//- 💫 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 is 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 our 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: "", 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). [![spaCy](#{url})](#{SITE_URL})