mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-04 01:48:04 +03:00
Add 101 for Vocab, Lexeme and StringStore
This commit is contained in:
ines
parent
d8fd002e59
commit
6d76c1ea16
92
website/docs/usage/_spacy-101/_vocab-stringstore.jade
Normal file
92
website/docs/usage/_spacy-101/_vocab-stringstore.jade
Normal file
|
|
@ -0,0 +1,92 @@
|
||||||
|
//- 💫 DOCS > USAGE > SPACY 101 > VOCAB & STRINGSTORE
|
||||||
|
|
||||||
|
p
|
||||||
|
| Whenever possible, spaCy tries to store data in a vocabulary, the
|
||||||
|
| #[+api("vocab") #[code Vocab]], that will be
|
||||||
|
| #[strong shared by multiple documents]. To save memory, spaCy also
|
||||||
|
| encodes all strings to #[strong integer IDs] – in this case for example,
|
||||||
|
| "coffee" has the ID #[code 3672]. Entity labels like "ORG" and
|
||||||
|
| part-of-speech tags like "VERB" are also encoded. Internally, spaCy
|
||||||
|
| only "speaks" in integer IDs.
|
||||||
|
|
||||||
|
+aside
|
||||||
|
| #[strong Token]: A word, punctuation mark etc. #[em in context], including
|
||||||
|
| its attributes, tags and dependencies.#[br]
|
||||||
|
| #[strong Lexeme]: A "word type" with no context. Includes the word shape
|
||||||
|
| and flags, e.g. if it's lowercase, a digit or punctuation.#[br]
|
||||||
|
| #[strong Doc]: A processed container of tokens in context.#[br]
|
||||||
|
| #[strong Vocab]: The collection of lexemes.#[br]
|
||||||
|
| #[strong StringStore]: The dictionary mapping integer IDs to strings, for
|
||||||
|
| example #[code 3672] → "coffee".
|
||||||
|
|
||||||
|
+image
|
||||||
|
include ../../../assets/img/docs/vocab_stringstore.svg
|
||||||
|
.u-text-right
|
||||||
|
+button("/assets/img/docs/vocab_stringstore.svg", false, "secondary").u-text-tag View large graphic
|
||||||
|
|
||||||
|
p
|
||||||
|
| If you process lots of documents containing the word "coffee" in all
|
||||||
|
| kinds of different contexts, storing the exact string "coffee" every time
|
||||||
|
| would take up way too much space. So instead, spaCy assigns it an ID
|
||||||
|
| and stores it in the #[+api("stringstore") #[code StringStore]]. You can
|
||||||
|
| think of the #[code StringStore] as a
|
||||||
|
| #[strong lookup table that works in both directions] – you can look up a
|
||||||
|
| string to get its ID, or an ID to get its string:
|
||||||
|
|
||||||
|
+code.
|
||||||
|
doc = nlp(u'I like coffee')
|
||||||
|
assert doc.vocab.strings[u'coffee'] == 3572
|
||||||
|
assert doc.vocab.strings[3572] == u'coffee'
|
||||||
|
|
||||||
|
p
|
||||||
|
| Now that all strings are encoded, the entries in the vocabulary
|
||||||
|
| #[strong don't need to include the word text] themselves. Instead,
|
||||||
|
| they can look it up in the #[code StringStore] via its integer ID. Each
|
||||||
|
| entry in the vocabulary, also called #[+api("lexeme") #[code Lexeme]],
|
||||||
|
| contains the #[strong context-independent] information about a word.
|
||||||
|
| For example, no matter if "love" is used as a verb or a noun in some
|
||||||
|
| context, its spelling and whether it consists of alphabetic characters
|
||||||
|
| won't ever change.
|
||||||
|
|
||||||
|
+code.
|
||||||
|
for word in doc:
|
||||||
|
lexeme = doc.vocab[word.text]
|
||||||
|
print(lexeme.text, lexeme.orth, lexeme.shape_, lexeme.prefix_, lexeme.suffix_,
|
||||||
|
lexeme.is_alpha, lexeme.is_digit, lexeme.is_title, lexeme.lang_)
|
||||||
|
|
||||||
|
+aside
|
||||||
|
| #[strong Text]: The original text of the lexeme.#[br]
|
||||||
|
| #[strong Orth]: The integer ID of the lexeme.#[br]
|
||||||
|
| #[strong Shape]: The abstract word shape of the lexeme.#[br]
|
||||||
|
| #[strong Prefix]: By default, the first letter of the word string.#[br]
|
||||||
|
| #[strong Suffix]: By default, the last three letters of the word string.#[br]
|
||||||
|
| #[strong is alpha]: Does the lexeme consist of alphabetic characters?#[br]
|
||||||
|
| #[strong is digit]: Does the lexeme consist of digits?#[br]
|
||||||
|
| #[strong is title]: Does the lexeme consist of alphabetic characters?#[br]
|
||||||
|
| #[strong Lang]: The language of the parent vocabulary.
|
||||||
|
|
||||||
|
+table(["text", "orth", "shape", "prefix", "suffix", "is_alpha", "is_digit", "is_title", "lang"])
|
||||||
|
- var style = [0, 1, 1, 0, 0, 1, 1, 1, 0]
|
||||||
|
+annotation-row(["I", 508, "X", "I", "I", true, false, true, "en"], style)
|
||||||
|
+annotation-row(["love", 949, "xxxx", "l", "ove", true, false, false, "en"], style)
|
||||||
|
+annotation-row(["coffee", 3572, "xxxx", "c", "ffe", true, false, false, "en"], style)
|
||||||
|
|
||||||
|
p
|
||||||
|
| The specific entries in the voabulary and their IDs don't really matter –
|
||||||
|
| #[strong as long as they match]. That's why you always need to make sure
|
||||||
|
| all objects you create have access to the same vocabulary. If they don't,
|
||||||
|
| the IDs won't match and spaCy will either produce very confusing results,
|
||||||
|
| or fail alltogether.
|
||||||
|
|
||||||
|
+code.
|
||||||
|
from spacy.tokens import Doc
|
||||||
|
from spacy.vocab import Vocab
|
||||||
|
|
||||||
|
doc = nlp(u'I like coffee') # original Doc
|
||||||
|
new_doc = Doc(Vocab(), words=['I', 'like', 'coffee']) # new Doc with empty Vocab
|
||||||
|
assert doc.vocab.strings[u'coffee'] == 3572 # ID in vocab of Doc
|
||||||
|
assert new_doc.vocab.strings[u'coffee'] == 446 # ID in vocab of new Doc
|
||||||
|
|
||||||
|
p
|
||||||
|
| Even though both #[code Doc] objects contain the same words, the internal
|
||||||
|
| integer IDs are very different.
|
||||||
|
|
@ -113,6 +113,10 @@ include _spacy-101/_word-vectors
|
||||||
|
|
||||||
include _spacy-101/_pipelines
|
include _spacy-101/_pipelines
|
||||||
|
|
||||||
|
+h(2, "vocab-stringstore") Vocab, lexemes and the string store
|
||||||
|
|
||||||
|
include _spacy-101/_vocab-stringstore
|
||||||
|
|
||||||
+h(2, "serialization") Serialization
|
+h(2, "serialization") Serialization
|
||||||
|
|
||||||
include _spacy-101/_serialization
|
include _spacy-101/_serialization
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue
Block a user