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spaCy/website/meta/universe.json
Jules Belveze cd09614ab2
chore: add 'concepCy' to spacy universe (#11255) 
* chore: add 'concepCy' to spacy universe

* docs: add 'slogan' to concepCy
2022-08-04 15:42:38 +09:00

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{
"resources": [
{
"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": "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": "spikex",
"title": "SpikeX - SpaCy Pipes for Knowledge Extraction",
"slogan": "Use SpikeX to build knowledge extraction tools with almost-zero effort",
"description": "SpikeX is a collection of pipes ready to be plugged in a spaCy pipeline. It aims to help in building knowledge extraction tools with almost-zero effort.",
"github": "erre-quadro/spikex",
"pip": "spikex",
"code_example": [
"from spacy import load as spacy_load",
"from spikex.wikigraph import load as wg_load",
"from spikex.pipes import WikiPageX",
"",
"# load a spacy model and get a doc",
"nlp = spacy_load('en_core_web_sm')",
"doc = nlp('An apple a day keeps the doctor away')",
"# load a WikiGraph",
"wg = wg_load('simplewiki_core')",
"# get a WikiPageX and extract all pages",
"wikipagex = WikiPageX(wg)",
"doc = wikipagex(doc)",
"# see all pages extracted from the doc",
"for span in doc._.wiki_spans:",
" print(span._.wiki_pages)"
],
"category": ["pipeline", "standalone"],
"author": "Erre Quadro",
"author_links": {
"github": "erre-quadro",
"website": "https://www.errequadrosrl.com"
}
},
{
"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-ray",
"title": "spacy-ray",
"slogan": "Parallel and distributed training with spaCy and Ray",
"description": "[Ray](https://ray.io/) is a fast and simple framework for building and running **distributed applications**. This very lightweight extension package lets you use Ray for parallel and distributed training with spaCy. If `spacy-ray` is installed in the same environment as spaCy, it will automatically add `spacy ray` commands to your spaCy CLI.",
"github": "explosion/spacy-ray",
"pip": "spacy-ray",
"category": ["training"],
"author": "Explosion / Anyscale",
"thumb": "https://i.imgur.com/7so6ZpS.png"
},
{
"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",
"pip": "spacy-readability",
"code_example": [
"import spacy",
"from spacy_readability import Readability",
"",
"nlp = spacy.load('en')",
"read = Readability(nlp)",
"nlp.add_pipe(read, last=True)",
"doc = nlp(\"I am some really difficult text to read because I use obnoxiously large words.\")",
"doc._.flesch_kincaid_grade_level",
"doc._.flesch_kincaid_reading_ease",
"doc._.dale_chall"
],
"author": "Michael Holtzscher",
"author_links": {
"github": "mholtzscher"
},
"category": ["pipeline"]
},
{
"id": "spacy-sentence-segmenter",
"title": "Sentence Segmenter",
"slogan": "Custom sentence segmentation for spaCy",
"code_example": [
"from seg.newline.segmenter import NewLineSegmenter",
"import spacy",
"",
"nlseg = NewLineSegmenter()",
"nlp = spacy.load('en')",
"nlp.add_pipe(nlseg.set_sent_starts, name='sentence_segmenter', before='parser')",
"doc = nlp(my_doc_text)"
],
"author": "tc64",
"author_links": {
"github": "tc64"
},
"category": ["pipeline"]
},
{
"id": "spacy_cld",
"title": "spaCy-CLD",
"slogan": "Add language detection to your spaCy pipeline using CLD2",
"description": "spaCy-CLD operates on `Doc` and `Span` spaCy objects. When called on a `Doc` or `Span`, the object is given two attributes: `languages` (a list of up to 3 language codes) and `language_scores` (a dictionary mapping language codes to confidence scores between 0 and 1).\n\nspacy-cld is a little extension that wraps the [PYCLD2](https://github.com/aboSamoor/pycld2) Python library, which in turn wraps the [Compact Language Detector 2](https://github.com/CLD2Owners/cld2) C library originally built at Google for the Chromium project. CLD2 uses character n-grams as features and a Naive Bayes classifier to identify 80+ languages from Unicode text strings (or XML/HTML). It can detect up to 3 different languages in a given document, and reports a confidence score (reported in with each language.",
"github": "nickdavidhaynes/spacy-cld",
"pip": "spacy_cld",
"code_example": [
"import spacy",
"from spacy_cld import LanguageDetector",
"",
"nlp = spacy.load('en')",
"language_detector = LanguageDetector()",
"nlp.add_pipe(language_detector)",
"doc = nlp('This is some English text.')",
"",
"doc._.languages # ['en']",
"doc._.language_scores['en'] # 0.96"
],
"author": "Nicholas D Haynes",
"author_links": {
"github": "nickdavidhaynes"
},
"category": ["pipeline"]
},
{
"id": "spacy-iwnlp",
"slogan": "German lemmatization with IWNLP",
"description": "This package uses the [spaCy 2.0 extensions](https://spacy.io/usage/processing-pipelines#extensions) to add [IWNLP-py](https://github.com/Liebeck/iwnlp-py) as German lemmatizer directly into your spaCy pipeline.",
"github": "Liebeck/spacy-iwnlp",
"pip": "spacy-iwnlp",
"code_example": [
"import spacy",
"from spacy_iwnlp import spaCyIWNLP",
"",
"nlp = spacy.load('de')",
"iwnlp = spaCyIWNLP(lemmatizer_path='data/IWNLP.Lemmatizer_20170501.json')",
"nlp.add_pipe(iwnlp)",
"doc = nlp('Wir mögen Fußballspiele mit ausgedehnten Verlängerungen.')",
"for token in doc:",
" print('POS: {}\tIWNLP:{}'.format(token.pos_, token._.iwnlp_lemmas))"
],
"author": "Matthias Liebeck",
"author_links": {
"github": "Liebeck"
},
"category": ["pipeline"],
"tags": ["lemmatizer", "german"]
},
{
"id": "spacy-sentiws",
"slogan": "German sentiment scores with SentiWS",
"description": "This package uses the [spaCy 2.0 extensions](https://spacy.io/usage/processing-pipelines#extensions) to add [SentiWS](http://wortschatz.uni-leipzig.de/en/download) as German sentiment score directly into your spaCy pipeline.",
"github": "Liebeck/spacy-sentiws",
"pip": "spacy-sentiws",
"code_example": [
"import spacy",
"from spacy_sentiws import spaCySentiWS",
"",
"nlp = spacy.load('de_core_news_sm')",
"nlp.add_pipe('sentiws', config={'sentiws_path': 'data/sentiws'})",
"doc = nlp('Die Dummheit der Unterwerfung blüht in hübschen Farben.')",
"",
"for token in doc:",
" print('{}, {}, {}'.format(token.text, token._.sentiws, token.pos_))"
],
"author": "Matthias Liebeck",
"author_links": {
"github": "Liebeck"
},
"category": ["pipeline"],
"tags": ["sentiment", "german"]
},
{
"id": "spacy-lefff",
"slogan": "POS and French lemmatization with Lefff",
"description": "spacy v2.0 extension and pipeline component for adding a French POS and lemmatizer based on [Lefff](https://hal.inria.fr/inria-00521242/).",
"github": "sammous/spacy-lefff",
"pip": "spacy-lefff",
"code_example": [
"import spacy",
"from spacy_lefff import LefffLemmatizer, POSTagger",
"",
"nlp = spacy.load('fr')",
"pos = POSTagger()",
"french_lemmatizer = LefffLemmatizer(after_melt=True)",
"nlp.add_pipe(pos, name='pos', after='parser')",
"nlp.add_pipe(french_lemmatizer, name='lefff', after='pos')",
"doc = nlp(u\"Paris est une ville très chère.\")",
"for d in doc:",
" print(d.text, d.pos_, d._.melt_tagger, d._.lefff_lemma, d.tag_, d.lemma_)"
],
"author": "Sami Moustachir",
"author_links": {
"github": "sammous"
},
"category": ["pipeline"],
"tags": ["pos", "lemmatizer", "french"]
},
{
"id": "lemmy",
"title": "Lemmy",
"slogan": "A Danish lemmatizer",
"description": "Lemmy is a lemmatizer for Danish 🇩🇰 . It comes already trained on Dansk Sprognævns (DSN) word list (‘fuldformliste’) and the Danish Universal Dependencies and is ready for use. Lemmy also supports training on your own dataset. The model currently included in Lemmy was evaluated on the Danish Universal Dependencies dev dataset and scored an accruacy > 99%.\n\nYou can use Lemmy as a spaCy extension, more specifcally a spaCy pipeline component. This is highly recommended and makes the lemmas easily accessible from the spaCy tokens. Lemmy makes use of POS tags to predict the lemmas. When wired up to the spaCy pipeline, Lemmy has the benefit of using spaCy’s builtin POS tagger.",
"github": "sorenlind/lemmy",
"pip": "lemmy",
"code_example": [
"import da_custom_model as da # name of your spaCy model",
"import lemmy.pipe",
"nlp = da.load()",
"",
"# create an instance of Lemmy's pipeline component for spaCy",
"pipe = lemmy.pipe.load()",
"",
"# add the comonent to the spaCy pipeline.",
"nlp.add_pipe(pipe, after='tagger')",
"",
"# lemmas can now be accessed using the `._.lemma` attribute on the tokens",
"nlp(\"akvariernes\")[0]._.lemma"
],
"thumb": "https://i.imgur.com/RJVFRWm.jpg",
"author": "Søren Lind Kristiansen",
"author_links": {
"github": "sorenlind"
},
"category": ["pipeline"],
"tags": ["lemmatizer", "danish"]
},
{
"id": "augmenty",
"title": "Augmenty",
"slogan": "The cherry on top of your NLP pipeline",
"description": "Augmenty is an augmentation library based on spaCy for augmenting texts. Augmenty differs from other augmentation libraries in that it corrects (as far as possible) the token, sentence and document labels under the augmentation.",
"github": "kennethenevoldsen/augmenty",
"pip": "augmenty",
"code_example": [
"import spacy",
"import augmenty",
"",
"nlp = spacy.load('en_core_web_md')",
"",
"docs = nlp.pipe(['Augmenty is a great tool for text augmentation'])",
"",
"ent_dict = {'ORG': [['spaCy'], ['spaCy', 'Universe']]}",
"entity_augmenter = augmenty.load('ents_replace.v1',",
" ent_dict = ent_dict, level=1)",
"",
"for doc in augmenty.docs(docs, augmenter=entity_augmenter, nlp=nlp):",
" print(doc)"
],
"thumb": "https://github.com/KennethEnevoldsen/augmenty/blob/master/img/icon.png?raw=true",
"author": "Kenneth Enevoldsen",
"author_links": {
"github": "kennethenevoldsen",
"website": "https://www.kennethenevoldsen.com"
},
"category": ["training", "research"],
"tags": ["training", "research", "augmentation"]
},
{
"id": "dacy",
"title": "DaCy",
"slogan": "An efficient Pipeline for Danish NLP",
"description": "DaCy is a Danish preprocessing pipeline trained in SpaCy. It has achieved State-of-the-Art performance on Named entity recognition, part-of-speech tagging and dependency parsing for Danish. This repository contains material for using the DaCy, reproducing the results and guides on usage of the package. Furthermore, it also contains a series of behavioural test for biases and robustness of Danish NLP pipelines.",
"github": "centre-for-humanities-computing/DaCy",
"pip": "dacy",
"code_example": [
"import dacy",
"print(dacy.models()) # get a list of dacy models",
"nlp = dacy.load('medium') # load your spacy pipeline",
"",
"# DaCy also includes functionality for adding other Danish models to the pipeline",
"# For instance you can add the BertTone model for classification of sentiment polarity to the pipeline:",
"nlp = add_berttone_polarity(nlp)"
],
"thumb": "https://github.com/centre-for-humanities-computing/DaCy/blob/main/img/icon_no_title.png?raw=true",
"author": "Centre for Humanities Computing Aarhus",
"author_links": {
"github": "centre-for-humanities-computing",
"website": "https://chcaa.io/#/"
},
"category": ["pipeline"],
"tags": ["pipeline", "danish"]
},
{
"id": "spacy-wrap",
"title": "spaCy-wrap",
"slogan": "For Wrapping fine-tuned transformers in spaCy pipelines",
"description": "spaCy-wrap is a wrapper library for spaCy for including fine-tuned transformers from Huggingface in your spaCy pipeline allowing inclusion of existing models within existing workflows.",
"github": "kennethenevoldsen/spacy-wrap",
"pip": "spacy_wrap",
"code_example": [
"import spacy",
"import spacy_wrap",
"",
"nlp = spacy.blank('en')",
"config = {",
" 'doc_extension_trf_data': 'clf_trf_data', # document extention for the forward pass",
" 'doc_extension_prediction': 'sentiment', # document extention for the prediction",
" 'labels': ['negative', 'neutral', 'positive'],",
" 'model': {",
" 'name': 'cardiffnlp/twitter-roberta-base-sentiment', # the model name or path of huggingface model",
"},",
"}",
"",
"transformer = nlp.add_pipe('classification_transformer', config=config)",
"transformer.model.initialize()",
"",
"doc = nlp('spaCy is a wonderful tool')",
"",
"print(doc._.clf_trf_data)",
"# TransformerData(wordpieces=...",
"print(doc._.sentiment)",
"# 'positive'",
"print(doc._.sentiment_prob)",
"# {'prob': array([0.004, 0.028, 0.969], dtype=float32), 'labels': ['negative', 'neutral', 'positive']}"
],
"thumb": "https://raw.githubusercontent.com/KennethEnevoldsen/spacy-wrap/main/docs/_static/icon.png",
"author": "Kenneth Enevoldsen",
"author_links": {
"github": "KennethEnevoldsen",
"website": "https://www.kennethenevoldsen.com"
},
"category": ["pipeline", "models", "training"],
"tags": ["pipeline", "models", "transformers"]
},
{
"id": "asent",
"title": "Asent",
"slogan": "Fast, flexible and transparent sentiment analysis",
"description": "Asent is a rule-based sentiment analysis library for Python made using spaCy. It is inspired by VADER, but uses a more modular ruleset, that allows the user to change e.g. the method for finding negations. Furthermore it includes visualisers to visualize the model predictions, making the model easily interpretable.",
"github": "kennethenevoldsen/asent",
"pip": "aseny",
"code_example": [
"import spacy",
"import asent",
"",
"# load spacy pipeline",
"nlp = spacy.blank('en')",
"nlp.add_pipe('sentencizer')",
"",
"# add the rule-based sentiment model",
"nlp.add_pipe('asent_en_v1')",
"",
"# try an example",
"text = 'I am not very happy, but I am also not especially sad'",
"doc = nlp(text)",
"",
"# print polarity of document, scaled to be between -1, and 1",
"print(doc._.polarity)",
"# neg=0.0 neu=0.631 pos=0.369 compound=0.7526",
"",
"# Naturally, a simple score can be quite unsatisfying, thus Asent implements a series of visualizer to interpret the results:",
"asent.visualize(doc, style='prediction')",
" # or",
"asent.visualize(doc[:5], style='analysis')"
],
"thumb": "https://github.com/KennethEnevoldsen/asent/raw/main/docs/img/logo_black_font.png?raw=true",
"author": "Kenneth Enevoldsen",
"author_links": {
"github": "KennethEnevoldsen",
"website": "https://www.kennethenevoldsen.com"
},
"category": ["pipeline", "models"],
"tags": ["pipeline", "models", "sentiment"]
},
{
"id": "textdescriptives",
"title": "TextDescriptives",
"slogan": "Extraction of descriptive stats, readability, and syntactic complexity measures",
"description": "Pipeline component for spaCy v.3 that calculates descriptive statistics, readability metrics, and syntactic complexity (dependency distance).",
"github": "HLasse/TextDescriptives",
"pip": "textdescriptives",
"code_example": [
"import spacy",
"import textdescriptives as td",
"nlp = spacy.load('en_core_web_sm')",
"nlp.add_pipe('textdescriptives')",
"doc = nlp('This is a short test text')",
"doc._.readability # access some of the values",
"td.extract_df(doc) # extract all metrics to DataFrame"
],
"author": "Lasse Hansen, Kenneth Enevoldsen, Ludvig Olsen",
"author_links": {
"github": "HLasse"
},
"category": ["pipeline"],
"tags": ["pipeline", "readability", "syntactic complexity", "descriptive statistics"]
},
{
"id": "neuralcoref",
"slogan": "State-of-the-art coreference resolution based on neural nets and spaCy",
"description": "This coreference resolution module is based on the super fast [spaCy](https://spacy.io/) parser and uses the neural net scoring model described in [Deep Reinforcement Learning for Mention-Ranking Coreference Models](http://cs.stanford.edu/people/kevclark/resources/clark-manning-emnlp2016-deep.pdf) by Kevin Clark and Christopher D. Manning, EMNLP 2016. Since ✨Neuralcoref v2.0, you can train the coreference resolution system on your own dataset — e.g., another language than English! — **provided you have an annotated dataset**. Note that to use neuralcoref with spaCy > 2.1.0, you'll have to install neuralcoref from source.",
"github": "huggingface/neuralcoref",
"thumb": "https://i.imgur.com/j6FO9O6.jpg",
"code_example": [
"import spacy",
"import neuralcoref",
"",
"nlp = spacy.load('en')",
"neuralcoref.add_to_pipe(nlp)",
"doc1 = nlp('My sister has a dog. She loves him.')",
"print(doc1._.coref_clusters)",
"",
"doc2 = nlp('Angela lives in Boston. She is quite happy in that city.')",
"for ent in doc2.ents:",
" print(ent._.coref_cluster)"
],
"author": "Hugging Face",
"author_links": {
"github": "huggingface"
},
"category": ["standalone", "conversational", "models"],
"tags": ["coref"]
},
{
"id": "neuralcoref-vizualizer",
"title": "Neuralcoref Visualizer",
"slogan": "State-of-the-art coreference resolution based on neural nets and spaCy",
"description": "In short, coreference is the fact that two or more expressions in a text – like pronouns or nouns – link to the same person or thing. It is a classical Natural language processing task, that has seen a revival of interest in the past two years as several research groups applied cutting-edge deep-learning and reinforcement-learning techniques to it. It is also one of the key building blocks to building conversational Artificial intelligences.",
"url": "https://huggingface.co/coref/",
"image": "https://i.imgur.com/3yy4Qyf.png",
"thumb": "https://i.imgur.com/j6FO9O6.jpg",
"github": "huggingface/neuralcoref",
"category": ["visualizers", "conversational"],
"tags": ["coref", "chatbots"],
"author": "Hugging Face",
"author_links": {
"github": "huggingface"
}
},
{
"id": "matcher-explorer",
"title": "Rule-based Matcher Explorer",
"slogan": "Test spaCy's rule-based Matcher by creating token patterns interactively",
"description": "Test spaCy's rule-based `Matcher` by creating token patterns interactively and running them over your text. Each token can set multiple attributes like text value, part-of-speech tag or boolean flags. The token-based view lets you explore how spaCy processes your text – and why your pattern matches, or why it doesn't. For more details on rule-based matching, see the [documentation](https://spacy.io/usage/rule-based-matching).",
"image": "https://explosion.ai/assets/img/demos/matcher.png",
"thumb": "https://i.imgur.com/rPK4AGt.jpg",
"url": "https://explosion.ai/demos/matcher",
"author": "Ines Montani",
"author_links": {
"twitter": "_inesmontani",
"github": "ines",
"website": "https://ines.io"
},
"category": ["visualizers"]
},
{
"id": "displacy",
"title": "displaCy",
"slogan": "A modern syntactic dependency visualizer",
"description": "Visualize spaCy's guess at the syntactic structure of a sentence. Arrows point from children to heads, and are labelled by their relation type.",
"url": "https://explosion.ai/demos/displacy",
"thumb": "https://i.imgur.com/nxDcHaL.jpg",
"image": "https://explosion.ai/assets/img/demos/displacy.png",
"author": "Ines Montani",
"author_links": {
"twitter": "_inesmontani",
"github": "ines",
"website": "https://ines.io"
},
"category": ["visualizers"]
},
{
"id": "displacy-ent",
"title": "displaCy ENT",
"slogan": "A modern named entity visualizer",
"description": "Visualize spaCy's guess at the named entities in the document. You can filter the displayed types, to only show the annotations you're interested in.",
"url": "https://explosion.ai/demos/displacy-ent",
"thumb": "https://i.imgur.com/A77Ecbs.jpg",
"image": "https://explosion.ai/assets/img/demos/displacy-ent.png",
"author": "Ines Montani",
"author_links": {
"twitter": "_inesmontani",
"github": "ines",
"website": "https://ines.io"
},
"category": ["visualizers"]
},
{
"id": "explacy",
"slogan": "A small tool that explains spaCy parse results",
"github": "tylerneylon/explacy",
"thumb": "https://i.imgur.com/V1hCWmn.jpg",
"image": "https://raw.githubusercontent.com/tylerneylon/explacy/master/img/screenshot.png",
"code_example": [
"import spacy",
"import explacy",
"",
"nlp = spacy.load('en')",
"explacy.print_parse_info(nlp, 'The salad was surprisingly tasty.')"
],
"author": "Tyler Neylon",
"author_links": {
"github": "tylerneylon"
},
"category": ["visualizers"]
},
{
"id": "deplacy",
"slogan": "CUI-based Tree Visualizer for Universal Dependencies and Immediate Catena Analysis",
"description": "Simple dependency visualizer for [spaCy](https://spacy.io/), [UniDic2UD](https://pypi.org/project/unidic2ud), [Stanza](https://stanfordnlp.github.io/stanza/), [NLP-Cube](https://github.com/Adobe/NLP-Cube), [Trankit](https://github.com/nlp-uoregon/trankit), etc.",
"github": "KoichiYasuoka/deplacy",
"image": "https://i.imgur.com/6uOI4Op.png",
"code_example": [
"import spacy",
"import deplacy",
"",
"nlp=spacy.load('en_core_web_sm')",
"doc=nlp('I saw a horse yesterday which had no name.')",
"deplacy.render(doc)"
],
"author": "Koichi Yasuoka",
"author_links": {
"github": "KoichiYasuoka"
},
"category": ["visualizers"]
},
{
"id": "scattertext",
"slogan": "Beautiful visualizations of how language differs among document types",
"description": "A tool for finding distinguishing terms in small-to-medium-sized corpora, and presenting them in a sexy, interactive scatter plot with non-overlapping term labels. Exploratory data analysis just got more fun.",
"github": "JasonKessler/scattertext",
"image": "https://jasonkessler.github.io/2012conventions0.0.2.2.png",
"code_example": [
"import spacy",
"import scattertext as st",
"",
"nlp = spacy.load('en')",
"corpus = st.CorpusFromPandas(convention_df,",
" category_col='party',",
" text_col='text',",
" nlp=nlp).build()"
],
"author": "Jason Kessler",
"author_links": {
"github": "JasonKessler",
"twitter": "jasonkessler"
},
"category": ["visualizers"]
},
{
"id": "rasa",
"title": "Rasa",
"slogan": "Turn natural language into structured data",
"description": "Machine learning tools for developers to build, improve, and deploy contextual chatbots and assistants. Powered by open source.",
"github": "RasaHQ/rasa",
"pip": "rasa",
"thumb": "https://i.imgur.com/TyZnpwL.png",
"url": "https://rasa.com/",
"author": "Rasa",
"author_links": {
"github": "RasaHQ"
},
"category": ["conversational"],
"tags": ["chatbots"]
},
{
"id": "mindmeld",
"title": "MindMeld - Conversational AI platform",
"slogan": "Conversational AI platform for deep-domain voice interfaces and chatbots",
"description": "The MindMeld Conversational AI platform is among the most advanced AI platforms for building production-quality conversational applications. It is a Python-based machine learning framework which encompasses all of the algorithms and utilities required for this purpose. (https://github.com/cisco/mindmeld)",
"github": "cisco/mindmeld",
"pip": "mindmeld",
"thumb": "https://www.mindmeld.com/img/mindmeld-logo.png",
"category": ["conversational", "ner"],
"tags": ["chatbots"],
"author": "Cisco",
"author_links": {
"github": "cisco/mindmeld",
"website": "https://www.mindmeld.com/"
}
},
{
"id": "torchtext",
"title": "torchtext",
"slogan": "Data loaders and abstractions for text and NLP",
"github": "pytorch/text",
"pip": "torchtext",
"thumb": "https://i.imgur.com/WFkxuPo.png",
"code_example": [
">>> pos = data.TabularDataset(",
"... path='data/pos/pos_wsj_train.tsv', format='tsv',",
"... fields=[('text', data.Field()),",
"... ('labels', data.Field())])",
"...",
">>> sentiment = data.TabularDataset(",
"... path='data/sentiment/train.json', format='json',",
"... fields={'sentence_tokenized': ('text', data.Field(sequential=True)),",
"... 'sentiment_gold': ('labels', data.Field(sequential=False))})"
],
"category": ["standalone", "research"],
"tags": ["pytorch"]
},
{
"id": "allennlp",
"title": "AllenNLP",
"slogan": "An open-source NLP research library, built on PyTorch and spaCy",
"description": "AllenNLP is a new library designed to accelerate NLP research, by providing a framework that supports modern deep learning workflows for cutting-edge language understanding problems. AllenNLP uses spaCy as a preprocessing component. You can also use Allen NLP to develop spaCy pipeline components, to add annotations to the `Doc` object.",
"github": "allenai/allennlp",
"pip": "allennlp",
"thumb": "https://i.imgur.com/U8opuDN.jpg",
"url": "http://allennlp.org",
"author": " Allen Institute for Artificial Intelligence",
"author_links": {
"github": "allenai",
"twitter": "allenai_org",
"website": "http://allenai.org"
},
"category": ["standalone", "research"]
},
{
"id": "scispacy",
"title": "scispaCy",
"slogan": "A full spaCy pipeline and models for scientific/biomedical documents",
"github": "allenai/scispacy",
"pip": "scispacy",
"thumb": "https://i.imgur.com/dJQSclW.png",
"url": "https://allenai.github.io/scispacy/",
"author": " Allen Institute for Artificial Intelligence",
"author_links": {
"github": "allenai",
"twitter": "allenai_org",
"website": "http://allenai.org"
},
"category": ["scientific", "models", "research"]
},
{
"id": "textacy",
"slogan": "NLP, before and after spaCy",
"description": "`textacy` is a Python library for performing a variety of natural language processing (NLP) tasks, built on the high-performance `spacy` library. With the fundamentals – tokenization, part-of-speech tagging, dependency parsing, etc. – delegated to another library, `textacy` focuses on the tasks that come before and follow after.",
"github": "chartbeat-labs/textacy",
"pip": "textacy",
"url": "https://github.com/chartbeat-labs/textacy",
"author": "Burton DeWilde",
"author_links": {
"github": "bdewilde",
"twitter": "bjdewilde"
},
"category": ["standalone"]
},
{
"id": "textpipe",
"slogan": "clean and extract metadata from text",
"description": "`textpipe` is a Python package for converting raw text in to clean, readable text and extracting metadata from that text. Its functionalities include transforming raw text into readable text by removing HTML tags and extracting metadata such as the number of words and named entities from the text.",
"github": "textpipe/textpipe",
"pip": "textpipe",
"author": "Textpipe Contributors",
"author_links": {
"github": "textpipe",
"website": "https://github.com/textpipe/textpipe/blob/master/CONTRIBUTORS.md"
},
"category": ["standalone"],
"tags": ["text-processing", "named-entity-recognition"],
"thumb": "https://avatars0.githubusercontent.com/u/40492530",
"code_example": [
"from textpipe import doc, pipeline",
"sample_text = 'Sample text! <!DOCTYPE>'",
"document = doc.Doc(sample_text)",
"print(document.clean)",
"'Sample text!'",
"print(document.language)",
"# 'en'",
"print(document.nwords)",
"# 2",
"",
"pipe = pipeline.Pipeline(['CleanText', 'NWords'])",
"print(pipe(sample_text))",
"# {'CleanText': 'Sample text!', 'NWords': 2}"
]
},
{
"id": "mordecai",
"slogan": "Full text geoparsing using spaCy, Geonames and Keras",
"description": "Extract the place names from a piece of text, resolve them to the correct place, and return their coordinates and structured geographic information.",
"github": "openeventdata/mordecai",
"pip": "mordecai",
"thumb": "https://i.imgur.com/gPJ9upa.jpg",
"code_example": [
"from mordecai import Geoparser",
"geo = Geoparser()",
"geo.geoparse(\"I traveled from Oxford to Ottawa.\")"
],
"author": "Andy Halterman",
"author_links": {
"github": "ahalterman",
"twitter": "ahalterman"
},
"category": ["standalone", "scientific"]
},
{
"id": "kindred",
"title": "Kindred",
"slogan": "Biomedical relation extraction using spaCy",
"description": "Kindred is a package for relation extraction in biomedical texts. Given some training data, it can build a model to identify relations between entities (e.g. drugs, genes, etc) in a sentence.",
"github": "jakelever/kindred",
"pip": "kindred",
"code_example": [
"import kindred",
"",
"trainCorpus = kindred.bionlpst.load('2016-BB3-event-train')",
"devCorpus = kindred.bionlpst.load('2016-BB3-event-dev')",
"predictionCorpus = devCorpus.clone()",
"predictionCorpus.removeRelations()",
"classifier = kindred.RelationClassifier()",
"classifier.train(trainCorpus)",
"classifier.predict(predictionCorpus)",
"f1score = kindred.evaluate(devCorpus, predictionCorpus, metric='f1score')"
],
"author": "Jake Lever",
"author_links": {
"github": "jakelever"
},
"category": ["standalone", "scientific"]
},
{
"id": "sense2vec",
"slogan": "Use NLP to go beyond vanilla word2vec",
"description": "sense2vec ([Trask et. al](https://arxiv.org/abs/1511.06388), 2015) is a nice twist on [word2vec](https://en.wikipedia.org/wiki/Word2vec) that lets you learn more interesting, detailed and context-sensitive word vectors. For an interactive example of the technology, see our [sense2vec demo](https://explosion.ai/demos/sense2vec) that lets you explore semantic similarities across all Reddit comments of 2015.",
"github": "explosion/sense2vec",
"pip": "sense2vec==1.0.0a1",
"thumb": "https://i.imgur.com/awfdhX6.jpg",
"image": "https://explosion.ai/assets/img/demos/sense2vec.png",
"url": "https://explosion.ai/demos/sense2vec",
"code_example": [
"import spacy",
"",
"nlp = spacy.load(\"en_core_web_sm\")",
"s2v = nlp.add_pipe(\"sense2vec\")",
"s2v.from_disk(\"/path/to/s2v_reddit_2015_md\")",
"",
"doc = nlp(\"A sentence about natural language processing.\")",
"assert doc[3:6].text == \"natural language processing\"",
"freq = doc[3:6]._.s2v_freq",
"vector = doc[3:6]._.s2v_vec",
"most_similar = doc[3:6]._.s2v_most_similar(3)",
"# [(('machine learning', 'NOUN'), 0.8986967),",
"# (('computer vision', 'NOUN'), 0.8636297),",
"# (('deep learning', 'NOUN'), 0.8573361)]"
],
"category": ["pipeline", "standalone", "visualizers"],
"tags": ["vectors"],
"author": "Explosion",
"author_links": {
"twitter": "explosion_ai",
"github": "explosion",
"website": "https://explosion.ai"
}
},
{
"id": "spacyr",
"slogan": "An R wrapper for spaCy",
"github": "quanteda/spacyr",
"cran": "spacyr",
"code_example": [
"library(\"spacyr\")",
"spacy_initialize()",
"",
"txt <- c(d1 = \"spaCy excels at large-scale information extraction tasks.\",",
" d2 = \"Mr. Smith goes to North Carolina.\")",
"",
"# process documents and obtain a data.table",
"parsedtxt <- spacy_parse(txt)"
],
"code_language": "r",
"author": "Kenneth Benoit & Aki Matsuo",
"category": ["nonpython"]
},
{
"id": "cleannlp",
"title": "CleanNLP",
"slogan": "A tidy data model for NLP in R",
"description": "The cleanNLP package is designed to make it as painless as possible to turn raw text into feature-rich data frames. the package offers four backends that can be used for parsing text: `tokenizers`, `udpipe`, `spacy` and `corenlp`.",
"github": "statsmaths/cleanNLP",
"cran": "cleanNLP",
"author": "Taylor B. Arnold",
"author_links": {
"github": "statsmaths"
},
"category": ["nonpython"]
},
{
"id": "spacy-cpp",
"slogan": "C++ wrapper library for spaCy",
"description": "The goal of spacy-cpp is to expose the functionality of spaCy to C++ applications, and to provide an API that is similar to that of spaCy, enabling rapid development in Python and simple porting to C++.",
"github": "d99kris/spacy-cpp",
"code_example": [
"Spacy::Spacy spacy;",
"auto nlp = spacy.load(\"en_core_web_sm\");",
"auto doc = nlp.parse(\"This is a sentence.\");",
"for (auto& token : doc.tokens())",
" std::cout << token.text() << \" [\" << token.pos_() << \"]\\n\";"
],
"code_language": "cpp",
"author": "Kristofer Berggren",
"author_links": {
"github": "d99kris"
},
"category": ["nonpython"]
},
{
"id": "ruby-spacy",
"title": "ruby-spacy",
"slogan": "Wrapper module for using spaCy from Ruby via PyCall",
"description": "ruby-spacy is a wrapper module for using spaCy from the Ruby programming language via PyCall. This module aims to make it easy and natural for Ruby programmers to use spaCy.",
"github": "yohasebe/ruby-spacy",
"code_example": [
"require \"ruby-spacy\"",
"require \"terminal-table\"",
"nlp = Spacy::Language.new(\"en_core_web_sm\")",
"doc = nlp.read(\"Apple is looking at buying U.K. startup for $1 billion\")",
"headings = [\"text\", \"lemma\", \"pos\", \"tag\", \"dep\"]",
"rows = []",
"doc.each do |token|",
" rows << [token.text, token.lemma, token.pos, token.tag, token.dep]",
"end",
"table = Terminal::Table.new rows: rows, headings: headings",
"puts table"
],
"code_language": "ruby",
"url": "https://rubygems.org/gems/ruby-spacy",
"author": "Yoichiro Hasebe",
"author_links": {
"github": "yohasebe",
"twitter": "yohasebe"
},
"category": ["nonpython"],
"tags": ["ruby"]
},
{
"id": "spacy_api",
"slogan": "Server/client to load models in a separate, dedicated process",
"github": "kootenpv/spacy_api",
"pip": "spacy_api",
"code_example": [
"from spacy_api import Client",
"",
"spacy_client = Client() # default args host/port",
"doc = spacy_client.single(\"How are you\")"
],
"author": "Pascal van Kooten",
"author_links": {
"github": "kootenpv"
},
"category": ["apis"]
},
{
"id": "spacy-api-docker",
"slogan": "spaCy REST API, wrapped in a Docker container",
"github": "jgontrum/spacy-api-docker",
"url": "https://hub.docker.com/r/jgontrum/spacyapi/",
"thumb": "https://i.imgur.com/NRnDKyj.jpg",
"code_example": [
"version: '2'",
"",
"services:",
" spacyapi:",
" image: jgontrum/spacyapi:en_v2",
" ports:",
" - \"127.0.0.1:8080:80\"",
" restart: always"
],
"code_language": "docker",
"author": "Johannes Gontrum",
"author_links": {
"github": "jgontrum"
},
"category": ["apis"]
},
{
"id": "spacy-nlp",
"slogan": " Expose spaCy NLP text parsing to Node.js (and other languages) via Socket.IO",
"github": "kengz/spacy-nlp",
"thumb": "https://i.imgur.com/w41VSr7.jpg",
"code_example": [
"const spacyNLP = require(\"spacy-nlp\")",
"// default port 6466",
"// start the server with the python client that exposes spacyIO (or use an existing socketIO server at IOPORT)",
"var serverPromise = spacyNLP.server({ port: process.env.IOPORT });",
"// Loading spacy may take up to 15s"
],
"code_language": "javascript",
"author": "Wah Loon Keng",
"author_links": {
"github": "kengz"
},
"category": ["apis", "nonpython"]
},
{
"id": "prodigy",
"title": "Prodigy",
"slogan": "Radically efficient machine teaching, powered by active learning",
"description": "Prodigy is an annotation tool so efficient that data scientists can do the annotation themselves, enabling a new level of rapid iteration. Whether you're working on entity recognition, intent detection or image classification, Prodigy can help you train and evaluate your models faster. Stream in your own examples or real-world data from live APIs, update your model in real-time and chain models together to build more complex systems.",
"thumb": "https://i.imgur.com/UVRtP6g.jpg",
"image": "https://i.imgur.com/Dt5vrY6.png",
"url": "https://prodi.gy",
"code_example": [
"prodigy dataset ner_product \"Improve PRODUCT on Reddit data\"",
"✨ Created dataset 'ner_product'.",
"",
"prodigy ner.teach ner_product en_core_web_sm ~/data.jsonl --label PRODUCT",
"✨ Starting the web server on port 8080..."
],
"code_language": "bash",
"category": ["standalone", "training"],
"author": "Explosion",
"author_links": {
"twitter": "explosion_ai",
"github": "explosion",
"website": "https://explosion.ai"
}
},
{
"id": "dragonfire",
"title": "Dragonfire",
"slogan": "An open-source virtual assistant for Ubuntu based Linux distributions",
"github": "DragonComputer/Dragonfire",
"thumb": "https://i.imgur.com/5fqguKS.jpg",
"image": "https://raw.githubusercontent.com/DragonComputer/Dragonfire/master/docs/img/demo.gif",
"author": "Dragon Computer",
"author_links": {
"github": "DragonComputer",
"website": "http://dragon.computer"
},
"category": ["standalone"]
},
{
"id": "prefect",
"title": "Prefect",
"slogan": "Workflow management system designed for modern infrastructure",
"github": "PrefectHQ/prefect",
"pip": "prefect",
"thumb": "https://i.imgur.com/oLTwr0e.png",
"code_example": [
"from prefect import Flow",
"from prefect.tasks.spacy.spacy_tasks import SpacyNLP",
"import spacy",
"",
"nlp = spacy.load(\"en_core_web_sm\")",
"",
"with Flow(\"Natural Language Processing\") as flow:",
" doc = SpacyNLP(text=\"This is some text\", nlp=nlp)",
"",
"flow.run()"
],
"author": "Prefect",
"author_links": {
"website": "https://prefect.io"
},
"category": ["standalone"]
},
{
"id": "graphbrain",
"title": "Graphbrain",
"slogan": "Automated meaning extraction and text understanding",
"description": "Graphbrain is an Artificial Intelligence open-source software library and scientific research tool. Its aim is to facilitate automated meaning extraction and text understanding, as well as the exploration and inference of knowledge.",
"github": "graphbrain/graphbrain",
"pip": "graphbrain",
"thumb": "https://i.imgur.com/cct9W1E.png",
"author": "Graphbrain",
"category": ["standalone"]
},
{
"type": "education",
"id": "nostarch-nlp-python",
"title": "Natural Language Processing Using Python",
"slogan": "No Starch Press, 2020",
"description": "Natural Language Processing Using Python is an introduction to natural language processing (NLP), the task of converting human language into data that a computer can process. The book uses spaCy, a leading Python library for NLP, to guide readers through common NLP tasks related to generating and understanding human language with code. It addresses problems like understanding a user's intent, continuing a conversation with a human, and maintaining the state of a conversation.",
"cover": "https://i.imgur.com/w0iycjl.jpg",
"url": "https://nostarch.com/NLPPython",
"author": "Yuli Vasiliev",
"category": ["books"]
},
{
"type": "education",
"id": "oreilly-python-ds",
"title": "Introduction to Machine Learning with Python: A Guide for Data Scientists",
"slogan": "O'Reilly, 2016",
"description": "Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.",
"cover": "https://covers.oreillystatic.com/images/0636920030515/lrg.jpg",
"url": "http://shop.oreilly.com/product/0636920030515.do",
"author": "Andreas Müller, Sarah Guido",
"category": ["books"]
},
{
"type": "education",
"id": "text-analytics-python",
"title": "Text Analytics with Python",
"slogan": "Apress / Springer, 2016",
"description": "*Text Analytics with Python* teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems.",
"github": "dipanjanS/text-analytics-with-python",
"cover": "https://i.imgur.com/AOmzZu8.png",
"url": "https://www.amazon.com/Text-Analytics-Python-Real-World-Actionable/dp/148422387X",
"author": "Dipanjan Sarkar",
"category": ["books"]
},
{
"type": "education",
"id": "practical-ml-python",
"title": "Practical Machine Learning with Python",
"slogan": "Apress, 2017",
"description": "Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully.",
"github": "dipanjanS/practical-machine-learning-with-python",
"cover": "https://i.imgur.com/5F4mkt7.jpg",
"url": "https://www.amazon.com/Practical-Machine-Learning-Python-Problem-Solvers/dp/1484232062",
"author": "Dipanjan Sarkar, Raghav Bali, Tushar Sharma",
"category": ["books"]
},
{
"type": "education",
"id": "packt-nlp-computational-linguistics",
"title": "Natural Language Processing and Computational Linguistics",
"slogan": "Packt, 2018",
"description": "This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy.",
"cover": "https://i.imgur.com/aleMf1Y.jpg",
"url": "https://www.amazon.com/Natural-Language-Processing-Computational-Linguistics-ebook/dp/B07BWH779J",
"author": "Bhargav Srinivasa-Desikan",
"category": ["books"]
},
{
"type": "education",
"id": "mastering-spacy",
"title": "Mastering spaCy",
"slogan": "Packt, 2021",
"description": "This is your ultimate spaCy book. Master the crucial skills to use spaCy components effectively to create real-world NLP applications with spaCy. Explaining linguistic concepts such as dependency parsing, POS-tagging and named entity extraction with many examples, this book will help you to conquer computational linguistics with spaCy. The book further focuses on ML topics with Keras and Tensorflow. You'll cover popular topics, including intent recognition, sentiment analysis and context resolution; and use them on popular datasets and interpret the results. A special hands-on section on chatbot design is included.",
"github": "PacktPublishing/Mastering-spaCy",
"cover": "https://tinyimg.io/i/aWEm0dh.jpeg",
"url": "https://www.amazon.com/Mastering-spaCy-end-end-implementing/dp/1800563353",
"author": "Duygu Altinok",
"author_links": {
"github": "DuyguA",
"website": "https://www.linkedin.com/in/duygu-altinok-4021389a"
},
"category": ["books"]
},
{
"type": "education",
"id": "applied-nlp-in-enterprise",
"title": "Applied Natural Language Processing in the Enterprise: Teaching Machines to Read, Write, and Understand",
"slogan": "O'Reilly, 2021",
"description": "Natural language processing (NLP) is one of the hottest topics in AI today. Having lagged behind other deep learning fields such as computer vision for years, NLP only recently gained mainstream popularity. Even though Google, Facebook, and OpenAI have open sourced large pretrained language models to make NLP easier, many organizations today still struggle with developing and productionizing NLP applications. This hands-on guide helps you learn the field quickly.",
"github": "nlpbook/nlpbook",
"cover": "https://i.imgur.com/6RxLBvf.jpg",
"url": "https://www.amazon.com/dp/149206257X",
"author": "Ankur A. Patel",
"author_links": {
"github": "aapatel09",
"website": "https://www.ankurapatel.io"
},
"category": ["books"]
},
{
"type": "education",
"id": "learning-path-spacy",
"title": "Learning Path: Mastering spaCy for Natural Language Processing",
"slogan": "O'Reilly, 2017",
"description": "spaCy, a fast, user-friendly library for teaching computers to understand text, simplifies NLP techniques, such as speech tagging and syntactic dependencies, so you can easily extract information, attributes, and objects from massive amounts of text to then document, measure, and analyze. This Learning Path is a hands-on introduction to using spaCy to discover insights through natural language processing. While end-to-end natural language processing solutions can be complex, you’ll learn the linguistics, algorithms, and machine learning skills to get the job done.",
"url": "https://www.safaribooksonline.com/library/view/learning-path-mastering/9781491986653/",
"thumb": "https://i.imgur.com/9MIgMAc.jpg",
"author": "Aaron Kramer",
"category": ["courses"]
},
{
"type": "education",
"id": "introduction-into-spacy-3",
"title": "Introduction to spaCy 3",
"slogan": "A free course for beginners by Dr. W.J.B. Mattingly",
"url": "http://spacy.pythonhumanities.com/",
"thumb": "https://spacy.pythonhumanities.com/_static/freecodecamp_small.jpg",
"author": "Dr. W.J.B. Mattingly",
"category": ["courses"]
},
{
"type": "education",
"id": "spacy-course",
"title": "Advanced NLP with spaCy",
"slogan": "A free online course",
"description": "In this free interactive course, you'll learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.",
"url": "https://course.spacy.io",
"image": "https://i.imgur.com/JC00pHW.jpg",
"thumb": "https://i.imgur.com/5RXLtrr.jpg",
"author": "Ines Montani",
"author_links": {
"twitter": "_inesmontani",
"github": "ines",
"website": "https://ines.io"
},
"category": ["courses"]
},
{
"type": "education",
"id": "applt-course",
"title": "Applied Language Technology",
"slogan": "NLP for newcomers using spaCy and Stanza",
"description": "These learning materials provide an introduction to applied language technology for audiences who are unfamiliar with language technology and programming. The learning materials assume no previous knowledge of the Python programming language.",
"url": "https://applied-language-technology.mooc.fi",
"image": "https://www.mv.helsinki.fi/home/thiippal/images/applt-preview.jpg",
"thumb": "https://www.mv.helsinki.fi/home/thiippal/images/applt-logo.png",
"author": "Tuomo Hiippala",
"author_links": {
"twitter": "tuomo_h",
"github": "thiippal",
"website": "https://www.mv.helsinki.fi/home/thiippal/"
},
"category": ["courses"]
},
{
"type": "education",
"id": "video-spacys-ner-model",
"title": "spaCy's NER model",
"slogan": "Incremental parsing with bloom embeddings and residual CNNs",
"description": "spaCy v2.0's Named Entity Recognition system features a sophisticated word embedding strategy using subword features and \"Bloom\" embeddings, a deep convolutional neural network with residual connections, and a novel transition-based approach to named entity parsing. The system is designed to give a good balance of efficiency, accuracy and adaptability. In this talk, I sketch out the components of the system, explaining the intuition behind the various choices. I also give a brief introduction to the named entity recognition problem, with an overview of what else Explosion AI is working on, and why.",
"youtube": "sqDHBH9IjRU",
"author": "Matthew Honnibal",
"author_links": {
"twitter": "honnibal",
"github": "honnibal",
"website": "https://explosion.ai"
},
"category": ["videos"]
},
{
"type": "education",
"id": "video-new-nlp-solutions",
"title": "Building new NLP solutions with spaCy and Prodigy",
"slogan": "PyData Berlin 2018",
"description": "In this talk, I will discuss how to address some of the most likely causes of failure for new Natural Language Processing (NLP) projects. My main recommendation is to take an iterative approach: don't assume you know what your pipeline should look like, let alone your annotation schemes or model architectures.",
"author": "Matthew Honnibal",
"author_links": {
"twitter": "honnibal",
"github": "honnibal",
"website": "https://explosion.ai"
},
"youtube": "jpWqz85F_4Y",
"category": ["videos"]
},
{
"type": "education",
"id": "video-modern-nlp-in-python",
"title": "Modern NLP in Python",
"slogan": "PyData DC 2016",
"description": "Academic and industry research in Natural Language Processing (NLP) has progressed at an accelerating pace over the last several years. Members of the Python community have been hard at work moving cutting-edge research out of papers and into open source, \"batteries included\" software libraries that can be applied to practical problems. We'll explore some of these tools for modern NLP in Python.",
"author": "Patrick Harrison",
"youtube": "6zm9NC9uRkk",
"category": ["videos"]
},
{
"type": "education",
"id": "video-spacy-course",
"title": "Advanced NLP with spaCy · A free online course",
"description": "spaCy is a modern Python library for industrial-strength Natural Language Processing. In this free and interactive online course, you'll learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.",
"url": "https://course.spacy.io/en",
"author": "Ines Montani",
"author_links": {
"twitter": "_inesmontani",
"github": "ines"
},
"youtube": "THduWAnG97k",
"category": ["videos"]
},
{
"type": "education",
"id": "video-spacy-course-de",
"title": "Modernes NLP mit spaCy · Ein Gratis-Onlinekurs",
"description": "spaCy ist eine moderne Python-Bibliothek für industriestarkes Natural Language Processing. In diesem kostenlosen und interaktiven Onlinekurs lernst du, mithilfe von spaCy fortgeschrittene Systeme für die Analyse natürlicher Sprache zu entwickeln und dabei sowohl regelbasierte Verfahren, als auch moderne Machine-Learning-Technologie einzusetzen.",
"url": "https://course.spacy.io/de",
"author": "Ines Montani",
"author_links": {
"twitter": "_inesmontani",
"github": "ines"
},
"youtube": "K1elwpgDdls",
"category": ["videos"]
},
{
"type": "education",
"id": "video-spacy-course-es",
"title": "NLP avanzado con spaCy · Un curso en línea gratis",
"description": "spaCy es un paquete moderno de Python para hacer Procesamiento de Lenguaje Natural de potencia industrial. En este curso en línea, interactivo y gratuito, aprenderás a usar spaCy para construir sistemas avanzados de comprensión de lenguaje natural usando enfoques basados en reglas y en machine learning.",
"url": "https://course.spacy.io/es",
"author": "Camila Gutiérrez",
"author_links": {
"twitter": "Mariacamilagl30"
},
"youtube": "RNiLVCE5d4k",
"category": ["videos"]
},
{
"type": "education",
"id": "video-intro-to-nlp-episode-1",
"title": "Intro to NLP with spaCy (1)",
"slogan": "Episode 1: Data exploration",
"description": "In this new video series, data science instructor Vincent Warmerdam gets started with spaCy, an open-source library for Natural Language Processing in Python. His mission: building a system to automatically detect programming languages in large volumes of text. Follow his process from the first idea to a prototype all the way to data collection and training a statistical named entity recogntion model from scratch.",
"author": "Vincent Warmerdam",
"author_links": {
"twitter": "fishnets88",
"github": "koaning"
},
"youtube": "WnGPv6HnBok",
"category": ["videos"]
},
{
"type": "education",
"id": "video-intro-to-nlp-episode-2",
"title": "Intro to NLP with spaCy (2)",
"slogan": "Episode 2: Rule-based Matching",
"description": "In this new video series, data science instructor Vincent Warmerdam gets started with spaCy, an open-source library for Natural Language Processing in Python. His mission: building a system to automatically detect programming languages in large volumes of text. Follow his process from the first idea to a prototype all the way to data collection and training a statistical named entity recogntion model from scratch.",
"author": "Vincent Warmerdam",
"author_links": {
"twitter": "fishnets88",
"github": "koaning"
},
"youtube": "KL4-Mpgbahw",
"category": ["videos"]
},
{
"type": "education",
"id": "video-intro-to-nlp-episode-3",
"title": "Intro to NLP with spaCy (3)",
"slogan": "Episode 2: Evaluation",
"description": "In this new video series, data science instructor Vincent Warmerdam gets started with spaCy, an open-source library for Natural Language Processing in Python. His mission: building a system to automatically detect programming languages in large volumes of text. Follow his process from the first idea to a prototype all the way to data collection and training a statistical named entity recogntion model from scratch.",
"author": "Vincent Warmerdam",
"author_links": {
"twitter": "fishnets88",
"github": "koaning"
},
"youtube": "4V0JDdohxAk",
"category": ["videos"]
},
{
"type": "education",
"id": "video-intro-to-nlp-episode-4",
"title": "Intro to NLP with spaCy (4)",
"slogan": "Episode 4: Named Entity Recognition",
"description": "In this new video series, data science instructor Vincent Warmerdam gets started with spaCy, an open-source library for Natural Language Processing in Python. His mission: building a system to automatically detect programming languages in large volumes of text. Follow his process from the first idea to a prototype all the way to data collection and training a statistical named entity recogntion model from scratch.",
"author": "Vincent Warmerdam",
"author_links": {
"twitter": "fishnets88",
"github": "koaning"
},
"youtube": "IqOJU1-_Fi0",
"category": ["videos"]
},
{
"type": "education",
"id": "video-intro-to-nlp-episode-5",
"title": "Intro to NLP with spaCy (5)",
"slogan": "Episode 5: Rules vs. Machine Learning",
"description": "In this new video series, data science instructor Vincent Warmerdam gets started with spaCy, an open-source library for Natural Language Processing in Python. His mission: building a system to automatically detect programming languages in large volumes of text. Follow his process from the first idea to a prototype all the way to data collection and training a statistical named entity recogntion model from scratch.",
"author": "Vincent Warmerdam",
"author_links": {
"twitter": "fishnets88",
"github": "koaning"
},
"youtube": "f4sqeLRzkPg",
"category": ["videos"]
},
{
"type": "education",
"id": "video-intro-to-nlp-episode-6",
"title": "Intro to NLP with spaCy (6)",
"slogan": "Episode 6: Moving to spaCy v3",
"description": "In this new video series, data science instructor Vincent Warmerdam gets started with spaCy, an open-source library for Natural Language Processing in Python. His mission: building a system to automatically detect programming languages in large volumes of text. Follow his process from the first idea to a prototype all the way to data collection and training a statistical named entity recogntion model from scratch.",
"author": "Vincent Warmerdam",
"author_links": {
"twitter": "fishnets88",
"github": "koaning"
},
"youtube": "k77RrmMaKEI",
"category": ["videos"]
},
{
"type": "education",
"id": "video-spacy-irl-entity-linking",
"title": "Entity Linking functionality in spaCy",
"slogan": "spaCy IRL 2019",
"url": "https://www.youtube.com/playlist?list=PLBmcuObd5An4UC6jvK_-eSl6jCvP1gwXc",
"author": "Sofie Van Landeghem",
"author_links": {
"twitter": "OxyKodit",
"github": "svlandeg"
},
"youtube": "PW3RJM8tDGo",
"category": ["videos"]
},
{
"type": "education",
"id": "video-spacy-irl-lemmatization",
"title": "Rethinking rule-based lemmatization",
"slogan": "spaCy IRL 2019",
"url": "https://www.youtube.com/playlist?list=PLBmcuObd5An4UC6jvK_-eSl6jCvP1gwXc",
"author": "Guadalupe Romero",
"author_links": {
"twitter": "_guadiromero",
"github": "guadi1994"
},
"youtube": "88zcQODyuko",
"category": ["videos"]
},
{
"type": "education",
"id": "video-spacy-irl-scispacy",
"title": "ScispaCy: A spaCy pipeline & models for scientific & biomedical text",
"slogan": "spaCy IRL 2019",
"url": "https://www.youtube.com/playlist?list=PLBmcuObd5An4UC6jvK_-eSl6jCvP1gwXc",
"author": "Mark Neumann",
"author_links": {
"twitter": "MarkNeumannnn",
"github": "DeNeutoy"
},
"youtube": "2_HSKDALwuw",
"category": ["videos"]
},
{
"type": "education",
"id": "podcast-nlp-highlights",
"title": "NLP Highlights #78: Where do corpora come from?",
"slogan": "January 2019",
"description": "Most NLP projects rely crucially on the quality of annotations used for training and evaluating models. In this episode, Matt and Ines of Explosion AI tell us how Prodigy can improve data annotation and model development workflows. Prodigy is an annotation tool implemented as a python library, and it comes with a web application and a command line interface. A developer can define input data streams and design simple annotation interfaces. Prodigy can help break down complex annotation decisions into a series of binary decisions, and it provides easy integration with spaCy models. Developers can specify how models should be modified as new annotations come in in an active learning framework.",
"soundcloud": "559200912",
"thumb": "https://i.imgur.com/hOBQEzc.jpg",
"url": "https://soundcloud.com/nlp-highlights/78-where-do-corpora-come-from-with-matt-honnibal-and-ines-montani",
"author": "Matt Gardner, Waleed Ammar (Allen AI)",
"author_links": {
"website": "https://soundcloud.com/nlp-highlights"
},
"category": ["podcasts"]
},
{
"type": "education",
"id": "podcast-init",
"title": "Podcast.__init__ #87: spaCy with Matthew Honnibal",
"slogan": "December 2017",
"description": "As the amount of text available on the internet and in businesses continues to increase, the need for fast and accurate language analysis becomes more prominent. This week Matthew Honnibal, the creator of spaCy, talks about his experiences researching natural language processing and creating a library to make his findings accessible to industry.",
"iframe": "https://www.pythonpodcast.com/wp-content/plugins/podlove-podcasting-plugin-for-wordpress/lib/modules/podlove_web_player/player_v4/dist/share.html?episode=https://www.pythonpodcast.com/?podlove_player4=176",
"iframe_height": 200,
"thumb": "https://i.imgur.com/rpo6BuY.png",
"url": "https://www.podcastinit.com/episode-87-spacy-with-matthew-honnibal/",
"author": "Tobias Macey",
"author_links": {
"website": "https://www.podcastinit.com"
},
"category": ["podcasts"]
},
{
"type": "education",
"id": "podcast-init2",
"title": "Podcast.__init__ #256: An Open Source Toolchain For NLP From Explosion AI",
"slogan": "March 2020",
"description": "The state of the art in natural language processing is a constantly moving target. With the rise of deep learning, previously cutting edge techniques have given way to robust language models. Through it all the team at Explosion AI have built a strong presence with the trifecta of spaCy, Thinc, and Prodigy to support fast and flexible data labeling to feed deep learning models and performant and scalable text processing. In this episode founder and open source author Matthew Honnibal shares his experience growing a business around cutting edge open source libraries for the machine learning developent process.",
"iframe": "https://cdn.podlove.org/web-player/share.html?episode=https%3A%2F%2Fwww.pythonpodcast.com%2F%3Fpodlove_player4%3D614",
"iframe_height": 200,
"thumb": "https://i.imgur.com/rpo6BuY.png",
"url": "https://www.pythonpodcast.com/explosion-ai-natural-language-processing-episode-256/",
"author": "Tobias Macey",
"author_links": {
"website": "https://www.podcastinit.com"
},
"category": ["podcasts"]
},
{
"type": "education",
"id": "talk-python-podcast",
"title": "Talk Python #202: Building a software business",
"slogan": "March 2019",
"description": "One core question around open source is how do you fund it? Well, there is always that PayPal donate button. But that's been a tremendous failure for many projects. Often the go-to answer is consulting. But what if you don't want to trade time for money? You could take things up a notch and change the equation, exchanging value for money. That's what Ines Montani and her co-founder did when they started Explosion AI with spaCy as the foundation.",
"thumb": "https://i.imgur.com/q1twuK8.png",
"url": "https://talkpython.fm/episodes/show/202/building-a-software-business",
"soundcloud": "588364857",
"author": "Michael Kennedy",
"author_links": {
"website": "https://talkpython.fm/"
},
"category": ["podcasts"]
},
{
"type": "education",
"id": "twimlai-podcast",
"title": "TWiML & AI: Practical NLP with spaCy and Prodigy",
"slogan": "May 2019",
"description": "\"Ines and I caught up to discuss her various projects, including the aforementioned spaCy, an open-source NLP library built with a focus on industry and production use cases. In our conversation, Ines gives us an overview of the spaCy Library, a look at some of the use cases that excite her, and the Spacy community and contributors. We also discuss her work with Prodigy, an annotation service tool that uses continuous active learning to train models, and finally, what other exciting projects she is working on.\"",
"thumb": "https://i.imgur.com/ng2F5gK.png",
"url": "https://twimlai.com/twiml-talk-262-practical-natural-language-processing-with-spacy-and-prodigy-w-ines-montani",
"iframe": "https://html5-player.libsyn.com/embed/episode/id/9691514/height/90/theme/custom/thumbnail/no/preload/no/direction/backward/render-playlist/no/custom-color/3e85b1/",
"iframe_height": 90,
"author": "Sam Charrington",
"author_links": {
"website": "https://twimlai.com"
},
"category": ["podcasts"]
},
{
"type": "education",
"id": "analytics-vidhya",
"title": "DataHack Radio #23: The Brains behind spaCy",
"slogan": "June 2019",
"description": "\"What would you do if you had the chance to pick the brains behind one of the most popular Natural Language Processing (NLP) libraries of our era? A library that has helped usher in the current boom in NLP applications and nurtured tons of NLP scientists? Well – you invite the creators on our popular DataHack Radio podcast and let them do the talking! We are delighted to welcome Ines Montani and Matt Honnibal, the developers of spaCy – a powerful and advanced library for NLP.\"",
"thumb": "https://i.imgur.com/3zJKZ1P.jpg",
"url": "https://www.analyticsvidhya.com/blog/2019/06/datahack-radio-ines-montani-matthew-honnibal-brains-behind-spacy/",
"soundcloud": "630741825",
"author": "Analytics Vidhya",
"author_links": {
"website": "https://www.analyticsvidhya.com",
"twitter": "analyticsvidhya"
},
"category": ["podcasts"]
},
{
"type": "education",
"id": "practical-ai-podcast",
"title": "Practical AI: Modern NLP with spaCy",
"slogan": "December 2019",
"description": "\"spaCy is awesome for NLP! It’s easy to use, has widespread adoption, is open source, and integrates the latest language models. Ines Montani and Matthew Honnibal (core developers of spaCy and co-founders of Explosion) join us to discuss the history of the project, its capabilities, and the latest trends in NLP. We also dig into the practicalities of taking NLP workflows to production. You don’t want to miss this episode!\"",
"thumb": "https://i.imgur.com/jn8Bcdw.png",
"url": "https://changelog.com/practicalai/68",
"author": "Daniel Whitenack & Chris Benson",
"author_links": {
"website": "https://changelog.com/practicalai",
"twitter": "PracticalAIFM"
},
"category": ["podcasts"]
},
{
"type": "education",
"id": "video-entity-linking",
"title": "Training a custom entity linking mode with spaCy",
"author": "Sofie Van Landeghem",
"author_links": {
"twitter": "OxyKodit",
"github": "svlandeg"
},
"youtube": "8u57WSXVpmw",
"category": ["videos"]
},
{
"id": "self-attentive-parser",
"title": "Berkeley Neural Parser",
"slogan": "Constituency Parsing with a Self-Attentive Encoder (ACL 2018)",
"description": "A Python implementation of the parsers described in *\"Constituency Parsing with a Self-Attentive Encoder\"* from ACL 2018.",
"url": "https://arxiv.org/abs/1805.01052",
"github": "nikitakit/self-attentive-parser",
"pip": "benepar",
"code_example": [
"import benepar, spacy",
"nlp = spacy.load('en_core_web_md')",
"nlp.add_pipe('benepar', config={'model': 'benepar_en3'})",
"doc = nlp('The time for action is now. It is never too late to do something.')",
"sent = list(doc.sents)[0]",
"print(sent._.parse_string)",
"# (S (NP (NP (DT The) (NN time)) (PP (IN for) (NP (NN action)))) (VP (VBZ is) (ADVP (RB now))) (. .))",
"print(sent._.labels)",
"# ('S',)",
"print(list(sent._.children)[0])",
"# The time for action"
],
"author": "Nikita Kitaev",
"author_links": {
"github": "nikitakit",
"website": " http://kitaev.io"
},
"category": ["research", "pipeline"]
},
{
"id": "spacy-graphql",
"title": "spacy-graphql",
"slogan": "Query spaCy's linguistic annotations using GraphQL",
"github": "ines/spacy-graphql",
"description": "A very simple and experimental app that lets you query spaCy's linguistic annotations using [GraphQL](https://graphql.org/). The API currently supports most token attributes, named entities, sentences and text categories (if available as `doc.cats`, i.e. if you added a text classifier to a model). The `meta` field will return the model meta data. Models are only loaded once and kept in memory.",
"url": "https://explosion.ai/demos/spacy-graphql",
"category": ["apis"],
"tags": ["graphql"],
"thumb": "https://i.imgur.com/xC7zpTO.png",
"code_example": [
"{",
" nlp(text: \"Zuckerberg is the CEO of Facebook.\", model: \"en_core_web_sm\") {",
" meta {",
" lang",
" description",
" }",
" doc {",
" text",
" tokens {",
" text",
" pos_",
" }",
" ents {",
" text",
" label_",
" }",
" }",
" }",
"}"
],
"code_language": "json",
"author": "Ines Montani",
"author_links": {
"twitter": "_inesmontani",
"github": "ines",
"website": "https://ines.io"
}
},
{
"id": "spacy-js",
"title": "spacy-js",
"slogan": "JavaScript API for spaCy with Python REST API",
"github": "ines/spacy-js",
"description": "JavaScript interface for accessing linguistic annotations provided by spaCy. This project is mostly experimental and was developed for fun to play around with different ways of mimicking spaCy's Python API.\n\nThe results will still be computed in Python and made available via a REST API. The JavaScript API resembles spaCy's Python API as closely as possible (with a few exceptions, as the values are all pre-computed and it's tricky to express complex recursive relationships).",
"code_language": "javascript",
"code_example": [
"const spacy = require('spacy');",
"",
"(async function() {",
" const nlp = spacy.load('en_core_web_sm');",
" const doc = await nlp('This is a text about Facebook.');",
" for (let ent of doc.ents) {",
" console.log(ent.text, ent.label);",
" }",
" for (let token of doc) {",
" console.log(token.text, token.pos, token.head.text);",
" }",
"})();"
],
"author": "Ines Montani",
"author_links": {
"twitter": "_inesmontani",
"github": "ines",
"website": "https://ines.io"
},
"category": ["nonpython"],
"tags": ["javascript"]
},
{
"id": "spacy-wordnet",
"title": "spacy-wordnet",
"slogan": "WordNet meets spaCy",
"description": "`spacy-wordnet` creates annotations that easily allow the use of WordNet and [WordNet Domains](http://wndomains.fbk.eu/) by using the [NLTK WordNet interface](http://www.nltk.org/howto/wordnet.html)",
"github": "recognai/spacy-wordnet",
"tags": ["wordnet", "synsets"],
"thumb": "https://i.imgur.com/ud4C7cj.png",
"code_example": [
"import spacy",
"from spacy_wordnet.wordnet_annotator import WordnetAnnotator ",
"",
"# Load an spacy model (supported models are \"es\" and \"en\") ",
"nlp = spacy.load('en')",
"# Spacy 3.x",
"nlp.add_pipe(\"spacy_wordnet\", after='tagger', config={'lang': nlp.lang})",
"# Spacy 2.x",
"# nlp.add_pipe(WordnetAnnotator(nlp.lang), after='tagger')",
"token = nlp('prices')[0]",
"",
"# wordnet object link spacy token with nltk wordnet interface by giving acces to",
"# synsets and lemmas ",
"token._.wordnet.synsets()",
"token._.wordnet.lemmas()",
"",
"# And automatically tags with wordnet domains",
"token._.wordnet.wordnet_domains()"
],
"author": "recognai",
"author_links": {
"github": "recognai",
"twitter": "recogn_ai",
"website": "https://recogn.ai"
},
"category": ["pipeline"]
},
{
"id": "spacy-conll",
"title": "spacy_conll",
"slogan": "Parsing from and to CoNLL-U format with `spacy`, `spacy-stanza` and `spacy-udpipe`",
"description": "This module allows you to parse text into CoNLL-U format or read ConLL-U into a spaCy `Doc`. You can use it as a command line tool, or embed it in your own scripts by adding it as a custom pipeline component to a `spacy`, `spacy-stanza` or `spacy-udpipe` pipeline. It also provides an easy-to-use function to quickly initialize any spaCy-wrapped parser. CoNLL-related properties are added to `Doc` elements, `Span` sentences, and `Token` objects.",
"code_example": [
"from spacy_conll import init_parser",
"",
"",
"# Initialise English parser, already including the ConllFormatter as a pipeline component.",
"# Indicate that we want to get the CoNLL headers in the string output.",
"# `use_gpu` and `verbose` are specific to stanza. These keywords arguments are passed onto their Pipeline() initialisation",
"nlp = init_parser(\"en\",",
" \"stanza\",",
" parser_opts={\"use_gpu\": True, \"verbose\": False},",
" include_headers=True)",
"# Parse a given string",
"doc = nlp(\"A cookie is a baked or cooked food that is typically small, flat and sweet. It usually contains flour, sugar and some type of oil or fat.\")",
"",
"# Get the CoNLL representation of the whole document, including headers",
"conll = doc._.conll_str",
"print(conll)"
],
"code_language": "python",
"author": "Bram Vanroy",
"author_links": {
"github": "BramVanroy",
"twitter": "BramVanroy",
"website": "http://bramvanroy.be"
},
"github": "BramVanroy/spacy_conll",
"category": ["standalone", "pipeline"],
"tags": ["linguistics", "computational linguistics", "conll", "conll-u"]
},
{
"id": "ludwig",
"title": "Ludwig",
"slogan": "A code-free deep learning toolbox",
"description": "Ludwig makes it easy to build deep learning models for many applications, including NLP ones. It uses spaCy for tokenizing text in different languages.",
"pip": "ludwig",
"github": "uber/ludwig",
"thumb": "https://i.imgur.com/j1sORgD.png",
"url": "http://ludwig.ai",
"author": "Piero Molino @ Uber AI",
"author_links": {
"github": "w4nderlust",
"twitter": "w4nderlus7",
"website": "http://w4nderlu.st"
},
"category": ["standalone", "research"]
},
{
"id": "pic2phrase_bot",
"title": "pic2phrase_bot: Photo Description Generator",
"slogan": "A bot that generates descriptions to submitted photos, in a human-like manner.",
"description": "pic2phrase_bot runs inside Telegram messenger and can be used to generate a phrase describing a submitted photo, employing computer vision, web scraping, and syntactic dependency analysis powered by spaCy.",
"thumb": "https://i.imgur.com/ggVI02O.jpg",
"image": "https://i.imgur.com/z1yhWQR.jpg",
"url": "https://telegram.me/pic2phrase_bot",
"author": "Yuli Vasiliev",
"author_links": {
"twitter": "VasilievYuli"
},
"category": ["standalone", "conversational"]
},
{
"id": "pyInflect",
"slogan": "A Python module for word inflections",
"description": "This package uses the [spaCy 2.0 extensions](https://spacy.io/usage/processing-pipelines#extensions) to add word inflections to the system.",
"github": "bjascob/pyInflect",
"pip": "pyinflect",
"code_example": [
"import spacy",
"import pyinflect",
"",
"nlp = spacy.load('en_core_web_sm')",
"doc = nlp('This is an example.')",
"doc[3].tag_ # NN",
"doc[3]._.inflect('NNS') # examples"
],
"author": "Brad Jascob",
"author_links": {
"github": "bjascob"
},
"category": ["pipeline"],
"tags": ["inflection"]
},
{
"id": "lemminflect",
"slogan": "A Python module for English lemmatization and inflection",
"description": "LemmInflect uses a dictionary approach to lemmatize English words and inflect them into forms specified by a user supplied [Universal Dependencies](https://universaldependencies.org/u/pos/) or [Penn Treebank](https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html) tag. The library works with out-of-vocabulary (OOV) words by applying neural network techniques to classify word forms and choose the appropriate morphing rules. The system acts as a standalone module or as an extension to spaCy.",
"github": "bjascob/LemmInflect",
"pip": "lemminflect",
"thumb": "https://raw.githubusercontent.com/bjascob/LemmInflect/master/docs/img/icons8-citrus-80.png",
"code_example": [
"import spacy",
"import lemminflect",
"",
"nlp = spacy.load('en_core_web_sm')",
"doc = nlp('I am testing this example.')",
"doc[2]._.lemma() # 'test'",
"doc[4]._.inflect('NNS') # 'examples'"
],
"author": "Brad Jascob",
"author_links": {
"github": "bjascob"
},
"category": ["pipeline"],
"tags": ["inflection", "lemmatizer"]
},
{
"id": "amrlib",
"slogan": "A python library that makes AMR parsing, generation and visualization simple.",
"description": "amrlib is a python module and spaCy add-in for Abstract Meaning Representation (AMR). The system can parse sentences to AMR graphs or generate text from existing graphs. It includes a GUI for visualization and experimentation.",
"github": "bjascob/amrlib",
"pip": "amrlib",
"code_example": [
"import spacy",
"import amrlib",
"amrlib.setup_spacy_extension()",
"nlp = spacy.load('en_core_web_sm')",
"doc = nlp('This is a test of the spaCy extension. The test has multiple sentences.')",
"graphs = doc._.to_amr()",
"for graph in graphs:",
" print(graph)"
],
"author": "Brad Jascob",
"author_links": {
"github": "bjascob"
},
"category": ["pipeline"]
},
{
"id": "classyclassification",
"title": "Classy Classification",
"slogan": "Have you ever struggled with needing a spaCy TextCategorizer but didn't have the time to train one from scratch? Classy Classification is the way to go!",
"description": "Have you ever struggled with needing a [spaCy TextCategorizer](https://spacy.io/api/textcategorizer) but didn't have the time to train one from scratch? Classy Classification is the way to go! For few-shot classification using [sentence-transformers](https://github.com/UKPLab/sentence-transformers) or [spaCy models](https://spacy.io/usage/models), provide a dictionary with labels and examples, or just provide a list of labels for zero shot-classification with [Huggingface zero-shot classifiers](https://huggingface.co/models?pipeline_tag=zero-shot-classification).",
"github": "davidberenstein1957/classy-classification",
"pip": "classy-classification",
"thumb": "https://raw.githubusercontent.com/Pandora-Intelligence/classy-classification/master/logo.png",
"code_example": [
"import spacy",
"import classy_classification",
"",
"data = {",
" \"furniture\": [\"This text is about chairs.\",",
" \"Couches, benches and televisions.\",",
" \"I really need to get a new sofa.\"],",
" \"kitchen\": [\"There also exist things like fridges.\",",
" \"I hope to be getting a new stove today.\",",
" \"Do you also have some ovens.\"]",
"}",
"",
"# see github repo for examples on sentence-transformers and Huggingface",
"nlp = spacy.load('en_core_web_md')",
"nlp.add_pipe(\"text_categorizer\", ",
" config={",
" \"data\": data,",
" \"model\": \"spacy\"",
" }",
")",
"",
"print(nlp(\"I am looking for kitchen appliances.\")._.cats)",
"# Output:",
"#",
"# [{\"label\": \"furniture\", \"score\": 0.21}, {\"label\": \"kitchen\", \"score\": 0.79}]"
],
"author": "David Berenstein",
"author_links": {
"github": "davidberenstein1957",
"website": "https://www.linkedin.com/in/david-berenstein-1bab11105/"
},
"category": [
"pipeline",
"standalone"
],
"tags": [
"classification",
"zero-shot",
"few-shot",
"sentence-transformers",
"huggingface"
],
"spacy_version": 3
},
{
"id": "conciseconcepts",
"title": "Concise Concepts",
"slogan": "Concise Concepts uses few-shot NER based on word embedding similarity to get you going with easy!",
"description": "When wanting to apply NER to concise concepts, it is really easy to come up with examples, but it takes some effort to train an entire pipeline. Concise Concepts uses few-shot NER based on word embedding similarity to get you going with easy!",
"github": "pandora-intelligence/concise-concepts",
"pip": "concise-concepts",
"thumb": "https://raw.githubusercontent.com/Pandora-Intelligence/concise-concepts/master/img/logo.png",
"image": "https://raw.githubusercontent.com/Pandora-Intelligence/concise-concepts/master/img/example.png",
"code_example": [
"import spacy",
"from spacy import displacy",
"import concise_concepts",
"",
"data = {",
" \"fruit\": [\"apple\", \"pear\", \"orange\"],",
" \"vegetable\": [\"broccoli\", \"spinach\", \"tomato\"],",
" \"meat\": [\"beef\", \"pork\", \"fish\", \"lamb\"]",
"}",
"",
"text = \"\"\"",
" Heat the oil in a large pan and add the Onion, celery and carrots.",
" Then, cook over a medium–low heat for 10 minutes, or until softened.",
" Add the courgette, garlic, red peppers and oregano and cook for 2–3 minutes.",
" Later, add some oranges and chickens.\"\"\"",
"",
"# use any model that has internal spacy embeddings",
"nlp = spacy.load('en_core_web_lg')",
"nlp.add_pipe(\"concise_concepts\", ",
" config={\"data\": data}",
")",
"doc = nlp(text)",
"",
"options = {\"colors\": {\"fruit\": \"darkorange\", \"vegetable\": \"limegreen\", \"meat\": \"salmon\"},",
" \"ents\": [\"fruit\", \"vegetable\", \"meat\"]}",
"",
"displacy.render(doc, style=\"ent\", options=options)"
],
"author": "David Berenstein",
"author_links": {
"github": "davidberenstein1957",
"website": "https://www.linkedin.com/in/david-berenstein-1bab11105/"
},
"category": [
"pipeline"
],
"tags": [
"ner",
"few-shot",
"gensim"
],
"spacy_version": 3
},
{
"id": "crosslingualcoreference",
"title": "Crosslingual Coreference",
"slogan": "One multi-lingual coreference model to rule them all!",
"description": "Coreference is amazing but the data required for training a model is very scarce. In our case, the available training for non-English languages also data proved to be poorly annotated. Crosslingual Coreference therefore uses the assumption a trained model with English data and cross-lingual embeddings should work for other languages with similar sentence structure. Verified to work quite well for at least (EN, NL, DK, FR, DE).",
"github": "pandora-intelligence/crosslingual-coreference",
"pip": "crosslingual-coreference",
"thumb": "https://raw.githubusercontent.com/Pandora-Intelligence/crosslingual-coreference/master/img/logo.png",
"image": "https://raw.githubusercontent.com/Pandora-Intelligence/crosslingual-coreference/master/img/example_total.png",
"code_example": [
"import spacy",
"import crosslingual_coreference",
"",
"text = \"\"\"",
" Do not forget about Momofuku Ando!",
" He created instant noodles in Osaka.",
" At that location, Nissin was founded.",
" Many students survived by eating these noodles, but they don't even know him.\"\"\"",
"",
"# use any model that has internal spacy embeddings",
"nlp = spacy.load('en_core_web_sm')",
"nlp.add_pipe(",
" \"xx_coref\", config={\"chunk_size\": 2500, \"chunk_overlap\": 2, \"device\": 0})",
")",
"",
"doc = nlp(text)",
"",
"print(doc._.coref_clusters)",
"# Output",
"#",
"# [[[4, 5], [7, 7], [27, 27], [36, 36]],",
"# [[12, 12], [15, 16]],",
"# [[9, 10], [27, 28]],",
"# [[22, 23], [31, 31]]]",
"print(doc._.resolved_text)",
"# Output",
"#",
"# Do not forget about Momofuku Ando!",
"# Momofuku Ando created instant noodles in Osaka.",
"# At Osaka, Nissin was founded.",
"# Many students survived by eating instant noodles,",
"# but Many students don't even know Momofuku Ando."
],
"author": "David Berenstein",
"author_links": {
"github": "davidberenstein1957",
"website": "https://www.linkedin.com/in/david-berenstein-1bab11105/"
},
"category": [
"pipeline",
"standalone"
],
"tags": [
"coreference",
"multi-lingual",
"cross-lingual",
"allennlp"
],
"spacy_version": 3
},
{
"id": "blackstone",
"title": "Blackstone",
"slogan": "A spaCy pipeline and model for NLP on unstructured legal text",
"description": "Blackstone is a spaCy model and library for processing long-form, unstructured legal text. Blackstone is an experimental research project from the [Incorporated Council of Law Reporting for England and Wales'](https://iclr.co.uk/) research lab, [ICLR&D](https://research.iclr.co.uk/).",
"github": "ICLRandD/Blackstone",
"pip": "blackstone",
"thumb": "https://iclr.s3-eu-west-1.amazonaws.com/assets/iclrand/Blackstone/thumb.png",
"url": "https://research.iclr.co.uk",
"author": " ICLR&D",
"author_links": {
"github": "ICLRandD",
"twitter": "ICLRanD",
"website": "https://research.iclr.co.uk"
},
"category": ["scientific", "models", "research"]
},
{
"id": "NGym",
"title": "NeuralGym",
"slogan": "A little Windows GUI for training models with spaCy",
"description": "NeuralGym is a Python application for Windows with a graphical user interface to train models with spaCy. Run the application, select an output folder, a training data file in spaCy's data format, a spaCy model or blank model and press 'Start'.",
"github": "d5555/NeuralGym",
"url": "https://github.com/d5555/NeuralGym",
"image": "https://github.com/d5555/NeuralGym/raw/master/NGym.png",
"thumb": "https://github.com/d5555/NeuralGym/raw/master/NGym/web.png",
"author": "d5555",
"category": ["training"],
"tags": ["windows"]
},
{
"id": "holmes",
"title": "Holmes",
"slogan": "Information extraction from English and German texts based on predicate logic",
"github": "explosion/holmes-extractor",
"url": "https://github.com/explosion/holmes-extractor",
"description": "Holmes is a Python 3 library that supports a number of use cases involving information extraction from English and German texts, including chatbot, structural extraction, topic matching and supervised document classification. There is a [website demonstrating intelligent search based on topic matching](https://holmes-demo.explosion.services).",
"pip": "holmes-extractor",
"category": ["pipeline", "standalone"],
"tags": ["chatbots", "text-processing"],
"thumb": "https://raw.githubusercontent.com/explosion/holmes-extractor/master/docs/holmes_thumbnail.png",
"code_example": [
"import holmes_extractor as holmes",
"holmes_manager = holmes.Manager(model='en_core_web_lg')",
"holmes_manager.register_search_phrase('A big dog chases a cat')",
"holmes_manager.start_chatbot_mode_console()"
],
"author": "Richard Paul Hudson",
"author_links": {
"github": "richardpaulhudson"
}
},
{
"id": "coreferee",
"title": "Coreferee",
"slogan": "Coreference resolution for multiple languages",
"github": "explosion/coreferee",
"url": "https://github.com/explosion/coreferee",
"description": "Coreferee is a pipeline plugin that performs coreference resolution for English, French, German and Polish. It is designed so that it is easy to add support for new languages and optimised for limited training data. It uses a mixture of neural networks and programmed rules. Please note you will need to [install models](https://github.com/explosion/coreferee#getting-started) before running the code example.",
"pip": "coreferee",
"category": ["pipeline", "models", "standalone"],
"tags": ["coreference-resolution", "anaphora"],
"code_example": [
"import coreferee, spacy",
"nlp = spacy.load('en_core_web_trf')",
"nlp.add_pipe('coreferee')",
"doc = nlp('Although he was very busy with his work, Peter had had enough of it. He and his wife decided they needed a holiday. They travelled to Spain because they loved the country very much.')",
"doc._.coref_chains.print()",
"# Output:",
"#",
"# 0: he(1), his(6), Peter(9), He(16), his(18)",
"# 1: work(7), it(14)",
"# 2: [He(16); wife(19)], they(21), They(26), they(31)",
"# 3: Spain(29), country(34)",
"#",
"print(doc._.coref_chains.resolve(doc[31]))",
"# Output:",
"#",
"# [Peter, wife]"
],
"author": "Richard Paul Hudson",
"author_links": {
"github": "richardpaulhudson"
}
},
{
"id": "spacy-transformers",
"title": "spacy-transformers",
"slogan": "spaCy pipelines for pretrained BERT, XLNet and GPT-2",
"description": "This package provides spaCy model pipelines that wrap [Hugging Face's `transformers`](https://github.com/huggingface/transformers) package, so you can use them in spaCy. The result is convenient access to state-of-the-art transformer architectures, such as BERT, GPT-2, XLNet, etc.",
"github": "explosion/spacy-transformers",
"url": "https://explosion.ai/blog/spacy-transformers",
"pip": "spacy-transformers",
"category": ["pipeline", "models", "research"],
"code_example": [
"import spacy",
"",
"nlp = spacy.load(\"en_core_web_trf\")",
"doc = nlp(\"Apple shares rose on the news. Apple pie is delicious.\")"
],
"author": "Explosion",
"author_links": {
"twitter": "explosion_ai",
"github": "explosion",
"website": "https://explosion.ai"
}
},
{
"id": "spacy-huggingface-hub",
"title": "spacy-huggingface-hub",
"slogan": "Push your spaCy pipelines to the Hugging Face Hub",
"description": "This package provides a CLI command for uploading any trained spaCy pipeline packaged with [`spacy package`](https://spacy.io/api/cli#package) to the [Hugging Face Hub](https://huggingface.co). It auto-generates all meta information for you, uploads a pretty README (requires spaCy v3.1+) and handles version control under the hood.",
"github": "explosion/spacy-huggingface-hub",
"thumb": "https://i.imgur.com/j6FO9O6.jpg",
"url": "https://github.com/explosion/spacy-huggingface-hub",
"pip": "spacy-huggingface-hub",
"category": ["pipeline", "models"],
"author": "Explosion",
"author_links": {
"twitter": "explosion_ai",
"github": "explosion",
"website": "https://explosion.ai"
}
},
{
"id": "spacy-clausie",
"title": "spacy-clausie",
"slogan": "Implementation of the ClausIE information extraction system for Python+spaCy",
"github": "mmxgn/spacy-clausie",
"url": "https://github.com/mmxgn/spacy-clausie",
"description": "ClausIE, a novel, clause-based approach to open information extraction, which extracts relations and their arguments from natural language text",
"category": ["pipeline", "scientific", "research"],
"code_example": [
"import spacy",
"import claucy",
"",
"nlp = spacy.load(\"en\")",
"claucy.add_to_pipe(nlp)",
"",
"doc = nlp(\"AE died in Princeton in 1955.\")",
"",
"print(doc._.clauses)",
"# Output:",
"# <SV, AE, died, None, None, None, [in Princeton, in 1955]>",
"",
"propositions = doc._.clauses[0].to_propositions(as_text=True)",
"",
"print(propositions)",
"# Output:",
"# [AE died in Princeton in 1955, AE died in 1955, AE died in Princeton"
],
"author": "Emmanouil Theofanis Chourdakis",
"author_links": {
"github": "mmxgn"
}
},
{
"id": "ipymarkup",
"slogan": "NER, syntax markup visualizations",
"description": "Collection of NLP visualizations for NER and syntax tree markup. Similar to [displaCy](https://explosion.ai/demos/displacy) and [displaCy ENT](https://explosion.ai/demos/displacy-ent).",
"github": "natasha/ipymarkup",
"image": "https://github.com/natasha/ipymarkup/blob/master/table.png?raw=true",
"pip":"pip install ipymarkup",
"code_example": [
"from ipymarkup import show_span_ascii_markup, show_dep_ascii_markup",
"",
"text = 'В мероприятии примут участие не только российские учёные, но и зарубежные исследователи, в том числе, Крис Хелмбрехт - управляющий директор и совладелец креативного агентства Kollektiv (Германия, США), Ннека Угбома - руководитель проекта Mushroom works (Великобритания), Гергей Ковач - политик и лидер субкультурной партии «Dog with two tails» (Венгрия), Георг Жено - немецкий режиссёр, один из создателей экспериментального театра «Театр.doc», Театра им. Йозефа Бойса (Германия).'",
"spans = [(102, 116, 'PER'), (186, 194, 'LOC'), (196, 199, 'LOC'), (202, 214, 'PER'), (254, 268, 'LOC'), (271, 283, 'PER'), (324, 342, 'ORG'), (345, 352, 'LOC'), (355, 365, 'PER'), (445, 455, 'ORG'), (456, 468, 'PER'), (470, 478, 'LOC')]",
"show_span_ascii_markup(text, spans)"
],
"author": "Alexander Kukushkin",
"author_links": {
"github": "kuk"
},
"category": ["visualizers"]
},
{
"id": "negspacy",
"title": "negspaCy",
"slogan": "spaCy pipeline object for negating concepts in text based on the NegEx algorithm.",
"github": "jenojp/negspacy",
"url": "https://github.com/jenojp/negspacy",
"description": "negspacy is a spaCy pipeline component that evaluates whether Named Entities are negated in text. It adds an extension to 'Span' objects.",
"pip": "negspacy",
"category": ["pipeline", "scientific"],
"tags": ["negation", "text-processing"],
"thumb": "https://github.com/jenojp/negspacy/blob/master/docs/thumb.png?raw=true",
"image": "https://github.com/jenojp/negspacy/blob/master/docs/icon.png?raw=true",
"code_example": [
"import spacy",
"from negspacy.negation import Negex",
"",
"nlp = spacy.load(\"en_core_web_sm\")",
"nlp.add_pipe(\"negex\", config={\"ent_types\":[\"PERSON\",\"ORG\"]})",
"",
"doc = nlp(\"She does not like Steve Jobs but likes Apple products.\")",
"for e in doc.ents:",
" print(e.text, e._.negex)"
],
"author": "Jeno Pizarro",
"author_links": {
"github": "jenojp",
"twitter": "jenojp"
}
},
{
"id": "ronec",
"title": "RONEC - Romanian Named Entity Corpus",
"slogan": "Named Entity Recognition corpus for Romanian language.",
"github": "dumitrescustefan/ronec",
"url": "https://github.com/dumitrescustefan/ronec",
"description": "The corpus holds 5127 sentences, annotated with 16 classes, with a total of 26376 annotated entities. The corpus comes into two formats: BRAT and CONLLUP.",
"category": ["standalone", "models"],
"tags": ["ner", "romanian"],
"thumb": "https://raw.githubusercontent.com/dumitrescustefan/ronec/master/res/thumb.png",
"code_example": [
"# to train a new model on ronec",
"python3 convert_spacy.py ronec/conllup/ronec.conllup output",
"python3 -m spacy train ro models output/train_ronec.json output/train_ronec.json -p ent",
"",
"# download the Romanian NER model",
"python -m spacy download ro_ner",
"",
"# load the model and print entities for a simple sentence",
"import spacy",
"",
"nlp = spacy.load(\"ro_ner\")",
"doc = nlp(\"Popescu Ion a fost la Cluj\")",
"",
"for ent in doc.ents:",
"\tprint(ent.text, ent.start_char, ent.end_char, ent.label_)"
],
"author": "Stefan Daniel Dumitrescu, Andrei-Marius Avram"
},
{
"id": "Healthsea",
"title": "Healthsea",
"slogan": "Healthsea: an end-to-end spaCy pipeline for exploring health supplement effects",
"description": "This spaCy project trains an NER model and a custom Text Classification model with Clause Segmentation and Blinding capabilities to analyze supplement reviews and their potential effects on health.",
"github": "explosion/healthsea",
"thumb": "https://github.com/explosion/healthsea/blob/main/img/Jellyfish.png",
"category": ["pipeline", "research"],
"code_example": [
"import spacy",
"",
"nlp = spacy.load(\"en_healthsea\")",
"doc = nlp(\"This is great for joint pain.\")",
"",
"# Clause Segmentation & Blinding",
"print(doc._.clauses)",
"",
"> {",
"> \"split_indices\": [0, 7],",
"> \"has_ent\": true,",
"> \"ent_indices\": [4, 6],",
"> \"blinder\": \"_CONDITION_\",",
"> \"ent_name\": \"joint pain\",",
"> \"cats\": {",
"> \"POSITIVE\": 0.9824668169021606,",
"> \"NEUTRAL\": 0.017364952713251114,",
"> \"NEGATIVE\": 0.00002889777533710003,",
"> \"ANAMNESIS\": 0.0001394189748680219",
"> \"prediction_text\": [\"This\", \"is\", \"great\", \"for\", \"_CONDITION_\", \"!\"]",
"> }",
"",
"# Aggregated results",
"> {",
"> \"joint_pain\": {",
"> \"effects\": [\"POSITIVE\"],",
"> \"effect\": \"POSITIVE\",",
"> \"label\": \"CONDITION\",",
"> \"text\": \"joint pain\"",
"> }",
"> }"
],
"author": "Edward Schmuhl",
"author_links": {
"github": "thomashacker",
"twitter": "aestheticedwar1",
"website": "https://explosion.ai/"
}
},
{
"id": "presidio",
"title": "Presidio",
"slogan": "Context aware, pluggable and customizable data protection and PII data anonymization",
"description": "Presidio *(Origin from Latin praesidium ‘protection, garrison’)* helps to ensure sensitive text is properly managed and governed. It provides fast ***analytics*** and ***anonymization*** for sensitive text such as credit card numbers, names, locations, social security numbers, bitcoin wallets, US phone numbers and financial data. Presidio analyzes the text using predefined or custom recognizers to identify entities, patterns, formats, and checksums with relevant context.",
"url": "https://aka.ms/presidio",
"image": "https://raw.githubusercontent.com/microsoft/presidio/master/docs/assets/before-after.png",
"github": "microsoft/presidio",
"category": ["standalone"],
"thumb": "https://avatars0.githubusercontent.com/u/6154722",
"author": "Microsoft",
"author_links": {
"github": "microsoft"
}
},
{
"id": "presidio-research",
"title": "Presidio Research",
"slogan": "Toolbox for developing and evaluating PII detectors, NER models for PII and generating fake PII data",
"description": "This package features data-science related tasks for developing new recognizers for Microsoft Presidio. It is used for the evaluation of the entire system, as well as for evaluating specific PII recognizers or PII detection models. Anyone interested in evaluating an existing Microsoft Presidio instance, a specific PII recognizer or to develop new models or logic for detecting PII could leverage the preexisting work in this package. Additionally, anyone interested in generating new data based on previous datasets (e.g. to increase the coverage of entity values) for Named Entity Recognition models could leverage the data generator contained in this package.",
"url": "https://aka.ms/presidio-research",
"github": "microsoft/presidio-research",
"category": ["standalone"],
"thumb": "https://avatars0.githubusercontent.com/u/6154722",
"author": "Microsoft",
"author_links": {
"github": "microsoft"
}
},
{
"id": "python-sentence-boundary-disambiguation",
"title": "pySBD - python Sentence Boundary Disambiguation",
"slogan": "Rule-based sentence boundary detection that works out-of-the-box",
"github": "nipunsadvilkar/pySBD",
"description": "pySBD is 'real-world' sentence segmenter which extracts reasonable sentences when the format and domain of the input text are unknown. It is a rules-based algorithm based on [The Golden Rules](https://s3.amazonaws.com/tm-town-nlp-resources/golden_rules.txt) - a set of tests to check accuracy of segmenter in regards to edge case scenarios developed by [TM-Town](https://www.tm-town.com/) dev team. pySBD is python port of ruby gem [Pragmatic Segmenter](https://github.com/diasks2/pragmatic_segmenter).",
"pip": "pysbd",
"category": ["scientific"],
"tags": ["sentence segmentation"],
"code_example": [
"from pysbd.utils import PySBDFactory",
"",
"nlp = spacy.blank('en')",
"# Caution: works with spaCy<=2.x.x",
"nlp.add_pipe(PySBDFactory(nlp))",
"",
"doc = nlp('My name is Jonas E. Smith. Please turn to p. 55.')",
"print(list(doc.sents))",
"# [My name is Jonas E. Smith., Please turn to p. 55.]"
],
"author": "Nipun Sadvilkar",
"author_links": {
"twitter": "nipunsadvilkar",
"github": "nipunsadvilkar",
"website": "https://nipunsadvilkar.github.io"
}
},
{
"id": "cookiecutter-spacy-fastapi",
"title": "cookiecutter-spacy-fastapi",
"slogan": "Docker-based cookiecutter for easy spaCy APIs using FastAPI",
"description": "Docker-based cookiecutter for easy spaCy APIs using FastAPI. The default endpoints expect batch requests with a list of Records in the Azure Search Cognitive Skill format. So out of the box, this cookiecutter can be setup as a Custom Cognitive Skill. For more on Azure Search and Cognitive Skills [see this page](https://docs.microsoft.com/en-us/azure/search/cognitive-search-custom-skill-interface).",
"url": "https://github.com/microsoft/cookiecutter-spacy-fastapi",
"image": "https://raw.githubusercontent.com/microsoft/cookiecutter-spacy-fastapi/master/images/cookiecutter-docs.png",
"github": "microsoft/cookiecutter-spacy-fastapi",
"category": ["apis"],
"thumb": "https://avatars0.githubusercontent.com/u/6154722",
"author": "Microsoft",
"author_links": {
"github": "microsoft"
}
},
{
"id": "dframcy",
"title": "Dframcy",
"slogan": "Dataframe Integration with spaCy NLP",
"github": "yash1994/dframcy",
"description": "DframCy is a light-weight utility module to integrate Pandas Dataframe to spaCy's linguistic annotation and training tasks.",
"pip": "dframcy",
"category": ["pipeline", "training"],
"tags": ["pandas"],
"code_example": [
"import spacy",
"from dframcy import DframCy",
"",
"nlp = spacy.load('en_core_web_sm')",
"dframcy = DframCy(nlp)",
"doc = dframcy.nlp(u'Apple is looking at buying U.K. startup for $1 billion')",
"annotation_dataframe = dframcy.to_dataframe(doc)"
],
"author": "Yash Patadia",
"author_links": {
"twitter": "PatadiaYash",
"github": "yash1994"
}
},
{
"id": "spacy-pytextrank",
"title": "PyTextRank",
"slogan": "Py impl of TextRank for lightweight phrase extraction",
"description": "An implementation of TextRank in Python for use in spaCy pipelines which provides fast, effective phrase extraction from texts, along with extractive summarization. The graph algorithm works independent of a specific natural language and does not require domain knowledge. See (Mihalcea 2004) https://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf",
"github": "DerwenAI/pytextrank",
"pip": "pytextrank",
"code_example": [
"import spacy",
"import pytextrank",
"",
"# example text",
"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. Upper bounds for components of a minimal set of",
"solutions and algorithms of construction of minimal generating sets of solutions for all types",
"of systems are given. These criteria and the corresponding algorithms for constructing a minimal",
"supporting set of solutions can be used in solving all the considered types systems and systems of mixed types.\"\"\"",
"",
"# load a spaCy model, depending on language, scale, etc.",
"nlp = spacy.load(\"en_core_web_sm\")",
"# add PyTextRank to the spaCy pipeline",
"nlp.add_pipe(\"textrank\")",
"",
"doc = nlp(text)",
"# examine the top-ranked phrases in the document",
"for phrase in doc._.phrases:",
" print(phrase.text)",
" print(phrase.rank, phrase.count)",
" print(phrase.chunks)"
],
"code_language": "python",
"url": "https://github.com/DerwenAI/pytextrank/wiki",
"thumb": "https://memegenerator.net/img/instances/66942896.jpg",
"image": "https://memegenerator.net/img/instances/66942896.jpg",
"author": "Paco Nathan",
"author_links": {
"twitter": "pacoid",
"github": "ceteri",
"website": "https://derwen.ai/paco"
},
"category": ["pipeline"],
"tags": ["phrase extraction", "ner", "summarization", "graph algorithms", "textrank"]
},
{
"id": "spacy_syllables",
"title": "Spacy Syllables",
"slogan": "Multilingual syllable annotations",
"description": "Spacy Syllables is a pipeline component that adds multilingual syllable annotations to Tokens. It uses Pyphen under the hood and has support for a long list of languages.",
"github": "sloev/spacy-syllables",
"pip": "spacy_syllables",
"code_example": [
"import spacy",
"from spacy_syllables import SpacySyllables",
"",
"nlp = spacy.load(\"en_core_web_sm\")",
"nlp.add_pipe(\"syllables\", after=\"tagger\")",
"",
"assert nlp.pipe_names == [\"tok2vec\", \"tagger\", \"syllables\", \"parser\", \"attribute_ruler\", \"lemmatizer\", \"ner\"]",
"doc = nlp(\"terribly long\")",
"data = [(token.text, token._.syllables, token._.syllables_count) for token in doc]",
"assert data == [(\"terribly\", [\"ter\", \"ri\", \"bly\"], 3), (\"long\", [\"long\"], 1)]"
],
"thumb": "https://raw.githubusercontent.com/sloev/spacy-syllables/master/logo.png",
"author": "Johannes Valbjørn",
"author_links": {
"github": "sloev"
},
"category": ["pipeline"],
"tags": ["syllables", "multilingual"]
},
{
"id": "gobbli",
"title": "gobbli",
"slogan": "Deep learning for text classification doesn't have to be scary",
"description": "gobbli is a Python library which wraps several modern deep learning models in a uniform interface that makes it easy to evaluate feasibility and conduct analyses. It leverages the abstractive powers of Docker to hide nearly all dependency management and functional differences between models from the user. It also contains an interactive app for exploring text data and evaluating classification models. spaCy's base text classification models, as well as models integrated from `spacy-transformers`, are available in the collection of classification models. In addition, spaCy is used for data augmentation and document embeddings.",
"url": "https://github.com/rtiinternational/gobbli",
"github": "rtiinternational/gobbli",
"pip": "gobbli",
"thumb": "https://i.postimg.cc/NGpzhrdr/gobbli-lg.png",
"code_example": [
"from gobbli.io import PredictInput, TrainInput",
"from gobbli.model.bert import BERT",
"",
"train_input = TrainInput(",
" X_train=['This is a training document.', 'This is another training document.'],",
" y_train=['0', '1'],",
" X_valid=['This is a validation sentence.', 'This is another validation sentence.'],",
" y_valid=['1', '0'],",
")",
"",
"clf = BERT()",
"",
"# Set up classifier resources -- Docker image, etc.",
"clf.build()",
"",
"# Train model",
"train_output = clf.train(train_input)",
"",
"predict_input = PredictInput(",
" X=['Which class is this document?'],",
" labels=train_output.labels,",
" checkpoint=train_output.checkpoint,",
")",
"",
"predict_output = clf.predict(predict_input)"
],
"category": ["standalone"]
},
{
"id": "spacy_fastlang",
"title": "Spacy FastLang",
"slogan": "Language detection done fast",
"description": "Fast language detection using FastText and Spacy.",
"github": "thomasthiebaud/spacy-fastlang",
"pip": "spacy_fastlang",
"code_example": [
"import spacy_fastlang",
"",
"nlp = spacy.load(\"en_core_web_sm\")",
"nlp.add_pipe(\"language_detector\")",
"doc = nlp('Life is like a box of chocolates. You never know what you are gonna get.')",
"",
"assert doc._.language == 'en'",
"assert doc._.language_score >= 0.8"
],
"author": "Thomas Thiebaud",
"author_links": {
"github": "thomasthiebaud"
},
"category": ["pipeline"]
},
{
"id": "mlflow",
"title": "MLflow",
"slogan": "An open source platform for the machine learning lifecycle",
"description": "MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components: Tracking, Projects, Models and Registry.",
"github": "mlflow/mlflow",
"pip": "mlflow",
"thumb": "https://www.mlflow.org/docs/latest/_static/MLflow-logo-final-black.png",
"image": "",
"url": "https://mlflow.org/",
"author": "Databricks",
"author_links": {
"github": "databricks",
"twitter": "databricks",
"website": "https://databricks.com/"
},
"category": ["standalone", "apis"],
"code_example": [
"import mlflow",
"import mlflow.spacy",
"",
"# MLflow Tracking",
"nlp = spacy.load('my_best_model_path/output/model-best')",
"with mlflow.start_run(run_name='Spacy'):",
" mlflow.set_tag('model_flavor', 'spacy')",
" mlflow.spacy.log_model(spacy_model=nlp, artifact_path='model')",
" mlflow.log_metric(('accuracy', 0.72))",
" my_run_id = mlflow.active_run().info.run_id",
"",
"",
"# MLflow Models",
"model_uri = f'runs:/{my_run_id}/model'",
"nlp2 = mlflow.spacy.load_model(model_uri=model_uri)"
]
},
{
"id": "pyate",
"title": "PyATE",
"slogan": "Python Automated Term Extraction",
"description": "PyATE is a term extraction library written in Python using Spacy POS tagging with Basic, Combo Basic, C-Value, TermExtractor, and Weirdness.",
"github": "kevinlu1248/pyate",
"pip": "pyate",
"code_example": [
"import spacy",
"import pyate",
"",
"nlp = spacy.load('en_core_web_sm')",
"nlp.add_pipe(\"combo_basic\") # or any of `basic`, `weirdness`, `term_extractor` or `cvalue`",
"# source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1994795/",
"string = 'Central to the development of cancer are genetic changes that endow these “cancer cells” with many of the hallmarks of cancer, such as self-sufficient growth and resistance to anti-growth and pro-death signals. However, while the genetic changes that occur within cancer cells themselves, such as activated oncogenes or dysfunctional tumor suppressors, are responsible for many aspects of cancer development, they are not sufficient. Tumor promotion and progression are dependent on ancillary processes provided by cells of the tumor environment but that are not necessarily cancerous themselves. Inflammation has long been associated with the development of cancer. This review will discuss the reflexive relationship between cancer and inflammation with particular focus on how considering the role of inflammation in physiologic processes such as the maintenance of tissue homeostasis and repair may provide a logical framework for understanding the connection between the inflammatory response and cancer.'",
"",
"doc = nlp(string)",
"print(doc._.combo_basic.sort_values(ascending=False).head(5))",
"\"\"\"\"\"\"",
"dysfunctional tumor 1.443147",
"tumor suppressors 1.443147",
"genetic changes 1.386294",
"cancer cells 1.386294",
"dysfunctional tumor suppressors 1.298612",
"\"\"\"\"\"\""
],
"code_language": "python",
"url": "https://github.com/kevinlu1248/pyate",
"author": "Kevin Lu",
"author_links": {
"twitter": "kevinlu1248",
"github": "kevinlu1248",
"website": "https://github.com/kevinlu1248/pyate"
},
"category": ["pipeline", "research"],
"tags": ["term_extraction"]
},
{
"id": "contextualSpellCheck",
"title": "Contextual Spell Check",
"slogan": "Contextual spell correction using BERT (bidirectional representations)",
"description": "This package currently focuses on Out of Vocabulary (OOV) word or non-word error (NWE) correction using BERT model. The idea of using BERT was to use the context when correcting NWE.",
"github": "R1j1t/contextualSpellCheck",
"pip": "contextualSpellCheck",
"code_example": [
"import spacy",
"import contextualSpellCheck",
"",
"nlp = spacy.load('en_core_web_sm')",
"contextualSpellCheck.add_to_pipe(nlp)",
"doc = nlp('Income was $9.4 milion compared to the prior year of $2.7 milion.')",
"",
"print(doc._.performed_spellCheck) #Should be True",
"print(doc._.outcome_spellCheck) #Income was $9.4 million compared to the prior year of $2.7 million."
],
"code_language": "python",
"url": "https://github.com/R1j1t/contextualSpellCheck",
"thumb": "https://user-images.githubusercontent.com/22280243/82760949-98e68480-9e14-11ea-952e-4738620fd9e3.png",
"image": "https://user-images.githubusercontent.com/22280243/82138959-2852cd00-9842-11ea-918a-49b2a7873ef6.png",
"author": "Rajat Goel",
"author_links": {
"github": "r1j1t",
"website": "https://github.com/R1j1t"
},
"category": ["pipeline", "conversational", "research"],
"tags": ["spell check", "correction", "preprocessing", "translation", "correction"]
},
{
"id": "texthero",
"title": "Texthero",
"slogan": "Text preprocessing, representation and visualization from zero to hero.",
"description": "Texthero is a python package to work with text data efficiently. It empowers NLP developers with a tool to quickly understand any text-based dataset and it provides a solid pipeline to clean and represent text data, from zero to hero.",
"github": "jbesomi/texthero",
"pip": "texthero",
"code_example": [
"import texthero as hero",
"import pandas as pd",
"",
"df = pd.read_csv('https://github.com/jbesomi/texthero/raw/master/dataset/bbcsport.csv')",
"df['named_entities'] = hero.named_entities(df['text'])",
"df.head()"
],
"code_language": "python",
"url": "https://texthero.org",
"thumb": "https://texthero.org/img/T.png",
"image": "https://texthero.org/docs/assets/texthero.png",
"author": "Jonathan Besomi",
"author_links": {
"github": "jbesomi",
"website": "https://besomi.ai"
},
"category": ["standalone"]
},
{
"id": "cov-bsv",
"title": "VA COVID-19 NLP BSV",
"slogan": "spaCy pipeline for COVID-19 surveillance.",
"github": "abchapman93/VA_COVID-19_NLP_BSV",
"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)"
],
"category": ["pipeline", "standalone", "biomedical", "scientific"],
"tags": ["clinical", "epidemiology", "covid-19", "surveillance"],
"author": "Alec Chapman",
"author_links": {
"github": "abchapman93"
}
},
{
"id": "medspacy",
"title": "medspaCy",
"thumb": "https://raw.githubusercontent.com/medspacy/medspacy/master/images/medspacy_logo.png",
"slogan": "A toolkit for clinical NLP with spaCy.",
"github": "medspacy/medspacy",
"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)"
],
"category": ["biomedical", "scientific", "research"],
"tags": ["clinical"],
"author": "medspacy",
"author_links": {
"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",
"code_language": "python",
"code_example": [
"import spacy",
"from rita.shortcuts import setup_spacy",
"",
"rules = \"\"\"",
"cuts = {\"fitted\", \"wide-cut\"}",
"lengths = {\"short\", \"long\", \"calf-length\", \"knee-length\"}",
"fabric_types = {\"soft\", \"airy\", \"crinkled\"}",
"fabrics = {\"velour\", \"chiffon\", \"knit\", \"woven\", \"stretch\"}",
"",
"{IN_LIST(cuts)?, IN_LIST(lengths), WORD(\"dress\")}->MARK(\"DRESS_TYPE\")",
"{IN_LIST(lengths), IN_LIST(cuts), WORD(\"dress\")}->MARK(\"DRESS_TYPE\")",
"{IN_LIST(fabric_types)?, IN_LIST(fabrics)}->MARK(\"DRESS_FABRIC\")",
"\"\"\"",
"",
"nlp = spacy.load(\"en\")",
"setup_spacy(nlp, rules_string=rules)",
"r = nlp(\"She was wearing a short wide-cut dress\")",
"print(list([{\"label\": e.label_, \"text\": e.text} for e in r.ents]))"
],
"category": ["standalone"],
"tags": ["dsl", "language-patterns", "language-rules", "nlp"],
"author": "Šarūnas Navickas",
"author_links": {
"github": "zaibacu"
}
},
{
"id": "PatternOmatic",
"title": "PatternOmatic",
"slogan": "Finds linguistic patterns effortlessly",
"description": "Discover spaCy's linguistic patterns matching a given set of String samples to be used by the spaCy's Rule Based Matcher",
"github": "revuel/PatternOmatic",
"pip": "PatternOmatic",
"code_example": [
"from PatternOmatic.api import find_patterns",
"",
"samples = ['I am a cat!', 'You are a dog!', 'She is an owl!']",
"",
"patterns_found, _ = find_patterns(samples)",
"",
"print(f'Patterns found: {patterns_found}')"
],
"code_language": "python",
"thumb": "https://svgshare.com/i/R3P.svg",
"image": "https://svgshare.com/i/R3P.svg",
"author": "Miguel Revuelta Espinosa",
"author_links": {
"github": "revuel"
},
"category": ["scientific", "research", "standalone"],
"tags": ["Evolutionary Computation", "Grammatical Evolution"]
},
{
"id": "SpacyDotNet",
"title": "spaCy .NET Wrapper",
"slogan": "SpacyDotNet is a .NET Core compatible wrapper for spaCy, based on Python.NET",
"description": "This projects relies on [Python.NET](http://pythonnet.github.io/) to interop with spaCy. It's not meant to be a complete and exhaustive implementation of all spaCy features and [APIs](https://spacy.io/api). Although it should be enough for basic tasks, it's considered as a starting point if you need to build a complex project using spaCy in .NET Most of the basic features in _Spacy101_ are available. All `Container` classes are present (`Doc`, `Token`, `Span` and `Lexeme`) with their basic properties/methods running and also `Vocab` and `StringStore` in a limited form. Anyway, any developer should be ready to add the missing properties or classes in a very straightforward manner.",
"github": "AMArostegui/SpacyDotNet",
"thumb": "https://raw.githubusercontent.com/AMArostegui/SpacyDotNet/master/cslogo.png",
"code_example": [
"var spacy = new Spacy();",
"",
"var nlp = spacy.Load(\"en_core_web_sm\");",
"var doc = nlp.GetDocument(\"Apple is looking at buying U.K. startup for $1 billion\");",
"",
"foreach (Token token in doc.Tokens)",
" Console.WriteLine($\"{token.Text} {token.Lemma} {token.PoS} {token.Tag} {token.Dep} {token.Shape} {token.IsAlpha} {token.IsStop}\");",
"",
"Console.WriteLine(\"\");",
"foreach (Span ent in doc.Ents)",
" Console.WriteLine($\"{ent.Text} {ent.StartChar} {ent.EndChar} {ent.Label}\");",
"",
"nlp = spacy.Load(\"en_core_web_md\");",
"var tokens = nlp.GetDocument(\"dog cat banana afskfsd\");",
"",
"Console.WriteLine(\"\");",
"foreach (Token token in tokens.Tokens)",
" Console.WriteLine($\"{token.Text} {token.HasVector} {token.VectorNorm}, {token.IsOov}\");",
"",
"tokens = nlp.GetDocument(\"dog cat banana\");",
"Console.WriteLine(\"\");",
"foreach (Token token1 in tokens.Tokens)",
"{",
" foreach (Token token2 in tokens.Tokens)",
" Console.WriteLine($\"{token1.Text} {token2.Text} {token1.Similarity(token2) }\");",
"}",
"",
"doc = nlp.GetDocument(\"I love coffee\");",
"Console.WriteLine(\"\");",
"Console.WriteLine(doc.Vocab.Strings[\"coffee\"]);",
"Console.WriteLine(doc.Vocab.Strings[3197928453018144401]);",
"",
"Console.WriteLine(\"\");",
"foreach (Token word in doc.Tokens)",
"{",
" var lexeme = doc.Vocab[word.Text];",
" Console.WriteLine($@\"{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": {
"github": "AMArostegui"
},
"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": "trunajod",
"title": "TRUNAJOD",
"slogan": "A text complexity library for text analysis built on spaCy",
"description": "With all the basic NLP capabilities provided by spaCy (dependency parsing, POS tagging, tokenizing), `TRUNAJOD` focuses on extracting measurements from texts that might be interesting for different applications and use cases.",
"github": "dpalmasan/TRUNAJOD2.0",
"pip": "trunajod",
"code_example": [
"import spacy",
"from TRUNAJOD.entity_grid import EntityGrid",
"",
"nlp = spacy.load('es_core_news_sm', disable=['ner', 'textcat'])",
"example_text = (",
" 'El espectáculo del cielo nocturno cautiva la mirada y suscita preguntas'",
" 'sobre el universo, su origen y su funcionamiento. No es sorprendente que '",
" 'todas las civilizaciones y culturas hayan formado sus propias '",
" 'cosmologías. Unas relatan, por ejemplo, que el universo ha'",
" 'sido siempre tal como es, con ciclos que inmutablemente se repiten; '",
" 'otras explican que este universo ha tenido un principio, '",
" 'que ha aparecido por obra creadora de una divinidad.'",
")",
"doc = nlp(example_text)",
"egrid = EntityGrid(doc)",
"print(egrid.get_egrid())"
],
"code_language": "python",
"thumb": "https://raw.githubusercontent.com/dpalmasan/TRUNAJOD2.0/master/imgs/trunajod_thumb.png",
"image": "https://raw.githubusercontent.com/dpalmasan/TRUNAJOD2.0/master/imgs/trunajod_logo.png",
"author": "Diego Palma",
"author_links": {
"github": "dpalmasan"
},
"category": ["research", "standalone", "scientific"],
"tags": ["Text Analytics", "Coherence", "Cohesion"]
},
{
"id": "lingfeat",
"title": "LingFeat",
"slogan": "A Linguistic Feature Extraction (Text Analysis) Tool for Readability Assessment and Text Simplification",
"description": "LingFeat is a feature extraction library which currently extracts 255 linguistic features from English string input. Categories include syntax, semantics, discourse, and also traditional readability formulas. Published in EMNLP 2021.",
"github": "brucewlee/lingfeat",
"pip": "lingfeat",
"code_example": [
"from lingfeat import extractor",
"",
"",
"text = 'TAEAN, South Chungcheong Province -- Just before sunup, Lee Young-ho, a seasoned fisherman with over 30 years of experience, silently waits for boats carrying blue crabs as the season for the seafood reaches its height. Soon afterward, small and big boats sail into Sinjin Port in Taean County, South Chungcheong Province, the second-largest source of blue crab after Incheon, accounting for 29 percent of total production of the country. A crane lifts 28 boxes filled with blue crabs weighing 40 kilograms each from the boat, worth about 10 million won ($8,500). “It has been a productive fall season for crabbing here. The water temperature is a very important factor affecting crab production. They hate cold water,” Lee said. The temperature of the sea off Taean appeared to have stayed at the level where crabs become active. If the sea temperature suddenly drops, crabs go into their winter dormancy mode, burrowing into the mud and sleeping through the cold months.'",
"",
"",
"#Pass text",
"LingFeat = extractor.pass_text(text)",
"",
"",
"#Preprocess text",
"LingFeat.preprocess()",
"",
"",
"#Extract features",
"#each method returns a dictionary of the corresponding features",
"#Advanced Semantic (AdSem) Features",
"WoKF = LingFeat.WoKF_() #Wikipedia Knowledge Features",
"WBKF = LingFeat.WBKF_() #WeeBit Corpus Knowledge Features",
"OSKF = LingFeat.OSKF_() #OneStopEng Corpus Knowledge Features",
"",
"#Discourse (Disco) Features",
"EnDF = LingFeat.EnDF_() #Entity Density Features",
"EnGF = LingFeat.EnGF_() #Entity Grid Features",
"",
"#Syntactic (Synta) Features",
"PhrF = LingFeat.PhrF_() #Noun/Verb/Adj/Adv/... Phrasal Features",
"TrSF = LingFeat.TrSF_() #(Parse) Tree Structural Features",
"POSF = LingFeat.POSF_() #Noun/Verb/Adj/Adv/... Part-of-Speech Features",
"",
"#Lexico Semantic (LxSem) Features",
"TTRF = LingFeat.TTRF_() #Type Token Ratio Features",
"VarF = LingFeat.VarF_() #Noun/Verb/Adj/Adv Variation Features",
"PsyF = LingFeat.PsyF_() #Psycholinguistic Difficulty of Words (AoA Kuperman)",
"WoLF = LingFeat.WorF_() #Word Familiarity from Frequency Count (SubtlexUS)",
"",
"Shallow Traditional (ShTra) Features",
"ShaF = LingFeat.ShaF_() #Shallow Features (e.g. avg number of tokens)",
"TraF = LingFeat.TraF_() #Traditional Formulas"
],
"code_language": "python",
"thumb": "https://raw.githubusercontent.com/brucewlee/lingfeat/master/img/lingfeat_logo2.png",
"image": "https://raw.githubusercontent.com/brucewlee/lingfeat/master/img/lingfeat_logo.png",
"author": "Bruce W. Lee (이웅성)",
"author_links": {
"github": "brucewlee",
"website": "https://brucewlee.github.io/"
},
"category": ["research", "scientific"],
"tags": ["Readability", "Simplification", "Feature Extraction", "Syntax", "Discourse", "Semantics", "Lexical"]
},
{
"id": "hmrb",
"title": "Hammurabi",
"slogan": "Python Rule Processing Engine 🏺",
"description": "Hammurabi works as a rule engine to parse input using a defined set of rules. It uses a simple and readable syntax to define complex rules to handle phrase matching. The syntax supports nested logical statements, regular expressions, reusable or side-loaded variables and match triggered callback functions to modularize your rules. The latest version works with both spaCy 2.X and 3.X. For more information check the documentation on [ReadTheDocs](https://hmrb.readthedocs.io/en/latest/).",
"github": "babylonhealth/hmrb",
"pip": "hmrb",
"code_example": [
"import spacy",
"from hmrb.core import SpacyCore",
"",
"nlp = spacy.load(\"en_core_web_sm\")",
"sentences = \"I love gorillas. Peter loves gorillas. Jane loves Tarzan.\"",
"",
"def conj_be(subj: str) -> str:",
" if subj == \"I\":",
" return \"am\"",
" elif subj == \"you\":",
" return \"are\"",
" else:",
" return \"is\"",
"",
"@spacy.registry.callbacks(\"gorilla_callback\")",
"def gorilla_clb(seq: list, span: slice, data: dict) -> None:",
" subj = seq[span.start].text",
" be = conj_be(subj)",
" print(f\"{subj} {be} a gorilla person.\")",
"@spacy.registry.callbacks(\"lover_callback\")",
"def lover_clb(seq: list, span: slice, data: dict) -> None:",
" print(f\"{seq[span][-1].text} is a love interest of {seq[span.start].text}.\")",
"",
"grammar = \"\"\"",
" Law:",
" - callback: \"loves_gorilla\"",
" (",
" ((pos: \"PROPN\") or (pos: \"PRON\"))",
" (lemma: \"love\")",
" (lemma: \"gorilla\")",
" )",
" Law:",
" - callback: \"loves_someone\"",
" (",
" (pos: \"PROPN\")",
" (lower: \"loves\")",
" (pos: \"PROPN\")",
" )",
"\"\"\"",
"",
"@spacy.registry.augmenters(\"jsonify_span\")",
"def jsonify_span(span):",
" return [{\"lemma\": token.lemma_, \"pos\": token.pos_, \"lower\": token.lower_} for token in span]",
"",
"conf = {",
" \"rules\": grammar,",
" \"callbacks\": {",
" \"loves_gorilla\": \"callbacks.gorilla_callback\",",
" \"loves_someone\": \"callbacks.lover_callback\",",
" },",
" \"map_doc\": \"augmenters.jsonify_span\",",
" \"sort_length\": True,",
"}",
"",
"nlp.add_pipe(\"hmrb\", config=conf)",
"nlp(sentences)"
],
"code_language": "python",
"thumb": "https://user-images.githubusercontent.com/6807878/118643685-cae6b880-b7d4-11eb-976e-066aec9505da.png",
"image": "https://user-images.githubusercontent.com/6807878/118643685-cae6b880-b7d4-11eb-976e-066aec9505da.png",
"author": "Kristian Boda",
"author_links": {
"github": "bodak",
"twitter": "bodak",
"website": "https://github.com/babylonhealth/"
},
"category": ["pipeline", "standalone", "scientific", "biomedical"],
"tags": ["babylonhealth", "rule-engine", "matcher"]
},
{
"id": "forte",
"title": "Forte",
"slogan": "Forte is a toolkit for building Natural Language Processing pipelines, featuring cross-task interaction, adaptable data-model interfaces and composable pipelines.",
"description": "Forte provides a platform to assemble state-of-the-art NLP and ML technologies in a highly-composable fashion, including a wide spectrum of tasks ranging from Information Retrieval, Natural Language Understanding to Natural Language Generation.",
"github": "asyml/forte",
"pip": "forte.spacy stave torch",
"code_example": [
"from fortex.spacy import SpacyProcessor",
"from forte.processors.stave import StaveProcessor",
"from forte import Pipeline",
"from forte.data.readers import StringReader",
"",
"pipeline = Pipeline()",
"pipeline.set_reader(StringReader())",
"pipeline.add(SpacyProcessor())",
"pipeline.add(StaveProcessor())",
"pipeline.run('Running SpaCy with Forte!')"
],
"code_language": "python",
"url": "https://medium.com/casl-project/forte-building-modular-and-re-purposable-nlp-pipelines-cf5b5c5abbe9",
"thumb": "https://raw.githubusercontent.com/asyml/forte/master/docs/_static/img/forte_graphic.png",
"image": "https://raw.githubusercontent.com/asyml/forte/master/docs/_static/img/logo_h.png",
"author": "Petuum",
"author_links": {
"twitter": "PetuumInc",
"github": "asyml",
"website": "https://petuum.com"
},
"category": ["pipeline", "standalone"],
"tags": ["pipeline"]
},
{
"id": "spacy-api-docker-v3",
"slogan": "spaCy v3 REST API, wrapped in a Docker container",
"github": "bbieniek/spacy-api-docker",
"url": "https://hub.docker.com/r/bbieniek/spacyapi/",
"thumb": "https://i.imgur.com/NRnDKyj.jpg",
"code_example": [
"version: '3'",
"",
"services:",
" spacyapi:",
" image: bbieniek/spacyapi:en_v3",
" ports:",
" - \"127.0.0.1:8080:80\"",
" restart: always"
],
"code_language": "docker",
"author": "Baltazar Bieniek",
"author_links": {
"github": "bbieniek"
},
"category": ["apis"]
},
{
"id": "phruzz_matcher",
"title": "phruzz-matcher",
"slogan": "Phrase matcher using RapidFuzz",
"description": "Combination of the RapidFuzz library with Spacy PhraseMatcher The goal of this component is to find matches when there were NO \"perfect matches\" due to typos or abbreviations between a Spacy doc and a list of phrases.",
"github": "mjvallone/phruzz-matcher",
"pip": "phruzz_matcher",
"code_example": [
"import spacy",
"from spacy.language import Language",
"from phruzz_matcher.phrase_matcher import PhruzzMatcher",
"",
"famous_people = [",
" \"Brad Pitt\",",
" \"Demi Moore\",",
" \"Bruce Willis\",",
" \"Jim Carrey\",",
"]",
"",
"@Language.factory(\"phrase_matcher\")",
"def phrase_matcher(nlp: Language, name: str):",
" return PhruzzMatcher(nlp, famous_people, \"FAMOUS_PEOPLE\", 85)",
"",
"nlp = spacy.blank('es')",
"nlp.add_pipe(\"phrase_matcher\")",
"",
"doc = nlp(\"El otro día fui a un bar donde vi a brad pit y a Demi Moore, estaban tomando unas cervezas mientras charlaban de sus asuntos.\")",
"print(f\"doc.ents: {doc.ents}\")",
"",
"#OUTPUT",
"#doc.ents: (brad pit, Demi Moore)"
],
"thumb": "https://avatars.githubusercontent.com/u/961296?v=4",
"image": "",
"code_language": "python",
"author": "Martin Vallone",
"author_links": {
"github": "mjvallone",
"twitter": "vallotin",
"website": "https://fiqus.coop/"
},
"category": ["pipeline", "research", "standalone"],
"tags": ["spacy", "python", "nlp", "ner"]
},
{
"id": "WordDumb",
"title": "WordDumb",
"slogan": "A calibre plugin that generates Word Wise and X-Ray files.",
"description": "A calibre plugin that generates Word Wise and X-Ray files then sends them to Kindle. Supports KFX, AZW3 and MOBI eBooks. X-Ray supports 18 languages.",
"github": "xxyzz/WordDumb",
"code_language": "python",
"thumb": "https://raw.githubusercontent.com/xxyzz/WordDumb/master/starfish.svg",
"image": "https://user-images.githubusercontent.com/21101839/130245435-b874f19a-7785-4093-9975-81596efc42bb.png",
"author": "xxyzz",
"author_links": {
"github": "xxyzz"
},
"category": ["standalone"]
},
{
"id": "eng_spacysentiment",
"title": "eng_spacysentiment",
"slogan": "Simple sentiment analysis using spaCy pipelines",
"description": "Sentiment analysis for simple english sentences using pre-trained spaCy pipelines",
"github": "vishnunkumar/spacysentiment",
"pip": "eng-spacysentiment",
"code_example": [
"import eng_spacysentiment",
"nlp = eng_spacysentiment.load()",
"text = \"Welcome to Arsenals official YouTube channel Watch as we take you closer and show you the personality of the club\"",
"doc = nlp(text)",
"print(doc.cats)",
"# {'positive': 0.29878824949264526, 'negative': 0.7012117505073547}"
],
"thumb": "",
"image": "",
"code_language": "python",
"author": "Vishnu Nandakumar",
"author_links": {
"github": "Vishnunkumar",
"twitter": "vishnun_uchiha"
},
"category": ["pipeline"],
"tags": ["pipeline", "nlp", "sentiment"]
},
{
"id": "textnets",
"slogan": "Text analysis with networks",
"description": "textnets represents collections of texts as networks of documents and words. This provides novel possibilities for the visualization and analysis of texts.",
"github": "jboynyc/textnets",
"image": "https://user-images.githubusercontent.com/2187261/152641425-6c0fb41c-b8e0-44fb-a52a-7c1ba24eba1e.png",
"code_example": [
"import textnets as tn",
"",
"corpus = tn.Corpus(tn.examples.moon_landing)",
"t = tn.Textnet(corpus.tokenized(), min_docs=1)",
"t.plot(label_nodes=True,",
" show_clusters=True,",
" scale_nodes_by=\"birank\",",
" scale_edges_by=\"weight\")"
],
"author": "John Boy",
"author_links": {
"github": "jboynyc",
"twitter": "jboy"
},
"category": ["visualizers", "standalone"]
},
{
"id": "tmtoolkit",
"slogan": "Text mining and topic modeling toolkit",
"description": "tmtoolkit is a set of tools for text mining and topic modeling with Python developed especially for the use in the social sciences, in journalism or related disciplines. It aims for easy installation, extensive documentation and a clear programming interface while offering good performance on large datasets by the means of vectorized operations (via NumPy) and parallel computation (using Python’s multiprocessing module and the loky package).",
"github": "WZBSocialScienceCenter/tmtoolkit",
"code_example": [
"# Note: This requires these setup steps:",
"# pip install tmtoolkit[recommended]",
"# python -m tmtoolkit setup en",
"from tmtoolkit.corpus import Corpus, tokens_table, lemmatize, to_lowercase, dtm",
"from tmtoolkit.bow.bow_stats import tfidf, sorted_terms_table",
"# load built-in sample dataset and use 4 worker processes",
"corp = Corpus.from_builtin_corpus('en-News100', max_workers=4)",
"# investigate corpus as dataframe",
"toktbl = tokens_table(corp)",
"print(toktbl)",
"# apply some text normalization",
"lemmatize(corp)",
"to_lowercase(corp)",
"# build sparse document-token matrix (DTM)",
"# document labels identify rows, vocabulary tokens identify columns",
"mat, doc_labels, vocab = dtm(corp, return_doc_labels=True, return_vocab=True)",
"# apply tf-idf transformation to DTM",
"# operation is applied on sparse matrix and uses few memory",
"tfidf_mat = tfidf(mat)",
"# show top 5 tokens per document ranked by tf-idf",
"top_tokens = sorted_terms_table(tfidf_mat, vocab, doc_labels, top_n=5)",
"print(top_tokens)"
],
"author": "Markus Konrad / WZB Social Science Center",
"author_links": {
"github": "internaut",
"twitter": "_knrd"
},
"category": ["scientific", "standalone"]
},
{
"id": "edsnlp",
"title": "EDS-NLP",
"slogan": "spaCy components to extract information from clinical notes written in French.",
"description": "EDS-NLP provides a set of rule-based spaCy components to extract information for French clinical notes. It also features _qualifier_ pipelines that detect negations, speculations and family context, among other modalities. Check out the [demo](https://aphp.github.io/edsnlp/demo/)!",
"github": "aphp/edsnlp",
"pip": "edsnlp",
"code_example": [
"import spacy",
"",
"nlp = spacy.blank(\"fr\")",
"",
"terms = dict(",
" covid=[\"covid\", \"coronavirus\"],",
")",
"",
"# Sentencizer component, needed for negation detection",
"nlp.add_pipe(\"eds.sentences\")",
"# Matcher component",
"nlp.add_pipe(\"eds.matcher\", config=dict(terms=terms))",
"# Negation detection",
"nlp.add_pipe(\"eds.negation\")",
"",
"# Process your text in one call !",
"doc = nlp(\"Le patient est atteint de covid\")",
"",
"doc.ents",
"# Out: (covid,)",
"",
"doc.ents[0]._.negation",
"# Out: False"
],
"code_language": "python",
"url": "https://aphp.github.io/edsnlp/",
"author": "AP-HP",
"author_links": {
"github": "aphp",
"website": "https://github.com/aphp"
},
"category": ["biomedical", "scientific", "research", "pipeline"],
"tags": ["clinical"]
},
{
"id": "sent-pattern",
"title": "English Interpretation Sentence Pattern",
"slogan": "English interpretation for accurate translation from English to Japanese",
"description": "This package categorizes English sentences into one of five basic sentence patterns and identifies the subject, verb, object, and other components. The five basic sentence patterns are based on C. T. Onions's Advanced English Syntax and are frequently used when teaching English in Japan.",
"github": "lll-lll-lll-lll/sent-pattern",
"pip": "sent-pattern",
"author": "Shunpei Nakayama",
"author_links": {
"twitter": "ExZ79575296",
"github": "lll-lll-lll-lll"
},
"category": ["pipeline"],
"tags": ["interpretation", "ja"]
}
],
"categories": [
{
"label": "Projects",
"items": [
{
"id": "pipeline",
"title": "Pipeline",
"description": "Custom pipeline components and extensions"
},
{
"id": "training",
"title": "Training",
"description": "Helpers and toolkits for training spaCy models"
},
{
"id": "conversational",
"title": "Conversational",
"description": "Frameworks and utilities for working with conversational text, e.g. for chat bots"
},
{
"id": "research",
"title": "Research",
"description": "Frameworks and utilities for developing better NLP models, especially using neural networks"
},
{
"id": "scientific",
"title": "Scientific",
"description": "Frameworks and utilities for scientific text processing"
},
{
"id": "biomedical",
"title": "Biomedical",
"description": "Frameworks and utilities for processing biomedical text"
},
{
"id": "visualizers",
"title": "Visualizers",
"description": "Demos and tools to visualize NLP annotations or systems"
},
{
"id": "apis",
"title": "Containers & APIs",
"description": "Infrastructure tools for managing or deploying spaCy"
},
{
"id": "nonpython",
"title": "Non-Python",
"description": "Wrappers, bindings and implementations in other programming languages"
},
{
"id": "standalone",
"title": "Standalone",
"description": "Self-contained libraries or tools that use spaCy under the hood"
},
{
"id": "models",
"title": "Models",
"description": "Third-party pretrained models for different languages and domains"
}
]
},
{
"label": "Education",
"items": [
{
"id": "books",
"title": "Books",
"description": "Books about or featuring spaCy"
},
{
"id": "courses",
"title": "Courses",
"description": "Online courses and interactive tutorials"
},
{
"id": "videos",
"title": "Videos",
"description": "Talks and tutorials in video format"
},
{
"id": "podcasts",
"title": "Podcasts",
"description": "Episodes about spaCy or interviews with the spaCy team"
}
]
}
]
}
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