mirror of https://github.com/explosion/spaCy.git
148 lines
6.6 KiB
Plaintext
148 lines
6.6 KiB
Plaintext
//- 💫 DOCS > USAGE > PROCESSING TEXT
|
|
|
|
include ../../_includes/_mixins
|
|
|
|
+under-construction
|
|
|
|
+h(2, "multithreading") Multi-threading with #[code .pipe()]
|
|
|
|
p
|
|
| If you have a sequence of documents to process, you should use the
|
|
| #[+api("language#pipe") #[code Language.pipe()]] method. The method takes
|
|
| an iterator of texts, and accumulates an internal buffer,
|
|
| which it works on in parallel. It then yields the documents in order,
|
|
| one-by-one. After a long and bitter struggle, the global interpreter
|
|
| lock was freed around spaCy's main parsing loop in v0.100.3. This means
|
|
| that #[code .pipe()] will be significantly faster in most
|
|
| practical situations, because it allows shared memory parallelism.
|
|
|
|
+code.
|
|
for doc in nlp.pipe(texts, batch_size=10000, n_threads=3):
|
|
pass
|
|
|
|
p
|
|
| To make full use of the #[code .pipe()] function, you might want to
|
|
| brush up on #[strong Python generators]. Here are a few quick hints:
|
|
|
|
+list
|
|
+item
|
|
| Generator comprehensions can be written as
|
|
| #[code (item for item in sequence)].
|
|
|
|
+item
|
|
| The
|
|
| #[+a("https://docs.python.org/2/library/itertools.html") #[code itertools] built-in library]
|
|
| and the
|
|
| #[+a("https://github.com/pytoolz/cytoolz") #[code cytoolz] package]
|
|
| provide a lot of handy #[strong generator tools].
|
|
|
|
+item
|
|
| Often you'll have an input stream that pairs text with some
|
|
| important meta data, e.g. a JSON document. To
|
|
| #[strong pair up the meta data] with the processed #[code Doc]
|
|
| object, you should use the #[code itertools.tee] function to split
|
|
| the generator in two, and then #[code izip] the extra stream to the
|
|
| document stream.
|
|
|
|
+h(2, "own-annotations") Bringing your own annotations
|
|
|
|
p
|
|
| spaCy generally assumes by default that your data is raw text. However,
|
|
| sometimes your data is partially annotated, e.g. with pre-existing
|
|
| tokenization, part-of-speech tags, etc. The most common situation is
|
|
| that you have pre-defined tokenization. If you have a list of strings,
|
|
| you can create a #[code Doc] object directly. Optionally, you can also
|
|
| specify a list of boolean values, indicating whether each word has a
|
|
| subsequent space.
|
|
|
|
+code.
|
|
doc = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'], spaces=[False, True, False, False])
|
|
|
|
p
|
|
| If provided, the spaces list must be the same length as the words list.
|
|
| The spaces list affects the #[code doc.text], #[code span.text],
|
|
| #[code token.idx], #[code span.start_char] and #[code span.end_char]
|
|
| attributes. If you don't provide a #[code spaces] sequence, spaCy will
|
|
| assume that all words are whitespace delimited.
|
|
|
|
+code.
|
|
good_spaces = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'], spaces=[False, True, False, False])
|
|
bad_spaces = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'])
|
|
assert bad_spaces.text == u'Hello , world !'
|
|
assert good_spaces.text == u'Hello, world!'
|
|
|
|
p
|
|
| Once you have a #[+api("doc") #[code Doc]] object, you can write to its
|
|
| attributes to set the part-of-speech tags, syntactic dependencies, named
|
|
| entities and other attributes. For details, see the respective usage
|
|
| pages.
|
|
|
|
+h(2, "models") Working with models
|
|
|
|
p
|
|
| If your application depends on one or more #[+a("/docs/usage/models") models],
|
|
| you'll usually want to integrate them into your continuous integration
|
|
| workflow and build process. While spaCy provides a range of useful helpers
|
|
| for downloading, linking and loading models, the underlying functionality
|
|
| is entirely based on native Python packages. This allows your application
|
|
| to handle a model like any other package dependency.
|
|
|
|
+h(3, "models-download") Downloading and requiring model dependencies
|
|
|
|
p
|
|
| spaCy's built-in #[+api("cli#download") #[code download]] command
|
|
| is mostly intended as a convenient, interactive wrapper. It performs
|
|
| compatibility checks and prints detailed error messages and warnings.
|
|
| However, if you're downloading models as part of an automated build
|
|
| process, this only adds an unnecessary layer of complexity. If you know
|
|
| which models your application needs, you should be specifying them directly.
|
|
|
|
p
|
|
| Because all models are valid Python packages, you can add them to your
|
|
| application's #[code requirements.txt]. If you're running your own
|
|
| internal PyPi installation, you can simply upload the models there. pip's
|
|
| #[+a("https://pip.pypa.io/en/latest/reference/pip_install/#requirements-file-format") requirements file format]
|
|
| supports both package names to download via a PyPi server, as well as direct
|
|
| URLs.
|
|
|
|
+code("requirements.txt", "text").
|
|
spacy>=2.0.0,<3.0.0
|
|
-e #{gh("spacy-models")}/releases/download/en_core_web_sm-2.0.0/en_core_web_sm-2.0.0.tar.gz
|
|
|
|
p
|
|
| All models are versioned and specify their spaCy dependency. This ensures
|
|
| cross-compatibility and lets you specify exact version requirements for
|
|
| each model. If you've trained your own model, you can use the
|
|
| #[+api("cli#package") #[code package]] command to generate the required
|
|
| meta data and turn it into a loadable package.
|
|
|
|
+h(3, "models-loading") Loading and testing models
|
|
|
|
p
|
|
| Downloading models directly via pip won't call spaCy's link
|
|
| #[+api("cli#link") #[code link]] command, which creates
|
|
| symlinks for model shortcuts. This means that you'll have to run this
|
|
| command separately, or use the native #[code import] syntax to load the
|
|
| models:
|
|
|
|
+code.
|
|
import en_core_web_sm
|
|
nlp = en_core_web_sm.load()
|
|
|
|
p
|
|
| In general, this approach is recommended for larger code bases, as it's
|
|
| more "native", and doesn't depend on symlinks or rely on spaCy's loader
|
|
| to resolve string names to model packages. If a model can't be
|
|
| imported, Python will raise an #[code ImportError] immediately. And if a
|
|
| model is imported but not used, any linter will catch that.
|
|
|
|
p
|
|
| Similarly, it'll give you more flexibility when writing tests that
|
|
| require loading models. For example, instead of writing your own
|
|
| #[code try] and #[code except] logic around spaCy's loader, you can use
|
|
| #[+a("http://pytest.readthedocs.io/en/latest/") pytest]'s
|
|
| #[code importorskip()] method to only run a test if a specific model or
|
|
| model version is installed. Each model package exposes a #[code __version__]
|
|
| attribute which you can also use to perform your own version compatibility
|
|
| checks before loading a model.
|