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Always compare lowercase package names
Otherwise, is_package will return False if model name contains uppercase characters. See this issue: https://support.prodi.gy/t/saving-a-trained-ner-model-as-a-loadable-modu le/46/6
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ines
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@ -181,9 +181,10 @@ def is_package(name):
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name (unicode): Name of package.
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RETURNS (bool): True if installed package, False if not.
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"""
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name = name.lower() # compare package name against lowercase name
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packages = pkg_resources.working_set.by_key.keys()
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for package in packages:
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if package.replace('-', '_') == name:
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if package.lower().replace('-', '_') == name:
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return True
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return False
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@ -194,6 +195,7 @@ def get_package_path(name):
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name (unicode): Package name.
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RETURNS (Path): Path to installed package.
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"""
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name = name.lower() # use lowercase version to be safe
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# Here we're importing the module just to find it. This is worryingly
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# indirect, but it's otherwise very difficult to find the package.
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pkg = importlib.import_module(name)
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300
website/usage/spacy-101.jade
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300
website/usage/spacy-101.jade
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@ -0,0 +1,300 @@
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//- 💫 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 introduction.
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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("#lightning-tour") Lightning tour]
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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 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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+section("features")
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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
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| functionality.
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+aside
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| If one of spaCy's functionalities #[strong needs a model], it means
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| that you need to have one of the available
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| #[+a("/models") statistical models] installed. Models are used
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| to #[strong predict] linguistic annotations – for example, if a word
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| is 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
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| relations 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
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| or locations.
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+cell #[+procon("pro")]
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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
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| they are to each other.
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+cell #[+procon("pro")]
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+row
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+cell #[strong Text Classification]
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+cell
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| Assigning categories or labels to a whole document, or parts
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| of a document.
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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
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| linguistic annotations, similar to regular expressions.
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+cell #[+procon("con")]
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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
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| includes the word types, like the parts of speech, and how the words
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| are related to each other. For example, if you're analysing text, it
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| makes a huge difference whether a noun is the subject of a sentence,
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| or the object – or whether "google" is used as a verb, or refers to
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| the website or company in a specific context.
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p
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| Once you've downloaded and installed a #[+a("/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("/usage/adding-languages") adding languages] and
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| #[+a("/usage/linguistic-features#tokenization") 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("/usage/linguistic-features#pos-tagging") part-of-speech tagging] and
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| #[+a("/usage/linguistic-features#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("/usage/linguistic-features#named-entities") named entity recognition] and
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| #[+a("/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("/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("/usage/processing-pipelines") 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 how to #[strong save and load your own models],
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| see the usage guide on
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| #[+a("/usage/training#saving-loading") saving and loading].
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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("/usage/training") training].
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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("/usage/adding-languages") adding languages].
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+section("lightning-tour")
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+h(2, "lightning-tour") Lightning tour
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include _spacy-101/_lightning-tour
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+section("architecture")
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+h(2, "architecture") Architecture
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include _spacy-101/_architecture
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+section("community-faq")
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+h(2, "community") Community & FAQ
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include _spacy-101/_community-faq
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