipe models directly in spaCy",
"description": "This package wraps the fast and efficient UDPipe language-agnostic NLP pipeline (via its Python bindings), so you can use UDPipe pre-trained models as a spaCy pipeline for 50+ languages out-of-the-box. Inspired by spacy-stanza, this package offers slightly less accurate models that are in turn much faster.",
"github": "TakeLab/spacy-udpipe",
"pip": "spacy-udpipe",
"code_example": [
"import spacy_udpipe",
"",
"spacy_udpipe.download(\"en\") # download English model",
"",
"text = \"Wikipedia is a free online encyclopedia, created and edited by volunteers around the world.\"",
"nlp = spacy_udpipe.load(\"en\")",
"",
"doc = nlp(text)",
"for token in doc:",
" print(token.text, token.lemma_, token.pos_, token.dep_)"
],
"category": ["pipeline", "standalone", "models", "research"],
"author": "TakeLab",
"author_links": {
"github": "TakeLab",
"website": "https://takelab.fer.hr/"
}
},
{
"id": "spacy-server",
"title": "spaCy Server",
"slogan": "\uD83E\uDD9C Containerized HTTP API for spaCy NLP",
"description": "For developers who need programming language agnostic NLP, spaCy Server is a containerized HTTP API that provides industrial-strength natural language processing. Unlike other servers, our server is fast, idiomatic, and well documented.",
"github": "neelkamath/spacy-server",
"code_example": [
"docker run --rm -dp 8080:8080 neelkamath/spacy-server",
"curl http://localhost:8080/ner -H 'Content-Type: application/json' -d '{\"sections\": [\"My name is John Doe. I grew up in California.\"]}'"
],
"code_language": "shell",
"url": "https://hub.docker.com/r/neelkamath/spacy-server",
"author": "Neel Kamath",
"author_links": {
"github": "neelkamath",
"website": "https://neelkamath.com"
},
"category": ["apis"],
"tags": ["docker"]
},
{
"id": "nlp-architect",
"title": "NLP Architect",
"slogan": "Python lib for exploring Deep NLP & NLU by Intel AI",
"github": "NervanaSystems/nlp-architect",
"pip": "nlp-architect",
"thumb": "https://i.imgur.com/vMideRx.png",
"category": ["standalone", "research"],
"tags": ["pytorch"]
},
{
"id": "Chatterbot",
"title": "Chatterbot",
"slogan": "A machine-learning based conversational dialog engine for creating chat bots",
"github": "gunthercox/ChatterBot",
"pip": "chatterbot",
"thumb": "https://i.imgur.com/eyAhwXk.jpg",
"code_example": [
"from chatterbot import ChatBot",
"from chatterbot.trainers import ListTrainer",
"# Create a new chat bot named Charlie",
"chatbot = ChatBot('Charlie')",
"trainer = ListTrainer(chatbot)",
"trainer.train([",
"'Hi, can I help you?',",
"'Sure, I would like to book a flight to Iceland.',",
"'Your flight has been booked.'",
"])",
"",
"response = chatbot.get_response('I would like to book a flight.')"
],
"author": "Gunther Cox",
"author_links": {
"github": "gunthercox"
},
"category": ["conversational", "standalone"],
"tags": ["chatbots"]
},
{
"id": "alibi",
"title": "alibi",
"slogan": "Algorithms for monitoring and explaining machine learning models ",
"github": "SeldonIO/alibi",
"pip": "alibi",
"thumb": "https://i.imgur.com/YkzQHRp.png",
"code_example": [
"from alibi.explainers import AnchorTabular",
"explainer = AnchorTabular(predict_fn, feature_names)",
"explainer.fit(X_train)",
"explainer.explain(x)"
],
"author": "Seldon",
"category": ["standalone", "research"]
},
{
"id": "spacymoji",
"slogan": "Emoji handling and meta data as a spaCy pipeline component",
"github": "ines/spacymoji",
"description": "spaCy extension and pipeline component for adding emoji meta data to `Doc` objects. Detects emoji consisting of one or more unicode characters, and can optionally merge multi-char emoji (combined pictures, emoji with skin tone modifiers) into one token. Human-readable emoji descriptions are added as a custom attribute, and an optional lookup table can be provided for your own descriptions. The extension sets the custom `Doc`, `Token` and `Span` attributes `._.is_emoji`, `._.emoji_desc`, `._.has_emoji` and `._.emoji`.",
"pip": "spacymoji",
"category": ["pipeline"],
"tags": ["emoji", "unicode"],
"thumb": "https://i.imgur.com/XOTYIgn.jpg",
"code_example": [
"import spacy",
"from spacymoji import Emoji",
"",
"nlp = spacy.load(\"en_core_web_sm\")",
"nlp.add_pipe(\"emoji\", first=True)",
"doc = nlp(\"This is a test 😻 👍🏿\")",
"",
"assert doc._.has_emoji is True",
"assert doc[2:5]._.has_emoji is True",
"assert doc[0]._.is_emoji is False",
"assert doc[4]._.is_emoji is True",
"assert doc[5]._.emoji_desc == \"thumbs up dark skin tone\"",
"assert len(doc._.emoji) == 2",
"assert doc._.emoji[1] == (\"👍🏿\", 5, \"thumbs up dark skin tone\")"
],
"author": "Ines Montani",
"author_links": {
"twitter": "_inesmontani",
"github": "ines",
"website": "https://ines.io"
}
},
{
"id": "spacyopentapioca",
"title": "spaCyOpenTapioca",
"slogan": "Named entity linking on Wikidata in spaCy via OpenTapioca",
"description": "A spaCy wrapper of OpenTapioca for named entity linking on Wikidata",
"github": "UB-Mannheim/spacyopentapioca",
"pip": "spacyopentapioca",
"code_example": [
"import spacy",
"nlp = spacy.blank('en')",
"nlp.add_pipe('opentapioca')",
"doc = nlp('Christian Drosten works in Germany.')",
"for span in doc.ents:",
" print((span.text, span.kb_id_, span.label_, span._.description, span._.score))",
"# ('Christian Drosten', 'Q1079331', 'PERSON', 'German virologist and university teacher', 3.6533377082098895)",
"# ('Germany', 'Q183', 'LOC', 'sovereign state in Central Europe', 2.1099332471902863)",
"## Check also span._.types, span._.aliases, span._.rank"
],
"category": ["models", "pipeline"],
"tags": ["NER", "NEL"],
"author": "Renat Shigapov",
"author_links": {
"twitter": "_shigapov",
"github": "shigapov"
}
},
{
"id": "spacy_readability",
"slogan": "Add text readability meta data to Doc objects",
"description": "spaCy v2.0 pipeline component for calculating readability scores of of text. Provides scores for Flesh-Kincaid grade level, Flesh-Kincaid reading ease, and Dale-Chall.",
"github": "mholtzscher/spacy_readability",