mirror of
https://github.com/explosion/spaCy.git
synced 2025-10-30 23:47:31 +03:00
431 lines
19 KiB
Plaintext
431 lines
19 KiB
Plaintext
//- 💫 DOCS > USAGE > SPACY 101
|
||
|
||
include ../../_includes/_mixins
|
||
|
||
p
|
||
| Whether you're new to spaCy, or just want to brush up on some
|
||
| NLP basics and implementation details – this page should have you covered.
|
||
| Each section will explain one of spaCy's features in simple terms and
|
||
| with examples or illustrations. Some sections will also reappear across
|
||
| the usage guides as a quick introcution.
|
||
|
||
+aside("Help us improve the docs")
|
||
| Did you spot a mistake or come across explanations that
|
||
| are unclear? We always appreciate improvement
|
||
| #[+a(gh("spaCy") + "/issues") suggestions] or
|
||
| #[+a(gh("spaCy") + "/pulls") pull requests]. You can find a "Suggest
|
||
| edits" link at the bottom of each page that points you to the source.
|
||
|
||
+h(2, "whats-spacy") What's spaCy?
|
||
|
||
+grid.o-no-block
|
||
+grid-col("half")
|
||
p
|
||
| spaCy is a #[strong free, open-source library] for advanced
|
||
| #[strong Natural Language Processing] (NLP) in Python.
|
||
|
||
p
|
||
| If you're working with a lot of text, you'll eventually want to
|
||
| know more about it. For example, what's it about? What do the
|
||
| words mean in context? Who is doing what to whom? What companies
|
||
| and products are mentioned? Which texts are similar to each other?
|
||
|
||
p
|
||
| spaCy is designed specifically for #[strong production use] and
|
||
| helps you build applications that process and "understand"
|
||
| large volumes of text. It can be used to build
|
||
| #[strong information extraction] or
|
||
| #[strong natural language understanding] systems, or to
|
||
| pre-process text for #[strong deep learning].
|
||
|
||
+table-of-contents
|
||
+item #[+a("#features") Features]
|
||
+item #[+a("#annotations") Linguistic annotations]
|
||
+item #[+a("#annotations-token") Tokenization]
|
||
+item #[+a("#annotations-pos-deps") POS tags and dependencies]
|
||
+item #[+a("#annotations-ner") Named entities]
|
||
+item #[+a("#vectors-similarity") Word vectors and similarity]
|
||
+item #[+a("#pipelines") Pipelines]
|
||
+item #[+a("#vocab") Vocab, hashes and lexemes]
|
||
+item #[+a("#serialization") Serialization]
|
||
+item #[+a("#training") Training]
|
||
+item #[+a("#language-data") Language data]
|
||
+item #[+a("#architecture") Architecture]
|
||
+item #[+a("#community") Community & FAQ]
|
||
|
||
+h(3, "what-spacy-isnt") What spaCy isn't
|
||
|
||
+list
|
||
+item #[strong spaCy is not a platform or "an API"].
|
||
| Unlike a platform, spaCy does not provide a software as a service, or
|
||
| a web application. It's an open-source library designed to help you
|
||
| build NLP applications, not a consumable service.
|
||
+item #[strong spaCy is not an out-of-the-box chat bot engine].
|
||
| While spaCy can be used to power conversational applications, it's
|
||
| not designed specifically for chat bots, and only provides the
|
||
| underlying text processing capabilities.
|
||
+item #[strong spaCy is not research software].
|
||
| It's built on the latest research, but it's designed to get
|
||
| things done. This leads to fairly different design decisions than
|
||
| #[+a("https://github./nltk/nltk") NLTK]
|
||
| or #[+a("https://stanfordnlp.github.io/CoreNLP/") CoreNLP], which were
|
||
| created as platforms for teaching and research. The main difference
|
||
| is that spaCy is integrated and opinionated. spaCy tries to avoid asking
|
||
| the user to choose between multiple algorithms that deliver equivalent
|
||
| functionality. Keeping the menu small lets spaCy deliver generally better
|
||
| performance and developer experience.
|
||
+item #[strong spaCy is not a company].
|
||
| It's an open-source library. Our company publishing spaCy and other
|
||
| software is called #[+a(COMPANY_URL, true) Explosion AI].
|
||
|
||
+h(2, "features") Features
|
||
|
||
p
|
||
| In the documentation, you'll come across mentions of spaCy's
|
||
| features and capabilities. Some of them refer to linguistic concepts,
|
||
| while others are related to more general machine learning functionality.
|
||
|
||
+aside
|
||
| If one of spaCy's functionalities #[strong needs a model], it means that
|
||
| you need to have one of the available
|
||
| #[+a("/docs/usage/models") statistical models] installed. Models are used
|
||
| to #[strong predict] linguistic annotations – for example, if a word is
|
||
| a verb or a noun.
|
||
|
||
+table(["Name", "Description", "Needs model"])
|
||
+row
|
||
+cell #[strong Tokenization]
|
||
+cell Segmenting text into words, punctuations marks etc.
|
||
+cell #[+procon("con")]
|
||
|
||
+row
|
||
+cell #[strong Part-of-speech] (POS) #[strong Tagging]
|
||
+cell Assigning word types to tokens, like verb or noun.
|
||
+cell #[+procon("pro")]
|
||
|
||
+row
|
||
+cell #[strong Dependency Parsing]
|
||
+cell
|
||
| Assigning syntactic dependency labels, describing the relations
|
||
| between individual tokens, like subject or object.
|
||
+cell #[+procon("pro")]
|
||
|
||
+row
|
||
+cell #[strong Lemmatization]
|
||
+cell
|
||
| Assigning the base forms of words. For example, the lemma of
|
||
| "was" is "be", and the lemma of "rats" is "rat".
|
||
+cell #[+procon("pro")]
|
||
|
||
+row
|
||
+cell #[strong Sentence Boundary Detection] (SBD)
|
||
+cell Finding and segmenting individual sentences.
|
||
+cell #[+procon("pro")]
|
||
|
||
+row
|
||
+cell #[strong Named Entity Recongition] (NER)
|
||
+cell
|
||
| Labelling named "real-world" objects, like persons, companies or
|
||
| locations.
|
||
+cell #[+procon("pro")]
|
||
|
||
+row
|
||
+cell #[strong Similarity]
|
||
+cell
|
||
| Comparing words, text spans and documents and how similar they
|
||
| are to each other.
|
||
+cell #[+procon("pro")]
|
||
|
||
+row
|
||
+cell #[strong Text classification]
|
||
+cell Assigning categories or labels to a whole document, or parts of a document.
|
||
+cell #[+procon("pro")]
|
||
|
||
+row
|
||
+cell #[strong Rule-based Matching]
|
||
+cell
|
||
| Finding sequences of tokens based on their texts and linguistic
|
||
| annotations, similar to regular expressions.
|
||
+cell #[+procon("con")]
|
||
|
||
+row
|
||
+cell #[strong Training]
|
||
+cell Updating and improving a statistical model's predictions.
|
||
+cell #[+procon("neutral")]
|
||
|
||
+row
|
||
+cell #[strong Serialization]
|
||
+cell Saving objects to files or byte strings.
|
||
+cell #[+procon("neutral")]
|
||
|
||
+h(2, "annotations") Linguistic annotations
|
||
|
||
p
|
||
| spaCy provides a variety of linguistic annotations to give you
|
||
| #[strong insights into a text's grammatical structure]. This includes the
|
||
| word types, like the parts of speech, and how the words are related to
|
||
| each other. For example, if you're analysing text, it makes a huge
|
||
| difference whether a noun is the subject of a sentence, or the object –
|
||
| or whether "google" is used as a verb, or refers to the website or
|
||
| company in a specific context.
|
||
|
||
p
|
||
| Once you've downloaded and installed a #[+a("/docs/usage/models") model],
|
||
| you can load it via #[+api("spacy#load") #[code spacy.load()]]. This will
|
||
| return a #[code Language] object contaning all components and data needed
|
||
| to process text. We usually call it #[code nlp]. Calling the #[code nlp]
|
||
| object on a string of text will return a processed #[code Doc]:
|
||
|
||
+code.
|
||
import spacy
|
||
|
||
nlp = spacy.load('en')
|
||
doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
|
||
|
||
p
|
||
| Even though a #[code Doc] is processed – e.g. split into individual words
|
||
| and annotated – it still holds #[strong all information of the original text],
|
||
| like whitespace characters. You can always get the offset of a token into the
|
||
| original string, or reconstruct the original by joining the tokens and their
|
||
| trailing whitespace. This way, you'll never lose any information
|
||
| when processing text with spaCy.
|
||
|
||
+h(3, "annotations-token") Tokenization
|
||
|
||
include _spacy-101/_tokenization
|
||
|
||
+infobox
|
||
| To learn more about how spaCy's tokenization rules work in detail,
|
||
| how to #[strong customise and replace] the default tokenizer and how to
|
||
| #[strong add language-specific data], see the usage guides on
|
||
| #[+a("/docs/usage/adding-languages") adding languages] and
|
||
| #[+a("/docs/usage/customizing-tokenizer") customising the tokenizer].
|
||
|
||
+h(3, "annotations-pos-deps") Part-of-speech tags and dependencies
|
||
+tag-model("dependency parse")
|
||
|
||
include _spacy-101/_pos-deps
|
||
|
||
+infobox
|
||
| To learn more about #[strong part-of-speech tagging] and rule-based
|
||
| morphology, and how to #[strong navigate and use the parse tree]
|
||
| effectively, see the usage guides on
|
||
| #[+a("/docs/usage/pos-tagging") part-of-speech tagging] and
|
||
| #[+a("/docs/usage/dependency-parse") using the dependency parse].
|
||
|
||
+h(3, "annotations-ner") Named Entities
|
||
+tag-model("named entities")
|
||
|
||
include _spacy-101/_named-entities
|
||
|
||
+infobox
|
||
| To learn more about entity recognition in spaCy, how to
|
||
| #[strong add your own entities] to a document and how to
|
||
| #[strong train and update] the entity predictions of a model, see the
|
||
| usage guides on
|
||
| #[+a("/docs/usage/entity-recognition") named entity recognition] and
|
||
| #[+a("/docs/usage/training-ner") training the named entity recognizer].
|
||
|
||
+h(2, "vectors-similarity") Word vectors and similarity
|
||
+tag-model("vectors")
|
||
|
||
include _spacy-101/_similarity
|
||
|
||
include _spacy-101/_word-vectors
|
||
|
||
+infobox
|
||
| To learn more about word vectors, how to #[strong customise them] and
|
||
| how to load #[strong your own vectors] into spaCy, see the usage
|
||
| guide on
|
||
| #[+a("/docs/usage/word-vectors-similarities") using word vectors and semantic similarities].
|
||
|
||
+h(2, "pipelines") Pipelines
|
||
|
||
include _spacy-101/_pipelines
|
||
|
||
+infobox
|
||
| To learn more about #[strong how processing pipelines work] in detail,
|
||
| how to enable and disable their components, and how to
|
||
| #[strong create your own], see the usage guide on
|
||
| #[+a("/docs/usage/language-processing-pipeline") language processing pipelines].
|
||
|
||
+h(2, "vocab") Vocab, hashes and lexemes
|
||
|
||
include _spacy-101/_vocab
|
||
|
||
+h(2, "serialization") Serialization
|
||
|
||
include _spacy-101/_serialization
|
||
|
||
+infobox
|
||
| To learn more about #[strong serialization] and how to
|
||
| #[strong save and load your own models], see the usage guide on
|
||
| #[+a("/docs/usage/saving-loading") saving, loading and data serialization].
|
||
|
||
+h(2, "training") Training
|
||
|
||
include _spacy-101/_training
|
||
|
||
+infobox
|
||
| To learn more about #[strong training and updating] models, how to create
|
||
| training data and how to improve spaCy's named entity recognition models,
|
||
| see the usage guides on #[+a("/docs/usage/training") training] and
|
||
| #[+a("/docs/usage/training-ner") training the named entity recognizer].
|
||
|
||
+h(2, "language-data") Language data
|
||
|
||
include _spacy-101/_language-data
|
||
|
||
+infobox
|
||
| To learn more about the individual components of the language data and
|
||
| how to #[strong add a new language] to spaCy in preparation for training
|
||
| a language model, see the usage guide on
|
||
| #[+a("/docs/usage/adding-languages") adding languages].
|
||
|
||
+h(2, "architecture") Architecture
|
||
|
||
include _spacy-101/_architecture.jade
|
||
|
||
+h(2, "community") Community & FAQ
|
||
|
||
p
|
||
| We're very happy to see the spaCy community grow and include a mix of
|
||
| people from all kinds of different backgrounds – computational
|
||
| linguistics, data science, deep learning, research and more. If you'd
|
||
| like to get involved, below are some answers to the most important
|
||
| questions and resources for further reading.
|
||
|
||
+h(3, "faq-help-code") Help, my code isn't working!
|
||
|
||
p
|
||
| Bugs suck, and we're doing our best to continuously improve the tests
|
||
| and fix bugs as soon as possible. Before you submit an issue, do a
|
||
| quick search and check if the problem has already been reported. If
|
||
| you're having installation or loading problems, make sure to also check
|
||
| out the #[+a("/docs/usage#troubleshooting") troubleshooting guide]. Help
|
||
| with spaCy is available via the following platforms:
|
||
|
||
+aside("How do I know if something is a bug?")
|
||
| Of course, it's always hard to know for sure, so don't worry – we're not
|
||
| going to be mad if a bug report turns out to be a typo in your
|
||
| code. As a simple rule, any C-level error without a Python traceback,
|
||
| like a #[strong segmentation fault] or #[strong memory error],
|
||
| is #[strong always] a spaCy bug.#[br]#[br]
|
||
|
||
| Because models are statistical, their performance will never be
|
||
| #[em perfect]. However, if you come across
|
||
| #[strong patterns that might indicate an underlying issue], please do
|
||
| file a report. Similarly, we also care about behaviours that
|
||
| #[strong contradict our docs].
|
||
|
||
+table(["Platform", "Purpose"])
|
||
+row
|
||
+cell #[+a("https://stackoverflow.com/questions/tagged/spacy") StackOverflow]
|
||
+cell
|
||
| #[strong Usage questions] and everything related to problems with
|
||
| your specific code. The StackOverflow community is much larger
|
||
| than ours, so if your problem can be solved by others, you'll
|
||
| receive help much quicker.
|
||
|
||
+row
|
||
+cell #[+a("https://gitter.im/" + SOCIAL.gitter) Gitter chat]
|
||
+cell
|
||
| #[strong General discussion] about spaCy, meeting other community
|
||
| members and exchanging #[strong tips, tricks and best practices].
|
||
| If we're working on experimental models and features, we usually
|
||
| share them on Gitter first.
|
||
|
||
+row
|
||
+cell #[+a(gh("spaCy") + "/issues") GitHub issue tracker]
|
||
+cell
|
||
| #[strong Bug reports] and #[strong improvement suggestions], i.e.
|
||
| everything that's likely spaCy's fault. This also includes
|
||
| problems with the models beyond statistical imprecisions, like
|
||
| patterns that point to a bug.
|
||
|
||
+infobox
|
||
| Please understand that we won't be able to provide individual support via
|
||
| email. We also believe that help is much more valuable if it's shared
|
||
| publicly, so that #[strong more people can benefit from it]. If you come
|
||
| across an issue and you think you might be able to help, consider posting
|
||
| a quick update with your solution. No matter how simple, it can easily
|
||
| save someone a lot of time and headache – and the next time you need help,
|
||
| they might repay the favour.
|
||
|
||
+h(3, "faq-contributing") How can I contribute to spaCy?
|
||
|
||
p
|
||
| You don't have to be an NLP expert or Python pro to contribute, and we're
|
||
| happy to help you get started. If you're new to spaCy, a good place to
|
||
| start is the
|
||
| #[+a(gh("spaCy") + '/issues?q=is%3Aissue+is%3Aopen+label%3A"help+wanted+%28easy%29"') #[code help wanted (easy)] label]
|
||
| on GitHub, which we use to tag bugs and feature requests that are easy
|
||
| and self-contained. We also appreciate contributions to the docs – whether
|
||
| it's fixing a typo, improving an example or adding additional explanations.
|
||
| You'll find a "Suggest edits" link at the bottom of each page that points
|
||
| you to the source.
|
||
|
||
p
|
||
| Another way of getting involved is to help us improve the
|
||
| #[+a("/docs/usage/adding-languages#language-data") language data] –
|
||
| especially if you happen to speak one of the languages currently in
|
||
| #[+a("/docs/api/language-models#alpha-support") alpha support]. Even
|
||
| adding simple tokenizer exceptions, stop words or lemmatizer data
|
||
| can make a big difference. It will also make it easier for us to provide
|
||
| a statistical model for the language in the future. Submitting a test
|
||
| that documents a bug or performance issue, or covers functionality that's
|
||
| especially important for your application is also very helpful. This way,
|
||
| you'll also make sure we never accidentally introduce regressions to the
|
||
| parts of the library that you care about the most.
|
||
|
||
p
|
||
strong
|
||
| For more details on the types of contributions we're looking for, the
|
||
| code conventions and other useful tips, make sure to check out the
|
||
| #[+a(gh("spaCy", "CONTRIBUTING.md")) contributing guidelines].
|
||
|
||
+infobox("Code of Conduct")
|
||
| spaCy adheres to the
|
||
| #[+a("http://contributor-covenant.org/version/1/4/") Contributor Covenant Code of Conduct].
|
||
| By participating, you are expected to uphold this code.
|
||
|
||
+h(3, "faq-project-with-spacy")
|
||
| I've built something cool with spaCy – how can I get the word out?
|
||
|
||
p
|
||
| First, congrats – we'd love to check it out! When you share your
|
||
| project on Twitter, don't forget to tag
|
||
| #[+a("https://twitter.com/" + SOCIAL.twitter) @#{SOCIAL.twitter}] so we
|
||
| don't miss it. If you think your project would be a good fit for the
|
||
| #[+a("/docs/usage/showcase") showcase], #[strong feel free to submit it!]
|
||
| Tutorials are also incredibly valuable to other users and a great way to
|
||
| get exposure. So we strongly encourage #[strong writing up your experiences],
|
||
| or sharing your code and some tips and tricks on your blog. Since our
|
||
| website is open-source, you can add your project or tutorial by making a
|
||
| pull request on GitHub.
|
||
|
||
+aside("Contributing to spacy.io")
|
||
| All showcase and tutorial links are stored in a
|
||
| #[+a(gh("spaCy", "website/docs/usage/_data.json")) JSON file], so you
|
||
| won't even have to edit any markup. For more info on how to submit
|
||
| your project, see the
|
||
| #[+a(gh("spaCy", "CONTRIBUTING.md#submitting-a-project-to-the-showcase")) contributing guidelines]
|
||
| and our #[+a(gh("spaCy", "website")) website docs].
|
||
|
||
p
|
||
| If you would like to use the spaCy logo on your site, please get in touch
|
||
| and ask us first. However, if you want to show support and tell others
|
||
| that your project is using spaCy, you can grab one of our
|
||
| #[strong spaCy badges] here:
|
||
|
||
- SPACY_BADGES = ["built%20with-spaCy-09a3d5.svg", "made%20with%20❤%20and-spaCy-09a3d5.svg", "spaCy-v2-09a3d5.svg"]
|
||
+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)
|
||
.c-code-block(data-qs-results)
|
||
for badge, i in SPACY_BADGES
|
||
- var url = "https://img.shields.io/badge/" + badge
|
||
+code(false, "text", "star").o-no-block(data-qs-badge=i)=url
|
||
+code(false, "text", "code").o-no-block(data-qs-badge=i).
|
||
<a href="#{SITE_URL}"><img src="#{url}" height="20"></a>
|
||
+code(false, "text", "markdown").o-no-block(data-qs-badge=i).
|
||
[](#{SITE_URL})
|