mirror of https://github.com/explosion/spaCy.git
426 lines
19 KiB
Plaintext
426 lines
19 KiB
Plaintext
//- 💫 DOCS > USAGE > SPACY 101
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include ../../_includes/_mixins
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p
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| Whether you're new to spaCy, or just want to brush up on some
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| NLP basics and implementation details – this page should have you covered.
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| Each section will explain one of spaCy's features in simple terms and
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| with examples or illustrations. Some sections will also reappear across
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| the usage guides as a quick introcution.
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+aside("Help us improve the docs")
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| Did you spot a mistake or come across explanations that
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| are unclear? We always appreciate improvement
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| #[+a(gh("spaCy") + "/issues") suggestions] or
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| #[+a(gh("spaCy") + "/pulls") pull requests]. You can find a "Suggest
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| edits" link at the bottom of each page that points you to the source.
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+h(2, "whats-spacy") What's spaCy?
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+grid.o-no-block
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+grid-col("half")
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p
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| spaCy is a #[strong free, open-source library] for advanced
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| #[strong Natural Language Processing] (NLP) in Python.
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p
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| If you're working with a lot of text, you'll eventually want to
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| know more about it. For example, what's it about? What do the
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| words mean in context? Who is doing what to whom? What companies
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| and products are mentioned? Which texts are similar to each other?
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p
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| spaCy is designed specifically for #[strong production use] and
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| helps you build applications that process and "understand"
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| large volumes of text. It can be used to build
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| #[strong information extraction] or
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| #[strong natural language understanding] systems, or to
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| pre-process text for #[strong deep learning].
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+table-of-contents
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+item #[+a("#features") Features]
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+item #[+a("#annotations") Linguistic annotations]
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+item #[+a("#annotations-token") Tokenization]
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+item #[+a("#annotations-pos-deps") POS tags and dependencies]
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+item #[+a("#annotations-ner") Named entities]
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+item #[+a("#vectors-similarity") Word vectors and similarity]
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+item #[+a("#pipelines") Pipelines]
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+item #[+a("#vocab") Vocab, hashes and lexemes]
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+item #[+a("#serialization") Serialization]
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+item #[+a("#training") Training]
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+item #[+a("#language-data") Language data]
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+item #[+a("#architecture") Architecture]
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+item #[+a("#community") Community & FAQ]
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+h(3, "what-spacy-isnt") What spaCy isn't
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+list
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+item #[strong spaCy is not a platform or "an API"].
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| Unlike a platform, spaCy does not provide a software as a service, or
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| a web application. It's an open-source library designed to help you
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| build NLP applications, not a consumable service.
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+item #[strong spaCy is not an out-of-the-box chat bot engine].
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| While spaCy can be used to power conversational applications, it's
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| not designed specifically for chat bots, and only provides the
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| underlying text processing capabilities.
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+item #[strong spaCy is not research software].
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| It's is built on the latest research, but it's designed to get
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| things done. This leads to fairly different design decisions than
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| #[+a("https://github./nltk/nltk") NLTK]
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| or #[+a("https://stanfordnlp.github.io/CoreNLP/") CoreNLP], which were
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| created as platforms for teaching and research. The main difference
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| is that spaCy is integrated and opinionated. spaCy tries to avoid asking
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| the user to choose between multiple algorithms that deliver equivalent
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| functionality. Keeping the menu small lets spaCy deliver generally better
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| performance and developer experience.
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+item #[strong spaCy is not a company].
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| It's an open-source library. Our company publishing spaCy and other
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| software is called #[+a(COMPANY_URL, true) Explosion AI].
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+h(2, "features") Features
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p
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| In the documentation, you'll come across mentions of spaCy's
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| features and capabilities. Some of them refer to linguistic concepts,
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| while others are related to more general machine learning functionality.
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+aside
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| If one of spaCy's functionalities #[strong needs a model], it means that
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| you need to have one our the available
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| #[+a("/docs/usage/models") statistical models] installed. Models are used
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| to #[strong predict] linguistic annotations – for example, if a word is
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| a verb or a noun.
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+table(["Name", "Description", "Needs model"])
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+row
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+cell #[strong Tokenization]
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+cell Segmenting text into words, punctuations marks etc.
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+cell #[+procon("con")]
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+row
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+cell #[strong Part-of-speech] (POS) #[strong Tagging]
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+cell Assigning word types to tokens, like verb or noun.
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+cell #[+procon("pro")]
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+row
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+cell #[strong Dependency Parsing]
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+cell
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| Assigning syntactic dependency labels, describing the relations
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| between individual tokens, like subject or object.
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+cell #[+procon("pro")]
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+row
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+cell #[strong Lemmatization]
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+cell
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| Assigning the base forms of words. For example, the lemma of
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| "was" is "be", and the lemma of "rats" is "rat".
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+cell #[+procon("pro")]
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+row
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+cell #[strong Sentence Boundary Detection] (SBD)
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+cell Finding and segmenting individual sentences.
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+cell #[+procon("pro")]
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+row
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+cell #[strong Named Entity Recongition] (NER)
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+cell
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| Labelling named "real-world" objects, like persons, companies or
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| locations.
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+cell #[+procon("pro")]
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+row
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+cell #[strong Rule-based Matching]
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+cell
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| Finding sequences of tokens based on their texts and linguistic
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| annotations, similar to regular expressions.
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+cell #[+procon("con")]
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+row
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+cell #[strong Similarity]
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+cell
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| Comparing words, text spans and documents and how similar they
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| are to each other.
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+cell #[+procon("pro")]
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+row
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+cell #[strong Training]
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+cell Updating and improving a statistical model's predictions.
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+cell #[+procon("neutral")]
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+row
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+cell #[strong Serialization]
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+cell Saving objects to files or byte strings.
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+cell #[+procon("neutral")]
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+h(2, "annotations") Linguistic annotations
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p
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| spaCy provides a variety of linguistic annotations to give you
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| #[strong insights into a text's grammatical structure]. This includes the
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| word types, like the parts of speech, and how the words are related to
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| each other. For example, if you're analysing text, it makes a huge
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| difference whether a noun is the subject of a sentence, or the object –
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| or whether "google" is used as a verb, or refers to the website or
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| company in a specific context.
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p
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| Once you've downloaded and installed a #[+a("/docs/usage/models") model],
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| you can load it via #[+api("spacy#load") #[code spacy.load()]]. This will
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| return a #[code Language] object contaning all components and data needed
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| to process text. We usually call it #[code nlp]. Calling the #[code nlp]
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| object on a string of text will return a processed #[code Doc]:
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+code.
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import spacy
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nlp = spacy.load('en')
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doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
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p
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| Even though a #[code Doc] is processed – e.g. split into individual words
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| and annotated – it still holds #[strong all information of the original text],
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| like whitespace characters. You can always get the offset of a token into the
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| original string, or reconstruct the original by joining the tokens and their
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| trailing whitespace. This way, you'll never lose any information
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| when processing text with spaCy.
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+h(3, "annotations-token") Tokenization
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include _spacy-101/_tokenization
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+infobox
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| To learn more about how spaCy's tokenization rules work in detail,
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| how to #[strong customise and replace] the default tokenizer and how to
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| #[strong add language-specific data], see the usage guides on
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| #[+a("/docs/usage/adding-languages") adding languages] and
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| #[+a("/docs/usage/customizing-tokenizer") customising the tokenizer].
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+h(3, "annotations-pos-deps") Part-of-speech tags and dependencies
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+tag-model("dependency parse")
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include _spacy-101/_pos-deps
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+infobox
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| To learn more about #[strong part-of-speech tagging] and rule-based
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| morphology, and how to #[strong navigate and use the parse tree]
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| effectively, see the usage guides on
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| #[+a("/docs/usage/pos-tagging") part-of-speech tagging] and
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| #[+a("/docs/usage/dependency-parse") using the dependency parse].
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+h(3, "annotations-ner") Named Entities
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+tag-model("named entities")
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include _spacy-101/_named-entities
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+infobox
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| To learn more about entity recognition in spaCy, how to
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| #[strong add your own entities] to a document and how to
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| #[strong train and update] the entity predictions of a model, see the
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| usage guides on
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| #[+a("/docs/usage/entity-recognition") named entity recognition] and
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| #[+a("/docs/usage/training-ner") training the named entity recognizer].
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+h(2, "vectors-similarity") Word vectors and similarity
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+tag-model("vectors")
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include _spacy-101/_similarity
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include _spacy-101/_word-vectors
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+infobox
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| To learn more about word vectors, how to #[strong customise them] and
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| how to load #[strong your own vectors] into spaCy, see the usage
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| guide on
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| #[+a("/docs/usage/word-vectors-similarities") using word vectors and semantic similarities].
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+h(2, "pipelines") Pipelines
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include _spacy-101/_pipelines
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+infobox
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| To learn more about #[strong how processing pipelines work] in detail,
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| how to enable and disable their components, and how to
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| #[strong create your own], see the usage guide on
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| #[+a("/docs/usage/language-processing-pipeline") language processing pipelines].
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+h(2, "vocab") Vocab, hashes and lexemes
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include _spacy-101/_vocab
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+h(2, "serialization") Serialization
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include _spacy-101/_serialization
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+infobox
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| To learn more about #[strong serialization] and how to
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| #[strong save and load your own models], see the usage guide on
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| #[+a("/docs/usage/saving-loading") saving, loading and data serialization].
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+h(2, "training") Training
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include _spacy-101/_training
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+infobox
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| To learn more about #[strong training and updating] models, how to create
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| training data and how to improve spaCy's named entity recognition models,
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| see the usage guides on #[+a("/docs/usage/training") training] and
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| #[+a("/docs/usage/training-ner") training the named entity recognizer].
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+h(2, "language-data") Language data
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include _spacy-101/_language-data
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+infobox
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| To learn more about the individual components of the language data and
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| how to #[strong add a new language] to spaCy in preparation for training
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| a language model, see the usage guide on
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| #[+a("/docs/usage/adding-languages") adding languages].
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+h(2, "architecture") Architecture
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include _spacy-101/_architecture.jade
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+h(2, "community") Community & FAQ
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p
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| We're very happy to see the spaCy community grow and include a mix of
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| people from all kinds of different backgrounds – computational
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| linguistics, data science, deep learning, research and more. If you'd
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| like to get involved, below are some answers to the most important
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| questions and resources for further reading.
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+h(3, "faq-help-code") Help, my code isn't working!
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p
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| Bugs suck, and we're doing our best to continuously improve the tests
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| and fix bugs as soon as possible. Before you submit an issue, do a
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| quick search and check if the problem has already been reported. If
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| you're having installation or loading problems, make sure to also check
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| out the #[+a("/docs/usage#troubleshooting") troubleshooting guide]. Help
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| with spaCy is available via the following platforms:
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+aside("How do I know if something is a bug?")
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| Of course, it's always hard to know for sure, so don't worry – we're not
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| going to be mad if a bug report turns out to be a typo in your
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| code. As a simple rule, any C-level error without a Python traceback,
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| like a #[strong segmentation fault] or #[strong memory error],
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| is #[strong always] a spaCy bug.#[br]#[br]
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| Because models are statistical, their performance will never be
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| #[em perfect]. However, if you come across
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| #[strong patterns that might indicate an underlying issue], please do
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| file a report. Similarly, we also care about behaviours that
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| #[strong contradict our docs].
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+table(["Platform", "Purpose"])
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+row
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+cell #[+a("https://stackoverflow.com/questions/tagged/spacy") StackOverflow]
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+cell
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| #[strong Usage questions] and everything related to problems with
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| your specific code. The StackOverflow community is much larger
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| than ours, so if your problem can be solved by others, you'll
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| receive help much quicker.
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+row
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+cell #[+a("https://gitter.im/" + SOCIAL.gitter) Gitter chat]
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+cell
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| #[strong General discussion] about spaCy, meeting other community
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| members and exchanging #[strong tips, tricks and best practices].
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| If we're working on experimental models and features, we usually
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| share them on Gitter first.
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+row
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+cell #[+a(gh("spaCy") + "/issues") GitHub issue tracker]
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+cell
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| #[strong Bug reports] and #[strong improvement suggestions], i.e.
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| everything that's likely spaCy's fault. This also includes
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| problems with the models beyond statistical imprecisions, like
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| patterns that point to a bug.
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+infobox
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| Please understand that we won't be able to provide individual support via
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| email. We also believe that help is much more valuable if it's shared
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| publicly, so that #[strong more people can benefit from it]. If you come
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| across an issue and you think you might be able to help, consider posting
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| a quick update with your solution. No matter how simple, it can easily
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| save someone a lot of time and headache – and the next time you need help,
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| they might repay the favour.
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+h(3, "faq-contributing") How can I contribute to spaCy?
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p
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| You don't have to be an NLP expert or Python pro to contribute, and we're
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| happy to help you get started. If you're new to spaCy, a good place to
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| start is the
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| #[+a(gh("spaCy") + '/issues?q=is%3Aissue+is%3Aopen+label%3A"help+wanted+%28easy%29"') #[code help wanted (easy)] label]
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| on GitHub, which we use to tag bugs and feature requests that are easy
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| and self-contained. We also appreciate contributions to the docs – whether
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| it's fixing a typo, improving an example or adding additional explanations.
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| You'll find a "Suggest edits" link at the bottom of each page that points
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| you to the source.
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p
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| Another way of getting involved is to help us improve the
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| #[+a("/docs/usage/adding-languages#language-data") language data] –
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| especially if you happen to speak one of the languages currently in
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| #[+a("/docs/api/language-models#alpha-support") alpha support]. Even
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| adding simple tokenizer exceptions, stop words or lemmatizer data
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| can make a big difference. It will also make it easier for us to provide
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| a statistical model for the language in the future. Submitting a test
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| that documents a bug or performance issue, or covers functionality that's
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| especially important for your application is also very helpful. This way,
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| you'll also make sure we never accidentally introduce regressions to the
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| parts of the library that you care about the most.
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p
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strong
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| For more details on the types of contributions we're looking for, the
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| code conventions and other useful tips, make sure to check out the
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| #[+a(gh("spaCy", "CONTRIBUTING.md")) contributing guidelines].
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+infobox("Code of Conduct")
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| spaCy adheres to the
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| #[+a("http://contributor-covenant.org/version/1/4/") Contributor Covenant Code of Conduct].
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| By participating, you are expected to uphold this code.
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+h(3, "faq-project-with-spacy")
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| I've built something cool with spaCy – how can I get the word out?
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p
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| First, congrats – we'd love to check it out! When you share your
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| project on Twitter, don't forget to tag
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| #[+a("https://twitter.com/" + SOCIAL.twitter) @#{SOCIAL.twitter}] so we
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| don't miss it. If you think your project would be a good fit for the
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| #[+a("/docs/usage/showcase") showcase], #[strong feel free to submit it!]
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| Tutorials are also incredibly valuable to other users and a great way to
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| get exposure. So we strongly encourage #[strong writing up your experiences],
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| or sharing your code and some tips and tricks on your blog. Since our
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| website is open-source, you can add your project or tutorial by making a
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| pull request on GitHub.
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+aside("Contributing to spacy.io")
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| All showcase and tutorial links are stored in a
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| #[+a(gh("spaCy", "website/docs/usage/_data.json")) JSON file], so you
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| won't even have to edit any markup. For more info on how to submit
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| your project, see the
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| #[+a(gh("spaCy", "CONTRIBUTING.md#submitting-a-project-to-the-showcase")) contributing guidelines]
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| and our #[+a(gh("spaCy", "website")) website docs].
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p
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| If you would like to use the spaCy logo on your site, please get in touch
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| and ask us first. However, if you want to show support and tell others
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| that your project is using spaCy, you can grab one of our
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| #[strong spaCy badges] here:
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- SPACY_BADGES = ["built%20with-spaCy-09a3d5.svg", "made%20with%20❤%20and-spaCy-09a3d5.svg", "spaCy-v2-09a3d5.svg"]
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+quickstart([{id: "badge", input_style: "check", options: SPACY_BADGES.map(function(badge, i) { return {id: i, title: "<img class='o-icon' src='https://img.shields.io/badge/" + badge + "' height='20'/>", checked: (i == 0) ? true : false}}) }], false, false, true)
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.c-code-block(data-qs-results)
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for badge, i in SPACY_BADGES
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- var url = "https://img.shields.io/badge/" + badge
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+code(false, "text", "star").o-no-block(data-qs-badge=i)=url
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+code(false, "text", "code").o-no-block(data-qs-badge=i).
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<a href="#{SITE_URL}"><img src="#{url}" height="20"></a>
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+code(false, "text", "markdown").o-no-block(data-qs-badge=i).
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[](#{SITE_URL})
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