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spaCy | Industrial-strength Natural Language Processing | Natural Language Processing library

 by   explosion Python Version: v3.1.6 License: MIT

 by   explosion Python Version: v3.1.6 License: MIT

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kandi X-RAY | spaCy Summary

spaCy is a Python library typically used in Artificial Intelligence, Natural Language Processing, Deep Learning, Pytorch, Bert applications. spaCy has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However spaCy has 8 bugs. You can download it from GitHub.
spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. spaCy comes with pretrained pipelines and currently supports tokenization and training for 60+ languages. It features state-of-the-art speed and neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pretrained transformers like BERT, as well as a production-ready training system and easy model packaging, deployment and workflow management. spaCy is commercial open-source software, released under the MIT license.
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kandi-support Support

  • spaCy has a medium active ecosystem.
  • It has 23063 star(s) with 3786 fork(s). There are 559 watchers for this library.
  • There were 7 major release(s) in the last 12 months.
  • There are 84 open issues and 4990 have been closed. On average issues are closed in 32 days. There are 15 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of spaCy is v3.1.6
spaCy Support
Best in #Natural Language Processing
Average in #Natural Language Processing
spaCy Support
Best in #Natural Language Processing
Average in #Natural Language Processing

quality kandi Quality

  • spaCy has 8 bugs (4 blocker, 0 critical, 3 major, 1 minor) and 1001 code smells.
spaCy Quality
Best in #Natural Language Processing
Average in #Natural Language Processing
spaCy Quality
Best in #Natural Language Processing
Average in #Natural Language Processing

securitySecurity

  • spaCy has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • spaCy code analysis shows 0 unresolved vulnerabilities.
  • There are 93 security hotspots that need review.
spaCy Security
Best in #Natural Language Processing
Average in #Natural Language Processing
spaCy Security
Best in #Natural Language Processing
Average in #Natural Language Processing

license License

  • spaCy is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
spaCy License
Best in #Natural Language Processing
Average in #Natural Language Processing
spaCy License
Best in #Natural Language Processing
Average in #Natural Language Processing

buildReuse

  • spaCy releases are available to install and integrate.
  • Build file is available. You can build the component from source.
  • Installation instructions, examples and code snippets are available.
spaCy Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
spaCy Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
Top functions reviewed by kandi - BETA

kandi has reviewed spaCy and discovered the below as its top functions. This is intended to give you an instant insight into spaCy implemented functionality, and help decide if they suit your requirements.

  • Defines a factory
    • Returns a fully qualified name for the given language
    • Sets the factory meta
    • Register a factory function
  • Compute the PRF score for the given examples
    • Calculate the tp score
  • Embed a character embedding
    • Construct a model of static vectors
  • Lemmatize a token
    • Get a table by name
  • Command line interface for debugging
    • Parse dependencies
      • Process if node
        • Create a model of static vectors
          • Lemmatize a word
            • Parse command line interface
              • Lemmatize rule
                • Setup package
                  • Forward layer computation
                    • Lemmatize a specific word
                      • Update the model with the given examples
                        • Builds a token embedding model
                          • Command line interface for pretraining
                            • Extract the words from the wiktionary
                              • Rehearse the language
                                • Process a for loop
                                  • Lemmatize a rule

                                    Get all kandi verified functions for this library.

                                    Get all kandi verified functions for this library.

                                    spaCy Key Features

                                    Support for 60+ languages

                                    Trained pipelines for different languages and tasks

                                    Multi-task learning with pretrained transformers like BERT

                                    Support for pretrained word vectors and embeddings

                                    State-of-the-art speed

                                    Production-ready training system

                                    Linguistically-motivated tokenization

                                    Components for named entity recognition, part-of-speech-tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking and more

                                    Easily extensible with custom components and attributes

                                    Support for custom models in PyTorch, TensorFlow and other frameworks

                                    Built in visualizers for syntax and NER

                                    Easy model packaging, deployment and workflow management

                                    Robust, rigorously evaluated accuracy

                                    pip

                                    copy iconCopydownload iconDownload
                                    pip install -U pip setuptools wheel
                                    pip install spacy
                                    

                                    conda

                                    copy iconCopydownload iconDownload
                                    conda install -c conda-forge spacy
                                    

                                    Updating spaCy

                                    copy iconCopydownload iconDownload
                                    pip install -U spacy
                                    python -m spacy validate
                                    

                                    📦 Download model packages

                                    copy iconCopydownload iconDownload
                                    # Download best-matching version of specific model for your spaCy installation
                                    python -m spacy download en_core_web_sm
                                    
                                    # pip install .tar.gz archive or .whl from path or URL
                                    pip install /Users/you/en_core_web_sm-3.0.0.tar.gz
                                    pip install /Users/you/en_core_web_sm-3.0.0-py3-none-any.whl
                                    pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.tar.gz
                                    

                                    Loading and using models

                                    copy iconCopydownload iconDownload
                                    import spacy
                                    nlp = spacy.load("en_core_web_sm")
                                    doc = nlp("This is a sentence.")
                                    

                                    ⚒ Compile from source

                                    copy iconCopydownload iconDownload
                                    git clone https://github.com/explosion/spaCy
                                    cd spaCy
                                    
                                    python -m venv .env
                                    source .env/bin/activate
                                    
                                    # make sure you are using the latest pip
                                    python -m pip install -U pip setuptools wheel
                                    
                                    pip install -r requirements.txt
                                    pip install --no-build-isolation --editable .
                                    

                                    🚦 Run tests

                                    copy iconCopydownload iconDownload
                                    pip install -r requirements.txt
                                    python -m pytest --pyargs spacy
                                    

                                    Save and load nlp results in spacy

                                    copy iconCopydownload iconDownload
                                    python -m spacy download en_core_web_md
                                    
                                    import spacy
                                    from spacy.tokens import Doc
                                    from spacy.vocab import Vocab
                                    
                                    nlp = spacy.load('en_core_web_md')
                                    doc = nlp("He eats a green apple")
                                    for token in doc:
                                        print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_,
                                              token.shape_, token.is_alpha, token.is_stop)
                                    
                                    NLP_FName = "E:\\SaveTest.nlp"
                                    doc.to_disk(NLP_FName)
                                    Vocab_FName = "E:\\SaveTest.voc"
                                    doc.vocab.to_disk(Vocab_FName)
                                    
                                    #To read the data again:
                                    idoc = Doc(Vocab()).from_disk(NLP_FName)
                                    idoc.vocab.from_disk(Vocab_FName)
                                    
                                    for token in idoc:
                                        print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_,
                                              token.shape_, token.is_alpha, token.is_stop)
                                    
                                    python -m spacy download en_core_web_md
                                    
                                    import spacy
                                    from spacy.tokens import Doc
                                    from spacy.vocab import Vocab
                                    
                                    nlp = spacy.load('en_core_web_md')
                                    doc = nlp("He eats a green apple")
                                    for token in doc:
                                        print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_,
                                              token.shape_, token.is_alpha, token.is_stop)
                                    
                                    NLP_FName = "E:\\SaveTest.nlp"
                                    doc.to_disk(NLP_FName)
                                    Vocab_FName = "E:\\SaveTest.voc"
                                    doc.vocab.to_disk(Vocab_FName)
                                    
                                    #To read the data again:
                                    idoc = Doc(Vocab()).from_disk(NLP_FName)
                                    idoc.vocab.from_disk(Vocab_FName)
                                    
                                    for token in idoc:
                                        print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_,
                                              token.shape_, token.is_alpha, token.is_stop)
                                    
                                    import spacy
                                    from spacy.tokens import DocBin
                                    
                                    doc_bin = DocBin()
                                    texts = ["Some text", "Lots of texts...", "..."]
                                    nlp = spacy.load("en_core_web_sm")
                                    for doc in nlp.pipe(texts):
                                        doc_bin.add(doc)
                                    
                                    bytes_data = doc_bin.to_bytes()
                                    
                                    # Deserialize later, e.g. in a new process
                                    nlp = spacy.blank("en")
                                    doc_bin = DocBin().from_bytes(bytes_data)
                                    docs = list(doc_bin.get_docs(nlp.vocab))
                                    

                                    How to match repeating patterns in spacy?

                                    copy iconCopydownload iconDownload
                                    patterns = []
                                    for ii in range(1, 5):
                                        pattern = [{"POS": "ADJ"}, {"IS_PUNCT":True}] * ii
                                        pattern += [{"POS": "ADJ"}, {"POS": "CCONJ"}, {"POS": "ADJ"}]
                                        patterns.append(pattern)
                                    

                                    Spacy tokenization add extra white space for dates with hyphen separator when I manually build the Doc

                                    copy iconCopydownload iconDownload
                                    from spacy.language import Doc
                                    doc2 = Doc(nlp.vocab, words=tokens,spaces=[1,1,1,1,1,1,0,0,0,0,0,0])
                                    # Run each model in pipeline
                                    for model_name in nlp.pipe_names:
                                        pipe = nlp.get_pipe(model_name)
                                        doc2 = pipe(doc2)
                                    
                                    # Print text and tokens
                                    print(doc2.text)
                                    tokens = [str(token) for token in doc2]
                                    print(tokens)
                                    
                                    # Show entities
                                    print(doc.ents[0].label_)
                                    print(doc.ents[0].text)
                                    
                                    # You can also replace 0 with False and 1 with True
                                    
                                    doc = Doc(nlp.vocab, words=words, spaces=spaces)
                                    
                                    from spacy.language import Doc
                                    doc2 = Doc(nlp.vocab, words=tokens,spaces=[1,1,1,1,1,1,0,0,0,0,0,0])
                                    # Run each model in pipeline
                                    for model_name in nlp.pipe_names:
                                        pipe = nlp.get_pipe(model_name)
                                        doc2 = pipe(doc2)
                                    
                                    # Print text and tokens
                                    print(doc2.text)
                                    tokens = [str(token) for token in doc2]
                                    print(tokens)
                                    
                                    # Show entities
                                    print(doc.ents[0].label_)
                                    print(doc.ents[0].text)
                                    
                                    # You can also replace 0 with False and 1 with True
                                    
                                    doc = Doc(nlp.vocab, words=words, spaces=spaces)
                                    

                                    After installing scrubadub_spacy package, spacy.load("en_core_web_sm") not working OSError: [E053] Could not read config.cfg

                                    copy iconCopydownload iconDownload
                                    en_core_web_sm-2.3.1/config.cfg
                                    

                                    Show NER Spacy Data in dataframe

                                    copy iconCopydownload iconDownload
                                    #... your code here ...
                                    body=soup.body.text
                                    
                                    # now, this is the modification:
                                    body = ' '.join(body.split())
                                    doc = NER(body)
                                    entities = [(e.label_,e.text) for e in doc.ents]
                                    df = pd.DataFrame(entities, columns=['Entity','Identified'])
                                    

                                    How to use japanese engine in Spacy

                                    copy iconCopydownload iconDownload
                                    pip spacy download ja_core_news_lg
                                    
                                    python -m spacy download ja_core_news_lg
                                    
                                    python -m spacy download ja_core_news_sm
                                    
                                    pip spacy download ja_core_news_lg
                                    
                                    python -m spacy download ja_core_news_lg
                                    
                                    python -m spacy download ja_core_news_sm
                                    
                                    pip spacy download ja_core_news_lg
                                    
                                    python -m spacy download ja_core_news_lg
                                    
                                    python -m spacy download ja_core_news_sm
                                    
                                    python -m spacy download ja_core_news_sm
                                    
                                    import spacy
                                    nlp = spacy.load("ja_core_news_sm")
                                    
                                    import spacy
                                    from spacy.lang.ja.examples import sentences 
                                    
                                    nlp = spacy.load("ja_core_news_sm")
                                    doc = nlp(sentences[0])
                                    print(doc.text)
                                    for token in doc:
                                       print(token.text, token.pos_, token.dep_)
                                    
                                    python -m spacy download ja_core_news_sm
                                    
                                    import spacy
                                    nlp = spacy.load("ja_core_news_sm")
                                    
                                    import spacy
                                    from spacy.lang.ja.examples import sentences 
                                    
                                    nlp = spacy.load("ja_core_news_sm")
                                    doc = nlp(sentences[0])
                                    print(doc.text)
                                    for token in doc:
                                       print(token.text, token.pos_, token.dep_)
                                    
                                    python -m spacy download ja_core_news_sm
                                    
                                    import spacy
                                    nlp = spacy.load("ja_core_news_sm")
                                    
                                    import spacy
                                    from spacy.lang.ja.examples import sentences 
                                    
                                    nlp = spacy.load("ja_core_news_sm")
                                    doc = nlp(sentences[0])
                                    print(doc.text)
                                    for token in doc:
                                       print(token.text, token.pos_, token.dep_)
                                    

                                    Return all possible entity types from spaCy model?

                                    copy iconCopydownload iconDownload
                                    model = spacy.load("en_core_web_sm")
                                    list(model.__dict__['_meta']['accuracy']['ents_per_type'].keys())
                                    
                                    ['ORG', 'CARDINAL', 'DATE', 'GPE', 'PERSON', 'MONEY', 'PRODUCT', 'TIME', 'PERCENT', 'WORK_OF_ART', 'QUANTITY', 'NORP', 'LOC', 'EVENT', 'ORDINAL', 'FAC', 'LAW', 'LANGUAGE']
                                    
                                    import spacy
                                    nlp = spacy.load("en_core_web_sm")
                                    nlp.get_pipe("ner").labels
                                    

                                    Spacy: count occurrence for specific token in each sentence

                                    copy iconCopydownload iconDownload
                                    for sent in doc.sents:
                                        i = 0
                                        for token in sent:
                                            if token.text == "and":
                                                i += 1
                                        nb_and.append(i)
                                    
                                    import spacy
                                    nlp = spacy.load("en_core_web_trf")
                                    corpus = "I see a cat and a dog. None seems to be unhappy. My mother and I wanted to buy a parrot and a tortoise."
                                    doc = nlp(corpus)
                                    nb_and = []
                                    for sent in doc.sents:
                                        i = 0
                                        for token in sent:
                                            if token.text == "and":
                                                i += 1
                                        nb_and.append(i)
                                    
                                    nb_and
                                    # => [1, 0, 2]
                                    
                                    for sent in doc.sents:
                                        i = 0
                                        for token in sent:
                                            if token.text == "and":
                                                i += 1
                                        nb_and.append(i)
                                    
                                    import spacy
                                    nlp = spacy.load("en_core_web_trf")
                                    corpus = "I see a cat and a dog. None seems to be unhappy. My mother and I wanted to buy a parrot and a tortoise."
                                    doc = nlp(corpus)
                                    nb_and = []
                                    for sent in doc.sents:
                                        i = 0
                                        for token in sent:
                                            if token.text == "and":
                                                i += 1
                                        nb_and.append(i)
                                    
                                    nb_and
                                    # => [1, 0, 2]
                                    

                                    How to install Tesseract OCR on Databricks

                                    copy iconCopydownload iconDownload
                                    %sh apt-get -f -y install tesseract-ocr 
                                    

                                    How to use existing huggingface-transformers model into spacy?

                                    copy iconCopydownload iconDownload
                                    [components.transformer.model]
                                    @architectures = "spacy-transformers.TransformerModel.v3"
                                    # XXX You can change the model name here
                                    name = "bert-base-cased"
                                    tokenizer_config = {"use_fast": true}
                                    

                                    Community Discussions

                                    Trending Discussions on spaCy
                                    • Error while loading vector from Glove in Spacy
                                    • Save and load nlp results in spacy
                                    • How to match repeating patterns in spacy?
                                    • Spacy adds words automatically to vocab?
                                    • Spacy tokenization add extra white space for dates with hyphen separator when I manually build the Doc
                                    • After installing scrubadub_spacy package, spacy.load("en_core_web_sm") not working OSError: [E053] Could not read config.cfg
                                    • Show NER Spacy Data in dataframe
                                    • How to get a description for each Spacy NER entity?
                                    • Do I need to do any text cleaning for Spacy NER?
                                    • How to use japanese engine in Spacy
                                    Trending Discussions on spaCy

                                    QUESTION

                                    Error while loading vector from Glove in Spacy

                                    Asked 2022-Mar-17 at 16:39

                                    I am facing the following attribute error when loading glove model:

                                    Code used to load model:

                                    nlp = spacy.load('en_core_web_sm')
                                    tokenizer = spacy.load('en_core_web_sm', disable=['tagger','parser', 'ner', 'textcat'])
                                    nlp.vocab.vectors.from_glove('../models/GloVe')
                                    

                                    Getting the following atribute error when trying to load the glove model:

                                    AttributeError: 'spacy.vectors.Vectors' object has no attribute 'from_glove'
                                    

                                    Have tried to search on StackOverflow and elsewhere but can't seem to find the solution. Thanks!

                                    From pip list:

                                    • spacy version: 3.1.4
                                    • spacy-legacy 3.0.8
                                    • en-core-web-sm 3.1.0

                                    ANSWER

                                    Answered 2022-Mar-17 at 14:08

                                    spacy version: 3.1.4 does not have the feature from_glove.

                                    I was able to use nlp.vocab.vectors.from_glove() in spacy version: 2.2.4.

                                    If you want, you can change your spacy version by using:

                                    !pip install spacy==2.2.4 on your Jupyter cell.

                                    Source https://stackoverflow.com/questions/71512064

                                    Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                                    Vulnerabilities

                                    No vulnerabilities reported

                                    Install spaCy

                                    For detailed installation instructions, see the documentation.
                                    Operating system: macOS / OS X · Linux · Windows (Cygwin, MinGW, Visual Studio)
                                    Python version: Python 3.6+ (only 64 bit)
                                    Package managers: pip · conda (via conda-forge)
                                    Trained pipelines for spaCy can be installed as Python packages. This means that they're a component of your application, just like any other module. Models can be installed using spaCy's download command, or manually by pointing pip to a path or URL.

                                    Support

                                    New to spaCy? Here's everything you need to know!. How to use spaCy and its features. 🚀 New in v3.0. New features, backwards incompatibilities and migration guide. End-to-end workflows you can clone, modify and run. The detailed reference for spaCy's API. Download trained pipelines for spaCy. Plugins, extensions, demos and books from the spaCy ecosystem. Learn spaCy in this free and interactive online course. Our YouTube channel with video tutorials, talks and more. Changes and version history. How to contribute to the spaCy project and code base. Get a custom spaCy pipeline, tailor-made for your NLP problem by spaCy's core developers. Streamlined, production-ready, predictable and maintainable. Start by completing our 5-minute questionnaire to tell us what you need and we'll be in touch! Learn more →.

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