diff --git a/website/meta/universe.json b/website/meta/universe.json index f1e6d1e8a..f67b7c219 100644 --- a/website/meta/universe.json +++ b/website/meta/universe.json @@ -36,29 +36,29 @@ "github": "SamEdwardes/spaCyTextBlob", "pip": "spacytextblob", "code_example": [ - "import spacy", - "from spacytextblob.spacytextblob import SpacyTextBlob", - "", - "nlp = spacy.load('en_core_web_sm')", - "spacy_text_blob = SpacyTextBlob()", - "nlp.add_pipe(spacy_text_blob)", - "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._.sentiment.polarity # Polarity: -0.125", - "doc._.sentiment.subjectivity # Sujectivity: 0.9", - "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)]" + "import spacy", + "from spacytextblob.spacytextblob import SpacyTextBlob", + "", + "nlp = spacy.load('en_core_web_sm')", + "spacy_text_blob = SpacyTextBlob()", + "nlp.add_pipe(spacy_text_blob)", + "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._.sentiment.polarity # Polarity: -0.125", + "doc._.sentiment.subjectivity # Sujectivity: 0.9", + "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)]" ], "code_language": "python", "url": "https://spacytextblob.netlify.app/", "author": "Sam Edwardes", "author_links": { - "twitter": "TheReaLSamlam", - "github": "SamEdwardes", - "website": "https://samedwardes.com" + "twitter": "TheReaLSamlam", + "github": "SamEdwardes", + "website": "https://samedwardes.com" }, "category": ["pipeline"], "tags": ["sentiment", "textblob"] - }, + }, { "id": "spacy-ray", "title": "spacy-ray", @@ -2602,14 +2602,14 @@ "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.", "pip": "cov-bsv", "code_example": [ - "import cov_bsv", - "", - "nlp = cov_bsv.load()", - "doc = nlp('Pt tested for COVID-19. His wife was recently diagnosed with novel coronavirus. SARS-COV-2: Detected')", - "", - "print(doc.ents)", - "print(doc._.cov_classification)", - "cov_bsv.visualize_doc(doc)" + "import cov_bsv", + "", + "nlp = cov_bsv.load()", + "doc = nlp('Pt tested for COVID-19. His wife was recently diagnosed with novel coronavirus. SARS-COV-2: Detected')", + "", + "print(doc.ents)", + "print(doc._.cov_classification)", + "cov_bsv.visualize_doc(doc)" ], "category": ["pipeline", "standalone", "biomedical", "scientific"], "tags": ["clinical", "epidemiology", "covid-19", "surveillance"], @@ -2627,18 +2627,18 @@ "description": "A toolkit for clinical NLP with spaCy. Features include sentence splitting, section detection, and asserting negation, family history, and uncertainty.", "pip": "medspacy", "code_example": [ - "import medspacy", - "from medspacy.ner import TargetRule", - "", - "nlp = medspacy.load()", - "print(nlp.pipe_names)", - "", - "nlp.get_pipe('target_matcher').add([TargetRule('stroke', 'CONDITION'), TargetRule('diabetes', 'CONDITION'), TargetRule('pna', 'CONDITION')])", - "doc = nlp('Patient has hx of stroke. Mother diagnosed with diabetes. No evidence of pna.')", - "", - "for ent in doc.ents:", - " print(ent, ent._.is_negated, ent._.is_family, ent._.is_historical)", - "medspacy.visualization.visualize_ent(doc)" + "import medspacy", + "from medspacy.ner import TargetRule", + "", + "nlp = medspacy.load()", + "print(nlp.pipe_names)", + "", + "nlp.get_pipe('target_matcher').add([TargetRule('stroke', 'CONDITION'), TargetRule('diabetes', 'CONDITION'), TargetRule('pna', 'CONDITION')])", + "doc = nlp('Patient has hx of stroke. Mother diagnosed with diabetes. No evidence of pna.')", + "", + "for ent in doc.ents:", + " print(ent, ent._.is_negated, ent._.is_family, ent._.is_historical)", + "medspacy.visualization.visualize_ent(doc)" ], "category": ["biomedical", "scientific", "research"], "tags": ["clinical"], @@ -2647,14 +2647,14 @@ "github": "medspacy" } }, - { + { "id": "rita-dsl", "title": "RITA DSL", "slogan": "Domain Specific Language for creating language rules", "github": "zaibacu/rita-dsl", "description": "A Domain Specific Language (DSL) for building language patterns. These can be later compiled into spaCy patterns, pure regex, or any other format", "pip": "rita-dsl", - "thumb": "https://raw.githubusercontent.com/zaibacu/rita-dsl/master/docs/assets/logo-100px.png", + "thumb": "https://raw.githubusercontent.com/zaibacu/rita-dsl/master/docs/assets/logo-100px.png", "code_language": "python", "code_example": [ "import spacy", @@ -2754,8 +2754,8 @@ "{", " var lexeme = doc.Vocab[word.Text];", " Console.WriteLine($@\"{lexeme.Text} {lexeme.Orth} {lexeme.Shape} {lexeme.Prefix} {lexeme.Suffix} {lexeme.IsAlpha} {lexeme.IsDigit} {lexeme.IsTitle} {lexeme.Lang}\");", - "}" - ], + "}" + ], "code_language": "csharp", "author": "Antonio Miras", "author_links": { @@ -2763,33 +2763,33 @@ }, "category": ["nonpython"] }, - { - "id": "ruts", - "title": "ruTS", - "slogan": "A library for statistics extraction from texts in Russian", - "description": "The library allows extracting the following statistics from a text: basic statistics, readability metrics, lexical diversity metrics, morphological statistics", - "github": "SergeyShk/ruTS", - "pip": "ruts", - "code_example": [ - "import spacy", - "import ruts", - "", - "nlp = spacy.load('ru_core_news_sm')", - "nlp.add_pipe('basic', last=True)", - "doc = nlp('мама мыла раму')", - "doc._.basic.get_stats()" - ], - "code_language": "python", - "thumb": "https://habrastorage.org/webt/6z/le/fz/6zlefzjavzoqw_wymz7v3pwgfp4.png", - "image": "https://clipartart.com/images/free-tree-roots-clipart-black-and-white-2.png", - "author": "Sergey Shkarin", - "author_links": { - "twitter": "shk_sergey", - "github": "SergeyShk" - }, - "category": ["pipeline", "standalone"], - "tags": ["Text Analytics", "Russian"] - } + { + "id": "ruts", + "title": "ruTS", + "slogan": "A library for statistics extraction from texts in Russian", + "description": "The library allows extracting the following statistics from a text: basic statistics, readability metrics, lexical diversity metrics, morphological statistics", + "github": "SergeyShk/ruTS", + "pip": "ruts", + "code_example": [ + "import spacy", + "import ruts", + "", + "nlp = spacy.load('ru_core_news_sm')", + "nlp.add_pipe('basic', last=True)", + "doc = nlp('мама мыла раму')", + "doc._.basic.get_stats()" + ], + "code_language": "python", + "thumb": "https://habrastorage.org/webt/6z/le/fz/6zlefzjavzoqw_wymz7v3pwgfp4.png", + "image": "https://clipartart.com/images/free-tree-roots-clipart-black-and-white-2.png", + "author": "Sergey Shkarin", + "author_links": { + "twitter": "shk_sergey", + "github": "SergeyShk" + }, + "category": ["pipeline", "standalone"], + "tags": ["Text Analytics", "Russian"] + } ], "categories": [