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Adding and updating content in the spacy universe (#10493)
* signing contributor agreement * adding new content to the spaCy universe * updating outdated example codes * resolving issues for the PR * resolve review for klayers * remove contributor-agreement file from the PR * Update code example of spaCySentiWS Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Update spacy-sentiws code example Co-authored-by: schaeran <schaeran1994@gmail.com> Co-authored-by: schaeran <schaeran@explosion.ai> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
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Schero1994
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@ -1,5 +1,69 @@
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{
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"resources": [
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{
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"id": "scrubadub_spacy",
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"title": "scrubadub_spacy",
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"category": ["pipeline"],
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"slogan": "Remove personally identifiable information from text using spaCy.",
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"description": "scrubadub removes personally identifiable information from text. scrubadub_spacy is an extension that uses spaCy NLP models to remove personal information from text.",
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"github": "LeapBeyond/scrubadub_spacy",
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"pip": "scrubadub-spacy",
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"url": "https://github.com/LeapBeyond/scrubadub_spacy",
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"code_language": "python",
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"author": "Leap Beyond",
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"author_links": {
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"github": "https://github.com/LeapBeyond",
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"website": "https://leapbeyond.ai"
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},
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"code_example": [
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"import scrubadub, scrubadub_spacy",
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"scrubber = scrubadub.Scrubber()",
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"scrubber.add_detector(scrubadub_spacy.detectors.SpacyEntityDetector)",
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"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\"))",
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"# 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}}"
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]
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},
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{
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"id": "spacy-setfit-textcat",
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"title": "spacy-setfit-textcat",
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"category": ["research"],
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"tags": ["SetFit", "Few-Shot"],
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"slogan": "spaCy Project: Experiments with SetFit & Few-Shot Classification",
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"description": "This project is an experiment with spaCy and few-shot text classification using SetFit",
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"github": "pmbaumgartner/spacy-setfit-textcat",
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"url": "https://github.com/pmbaumgartner/spacy-setfit-textcat",
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"code_language": "python",
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"author": "Peter Baumgartner",
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"author_links": {
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"twitter" : "https://twitter.com/pmbaumgartner",
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"github": "https://github.com/pmbaumgartner",
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"website": "https://www.peterbaumgartner.com/"
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},
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"code_example": [
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"https://colab.research.google.com/drive/1CvGEZC0I9_v8gWrBxSJQ4Z8JGPJz-HYb?usp=sharing"
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]
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},
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{
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"id": "spacy-experimental",
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"title": "spacy-experimental",
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"category": ["extension"],
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"slogan": "Cutting-edge experimental spaCy components and features",
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"description": "This package includes experimental components and features for spaCy v3.x, for example model architectures, pipeline components and utilities.",
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"github": "explosion/spacy-experimental",
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"pip": "spacy-experimental",
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"url": "https://github.com/explosion/spacy-experimental",
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"code_language": "python",
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"author": "Explosion",
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"author_links": {
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"twitter" : "https://twitter.com/explosion_ai",
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"github": "https://github.com/explosion",
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"website": "https://explosion.ai/"
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},
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"code_example": [
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"python -m pip install -U pip setuptools wheel",
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"python -m pip install spacy-experimental"
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]
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},
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{
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"id": "spacypdfreader",
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"title": "spadypdfreader",
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@ -327,15 +391,20 @@
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"pip": "spaczz",
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"code_example": [
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"import spacy",
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"from spaczz.pipeline import SpaczzRuler",
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"from spaczz.matcher import FuzzyMatcher",
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"",
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"nlp = spacy.blank('en')",
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"ruler = SpaczzRuler(nlp)",
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"ruler.add_patterns([{'label': 'PERSON', 'pattern': 'Bill Gates', 'type': 'fuzzy'}])",
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"nlp.add_pipe(ruler)",
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"nlp = spacy.blank(\"en\")",
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"text = \"\"\"Grint Anderson created spaczz in his home at 555 Fake St,",
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"Apt 5 in Nashv1le, TN 55555-1234 in the US.\"\"\" # Spelling errors intentional.",
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"doc = nlp(text)",
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"",
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"doc = nlp('Oops, I spelled Bill Gatez wrong.')",
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"print([(ent.text, ent.start, ent.end, ent.label_) for ent in doc.ents])"
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"matcher = FuzzyMatcher(nlp.vocab)",
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"matcher.add(\"NAME\", [nlp(\"Grant Andersen\")])",
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"matcher.add(\"GPE\", [nlp(\"Nashville\")])",
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"matches = matcher(doc)",
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"",
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"for match_id, start, end, ratio in matches:",
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" print(match_id, doc[start:end], ratio)"
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],
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"code_language": "python",
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"url": "https://spaczz.readthedocs.io/en/latest/",
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@ -442,6 +511,84 @@
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"website": "https://koaning.io"
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}
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},
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{
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"id": "Klayers",
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"title": "Klayers",
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"category": ["pipeline"],
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"tags": ["AWS"],
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"slogan": "spaCy as a AWS Lambda Layer",
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"description": "A collection of Python Packages as AWS Lambda(λ) Layers",
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"github": "keithrozario/Klayers",
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"pip": "",
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"url": "https://github.com/keithrozario/Klayers",
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"code_language": "python",
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"author": "Keith Rozario",
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"author_links": {
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"twitter" : "https://twitter.com/keithrozario",
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"github": "https://github.com/keithrozario",
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"website": "https://www.keithrozario.com"
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},
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"code_example": [
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"# SAM Template",
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"MyLambdaFunction:",
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" Type: AWS::Serverless::Function",
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" Handler: 02_pipeline/spaCy.main",
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" Description: Name Entity Extraction",
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" Runtime: python3.8",
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" Layers:",
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" - arn:aws:lambda:${self:provider.region}:113088814899:layer:Klayers-python37-spacy:18"
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]
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},
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{
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"type": "education",
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"id": "video-spacys-ner-model-alt",
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"title": "Named Entity Recognition (NER) using spaCy",
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"slogan": "",
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"description": "In this video, I show you how to do named entity recognition using the spaCy library for Python.",
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"youtube": "Gn_PjruUtrc",
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"author": "Applied Language Technology",
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"author_links": {
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"twitter": "HelsinkiNLP",
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"github": "Applied-Language-Technology",
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"website": "https://applied-language-technology.mooc.fi/"
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},
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"category": ["videos"]
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},
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{
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"id": "HuSpaCy",
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"title": "HuSpaCy",
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"category": ["models"],
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"tags": ["Hungarian"],
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"slogan": "HuSpaCy: industrial-strength Hungarian natural language processing",
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"description": "HuSpaCy is a spaCy model and a library providing industrial-strength Hungarian language processing facilities.",
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"github": "huspacy/huspacy",
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"pip": "huspacy",
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"url": "https://github.com/huspacy/huspacy",
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"code_language": "python",
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"author": "SzegedAI",
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"author_links": {
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"github": "https://szegedai.github.io/",
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"website": "https://u-szeged.hu/english"
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},
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"code_example": [
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"# Load the model using huspacy",
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"import huspacy",
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"",
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"nlp = huspacy.load()",
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"",
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"# Load the mode using spacy.load()",
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"import spacy",
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"",
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"nlp = spacy.load(\"hu_core_news_lg\")",
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"",
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"# Load the model directly as a module",
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"import hu_core_news_lg",
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"",
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"nlp = hu_core_news_lg.load()\n",
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"# Either way you get the same model and can start processing texts.",
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"doc = nlp(\"Csiribiri csiribiri zabszalma - négy csillag közt alszom ma.\")"
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]
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},
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{
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"id": "spacy-stanza",
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"title": "spacy-stanza",
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"import spacy",
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"from spacymoji import Emoji",
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"",
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"nlp = spacy.load('en')",
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"emoji = Emoji(nlp)",
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"nlp.add_pipe(emoji, first=True)",
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"nlp = spacy.load(\"en_core_web_sm\")",
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"nlp.add_pipe(\"emoji\", first=True)",
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"doc = nlp(\"This is a test 😻 👍🏿\")",
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"",
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"doc = nlp('This is a test 😻 👍🏿')",
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"assert doc._.has_emoji == True",
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"assert doc[2:5]._.has_emoji == True",
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"assert doc[0]._.is_emoji == False",
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"assert doc[4]._.is_emoji == True",
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"assert doc[5]._.emoji_desc == 'thumbs up dark skin tone'",
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"assert doc._.has_emoji is True",
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"assert doc[2:5]._.has_emoji is True",
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"assert doc[0]._.is_emoji is False",
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"assert doc[4]._.is_emoji is True",
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"assert doc[5]._.emoji_desc == \"thumbs up dark skin tone\"",
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"assert len(doc._.emoji) == 2",
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"assert doc._.emoji[1] == ('👍🏿', 5, 'thumbs up dark skin tone')"
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"assert doc._.emoji[1] == (\"👍🏿\", 5, \"thumbs up dark skin tone\")"
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],
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"author": "Ines Montani",
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"author_links": {
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"import spacy",
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"from spacy_sentiws import spaCySentiWS",
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"",
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"nlp = spacy.load('de')",
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"sentiws = spaCySentiWS(sentiws_path='data/sentiws/')",
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"nlp.add_pipe(sentiws)",
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"nlp = spacy.load('de_core_news_sm')",
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"nlp.add_pipe('sentiws', config={'sentiws_path': 'data/sentiws'})",
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"doc = nlp('Die Dummheit der Unterwerfung blüht in hübschen Farben.')",
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"",
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"for token in doc:",
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"import spacy",
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"import pytextrank",
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"",
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"nlp = spacy.load('en_core_web_sm')",
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"# example text",
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"text = \"\"\"Compatibility of systems of linear constraints over the set of natural numbers.",
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"Criteria of compatibility of a system of linear Diophantine equations, strict inequations,",
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"and nonstrict inequations are considered. Upper bounds for components of a minimal set of",
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"solutions and algorithms of construction of minimal generating sets of solutions for all types",
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"of systems are given. These criteria and the corresponding algorithms for constructing a minimal",
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"supporting set of solutions can be used in solving all the considered types systems and systems of mixed types.\"\"\"",
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"",
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"tr = pytextrank.TextRank()",
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"nlp.add_pipe(tr.PipelineComponent, name='textrank', last=True)",
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"# load a spaCy model, depending on language, scale, etc.",
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"nlp = spacy.load(\"en_core_web_sm\")",
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"# add PyTextRank to the spaCy pipeline",
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"nlp.add_pipe(\"textrank\")",
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"",
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"text = 'Compatibility of systems of linear constraints over the set of natural numbers. Criteria of compatibility of a system of linear Diophantine equations, strict inequations, and nonstrict inequations are considered.'",
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"doc = nlp(text)",
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"",
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"# examine the top-ranked phrases in the document",
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"for p in doc._.phrases:",
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" print('{:.4f} {:5d} {}'.format(p.rank, p.count, p.text))",
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" print(p.chunks)"
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"for phrase in doc._.phrases:",
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" print(phrase.text)",
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" print(phrase.rank, phrase.count)",
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" print(phrase.chunks)"
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],
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"code_language": "python",
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"url": "https://github.com/DerwenAI/pytextrank/wiki",
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"import spacy",
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"from spacy_syllables import SpacySyllables",
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"",
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"nlp = spacy.load('en_core_web_sm')",
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"syllables = SpacySyllables(nlp)",
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"nlp.add_pipe(syllables, after='tagger')",
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"nlp = spacy.load(\"en_core_web_sm\")",
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"nlp.add_pipe(\"syllables\", after=\"tagger\")",
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"",
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"doc = nlp('terribly long')",
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"",
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"data = [",
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" (token.text, token._.syllables, token._.syllables_count)",
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" for token in doc",
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"]",
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"",
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"assert data == [",
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" ('terribly', ['ter', 'ri', 'bly'], 3),",
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" ('long', ['long'], 1)",
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"]"
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"assert nlp.pipe_names == [\"tok2vec\", \"tagger\", \"syllables\", \"parser\", \"attribute_ruler\", \"lemmatizer\", \"ner\"]",
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"doc = nlp(\"terribly long\")",
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"data = [(token.text, token..syllables, token..syllables_count) for token in doc]",
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"assert data == [(\"terribly\", [\"ter\", \"ri\", \"bly\"], 3), (\"long\", [\"long\"], 1)]"
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],
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"thumb": "https://raw.githubusercontent.com/sloev/spacy-syllables/master/logo.png",
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"author": "Johannes Valbjørn",
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