mirror of https://github.com/explosion/spaCy.git
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Ines Montani
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@ -36,29 +36,29 @@
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"github": "SamEdwardes/spaCyTextBlob",
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"pip": "spacytextblob",
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"code_example": [
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"import spacy",
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"from spacytextblob.spacytextblob import SpacyTextBlob",
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"",
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"nlp = spacy.load('en_core_web_sm')",
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"spacy_text_blob = SpacyTextBlob()",
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"nlp.add_pipe(spacy_text_blob)",
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"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.'",
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"doc = nlp(text)",
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"doc._.sentiment.polarity # Polarity: -0.125",
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"doc._.sentiment.subjectivity # Sujectivity: 0.9",
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"doc._.sentiment.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)]"
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"import spacy",
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"from spacytextblob.spacytextblob import SpacyTextBlob",
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"",
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"nlp = spacy.load('en_core_web_sm')",
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"spacy_text_blob = SpacyTextBlob()",
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"nlp.add_pipe(spacy_text_blob)",
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"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.'",
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"doc = nlp(text)",
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"doc._.sentiment.polarity # Polarity: -0.125",
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"doc._.sentiment.subjectivity # Sujectivity: 0.9",
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"doc._.sentiment.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)]"
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],
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"code_language": "python",
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"url": "https://spacytextblob.netlify.app/",
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"author": "Sam Edwardes",
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"author_links": {
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"twitter": "TheReaLSamlam",
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"github": "SamEdwardes",
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"website": "https://samedwardes.com"
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"twitter": "TheReaLSamlam",
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"github": "SamEdwardes",
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"website": "https://samedwardes.com"
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},
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"category": ["pipeline"],
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"tags": ["sentiment", "textblob"]
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},
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},
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{
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"id": "spacy-ray",
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"title": "spacy-ray",
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@ -2602,14 +2602,14 @@
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"description": "A spaCy rule-based pipeline for identifying positive cases of COVID-19 from clinical text. A version of this system was deployed as part of the US Department of Veterans Affairs biosurveillance response to COVID-19.",
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"pip": "cov-bsv",
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"code_example": [
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"import cov_bsv",
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"",
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"nlp = cov_bsv.load()",
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"doc = nlp('Pt tested for COVID-19. His wife was recently diagnosed with novel coronavirus. SARS-COV-2: Detected')",
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"",
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"print(doc.ents)",
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"print(doc._.cov_classification)",
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"cov_bsv.visualize_doc(doc)"
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"import cov_bsv",
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"",
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"nlp = cov_bsv.load()",
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"doc = nlp('Pt tested for COVID-19. His wife was recently diagnosed with novel coronavirus. SARS-COV-2: Detected')",
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"",
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"print(doc.ents)",
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"print(doc._.cov_classification)",
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"cov_bsv.visualize_doc(doc)"
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],
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"category": ["pipeline", "standalone", "biomedical", "scientific"],
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"tags": ["clinical", "epidemiology", "covid-19", "surveillance"],
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@ -2627,18 +2627,18 @@
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"description": "A toolkit for clinical NLP with spaCy. Features include sentence splitting, section detection, and asserting negation, family history, and uncertainty.",
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"pip": "medspacy",
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"code_example": [
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"import medspacy",
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"from medspacy.ner import TargetRule",
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"",
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"nlp = medspacy.load()",
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"print(nlp.pipe_names)",
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"",
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"nlp.get_pipe('target_matcher').add([TargetRule('stroke', 'CONDITION'), TargetRule('diabetes', 'CONDITION'), TargetRule('pna', 'CONDITION')])",
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"doc = nlp('Patient has hx of stroke. Mother diagnosed with diabetes. No evidence of pna.')",
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"",
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"for ent in doc.ents:",
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" print(ent, ent._.is_negated, ent._.is_family, ent._.is_historical)",
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"medspacy.visualization.visualize_ent(doc)"
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"import medspacy",
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"from medspacy.ner import TargetRule",
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"",
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"nlp = medspacy.load()",
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"print(nlp.pipe_names)",
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"",
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"nlp.get_pipe('target_matcher').add([TargetRule('stroke', 'CONDITION'), TargetRule('diabetes', 'CONDITION'), TargetRule('pna', 'CONDITION')])",
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"doc = nlp('Patient has hx of stroke. Mother diagnosed with diabetes. No evidence of pna.')",
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"",
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"for ent in doc.ents:",
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" print(ent, ent._.is_negated, ent._.is_family, ent._.is_historical)",
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"medspacy.visualization.visualize_ent(doc)"
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],
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"category": ["biomedical", "scientific", "research"],
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"tags": ["clinical"],
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@ -2647,14 +2647,14 @@
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"github": "medspacy"
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}
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},
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{
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{
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"id": "rita-dsl",
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"title": "RITA DSL",
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"slogan": "Domain Specific Language for creating language rules",
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"github": "zaibacu/rita-dsl",
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"description": "A Domain Specific Language (DSL) for building language patterns. These can be later compiled into spaCy patterns, pure regex, or any other format",
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"pip": "rita-dsl",
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"thumb": "https://raw.githubusercontent.com/zaibacu/rita-dsl/master/docs/assets/logo-100px.png",
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"thumb": "https://raw.githubusercontent.com/zaibacu/rita-dsl/master/docs/assets/logo-100px.png",
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"code_language": "python",
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"code_example": [
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"import spacy",
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@ -2754,8 +2754,8 @@
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"{",
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" var lexeme = doc.Vocab[word.Text];",
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" Console.WriteLine($@\"{lexeme.Text} {lexeme.Orth} {lexeme.Shape} {lexeme.Prefix} {lexeme.Suffix} {lexeme.IsAlpha} {lexeme.IsDigit} {lexeme.IsTitle} {lexeme.Lang}\");",
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"}"
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],
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"}"
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],
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"code_language": "csharp",
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"author": "Antonio Miras",
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"author_links": {
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@ -2763,33 +2763,33 @@
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},
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"category": ["nonpython"]
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},
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{
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"id": "ruts",
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"title": "ruTS",
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"slogan": "A library for statistics extraction from texts in Russian",
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"description": "The library allows extracting the following statistics from a text: basic statistics, readability metrics, lexical diversity metrics, morphological statistics",
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"github": "SergeyShk/ruTS",
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"pip": "ruts",
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"code_example": [
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"import spacy",
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"import ruts",
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"",
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"nlp = spacy.load('ru_core_news_sm')",
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"nlp.add_pipe('basic', last=True)",
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"doc = nlp('мама мыла раму')",
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"doc._.basic.get_stats()"
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],
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"code_language": "python",
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"thumb": "https://habrastorage.org/webt/6z/le/fz/6zlefzjavzoqw_wymz7v3pwgfp4.png",
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"image": "https://clipartart.com/images/free-tree-roots-clipart-black-and-white-2.png",
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"author": "Sergey Shkarin",
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"author_links": {
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"twitter": "shk_sergey",
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"github": "SergeyShk"
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},
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"category": ["pipeline", "standalone"],
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"tags": ["Text Analytics", "Russian"]
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}
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{
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"id": "ruts",
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"title": "ruTS",
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"slogan": "A library for statistics extraction from texts in Russian",
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"description": "The library allows extracting the following statistics from a text: basic statistics, readability metrics, lexical diversity metrics, morphological statistics",
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"github": "SergeyShk/ruTS",
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"pip": "ruts",
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"code_example": [
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"import spacy",
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"import ruts",
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"",
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"nlp = spacy.load('ru_core_news_sm')",
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"nlp.add_pipe('basic', last=True)",
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"doc = nlp('мама мыла раму')",
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"doc._.basic.get_stats()"
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],
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"code_language": "python",
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"thumb": "https://habrastorage.org/webt/6z/le/fz/6zlefzjavzoqw_wymz7v3pwgfp4.png",
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"image": "https://clipartart.com/images/free-tree-roots-clipart-black-and-white-2.png",
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"author": "Sergey Shkarin",
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"author_links": {
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"twitter": "shk_sergey",
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"github": "SergeyShk"
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},
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"category": ["pipeline", "standalone"],
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"tags": ["Text Analytics", "Russian"]
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}
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],
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"categories": [
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