mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 20:28:20 +03:00
7e98a3613c
Use an example from an old issue https://github.com/explosion/spaCy/issues/172#issuecomment-183963403.
136 lines
5.4 KiB
Plaintext
136 lines
5.4 KiB
Plaintext
//- 💫 DOCS > USAGE > PROCESSING TEXT
|
|
|
|
include ../../_includes/_mixins
|
|
|
|
p
|
|
| Once you have loaded the #[code nlp] object, you can call it as though
|
|
| it were a function. This allows you to process a single unicode string.
|
|
|
|
+code.
|
|
doc = nlp(u'Hello, world! A three sentence document.\nWith new lines...')
|
|
|
|
p
|
|
| The library should perform equally well with short or long documents.
|
|
| All algorithms are linear-time in the length of the string, and once the
|
|
| data is loaded, there's no significant start-up cost to consider. This
|
|
| means that you don't have to strategically merge or split your text —
|
|
| you should feel free to feed in either single tweets or whole novels.
|
|
|
|
p
|
|
| If you run #[code nlp = spacy.load('en')], the #[code nlp] object will
|
|
| be an instance of #[code spacy.en.English]. This means that when you run
|
|
| #[code doc = nlp(text)], you're executing
|
|
| #[code spacy.en.English.__call__], which is implemented on its parent
|
|
| class, #[+api("language") #[code Language]].
|
|
|
|
+code.
|
|
doc = nlp.make_doc(text)
|
|
for proc in nlp.pipeline:
|
|
proc(doc)
|
|
|
|
p
|
|
| I've tried to make sure that the #[code Language.__call__] function
|
|
| doesn't do any "heavy lifting", so that you won't have complicated logic
|
|
| to replicate if you need to make your own pipeline class. This is all it
|
|
| does.
|
|
|
|
p
|
|
| The #[code .make_doc()] method and #[code .pipeline] attribute make it
|
|
| easier to customise spaCy's behaviour. If you're using the default
|
|
| pipeline, we can desugar one more time.
|
|
|
|
+code.
|
|
doc = nlp.tokenizer(text)
|
|
nlp.tagger(doc)
|
|
nlp.parser(doc)
|
|
nlp.entity(doc)
|
|
|
|
p Finally, here's where you can find out about each of those components:
|
|
|
|
+table(["Name", "Source"])
|
|
+row
|
|
+cell #[code tokenizer]
|
|
+cell #[+src(gh("spacy", "spacy/tokenizer.pyx")) spacy.tokenizer.Tokenizer]
|
|
|
|
+row
|
|
+cell #[code tagger]
|
|
+cell #[+src(gh("spacy", "spacy/tagger.pyx")) spacy.pipeline.Tagger]
|
|
|
|
+row
|
|
+cell #[code parser]
|
|
+cell #[+src(gh("spacy", "spacy/syntax/parser.pyx")) spacy.pipeline.DependencyParser]
|
|
|
|
+row
|
|
+cell #[code entity]
|
|
+cell #[+src(gh("spacy", "spacy/syntax/parser.pyx")) spacy.pipeline.EntityRecognizer]
|
|
|
|
+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 .pipe()]] method. The #[code .pipe()]
|
|
| 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 the #[code .pipe()] method 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 Python generators. Here are a few quick hints:
|
|
|
|
+list
|
|
+item
|
|
| Generator comprehensions can be written
|
|
| (#[code item for item in sequence])
|
|
|
|
+item
|
|
| The #[code itertools] built-in library and the #[code cytoolz]
|
|
| package provide a lot of handy generator tools
|
|
|
|
+item
|
|
| Often you'll have an input stream that pairs text with some
|
|
| important metadata, e.g. a JSON document. To pair up the metadata
|
|
| with the processed #[code Doc] object, you should use the tee
|
|
| function to split the generator in two, and then #[code izip] the
|
|
| extra stream to the document stream. Here's an
|
|
| #[a(href="https://github.com/explosion/spaCy/issues/172#issuecomment-183963403")= "example"]
|
|
|
|
+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.
|