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Port over docs changes [ci skip]
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@ -208,7 +208,7 @@ p
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+row
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+cell #[code word_spacing]
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+cell int
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+cell Horizontal spacing between words and arcs in px.
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+cell Vertical spacing between words and arcs in px.
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+cell #[code 45]
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+row
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@ -674,7 +674,7 @@ p
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| token vectors.
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+aside-code("Example").
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apples = nlp(u'I like apples')
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doc = nlp(u'I like apples')
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assert doc.vector.dtype == 'float32'
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assert doc.vector.shape == (300,)
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@ -12,11 +12,24 @@ p Create a #[code GoldCorpus].
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code train_path]
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+cell unicode or #[code Path]
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+cell File or directory of training data.
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+cell #[code train]
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+cell unicode or #[code Path] or iterable
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+cell
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| Training data, as a path (file or directory) or iterable. If an
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| iterable, each item should be a #[code (text, paragraphs)]
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| tuple, where each paragraph is a tuple
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| #[code.u-break (sentences, brackets)],and each sentence is a
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| tuple #[code.u-break (ids, words, tags, heads, ner)]. See the
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| implementation of
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| #[+src(gh("spacy", "spacy/gold.pyx")) #[code gold.read_json_file]]
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| for further details.
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+row
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+cell #[code dev_path]
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+cell unicode or #[code Path]
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+cell File or directory of development data.
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+cell #[code dev]
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+cell unicode or #[code Path] or iterable
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+cell Development data, as a path (file or directory) or iterable.
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+row("foot")
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+cell returns
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+cell #[code GoldCorpus]
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+cell The newly constructed object.
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@ -325,6 +325,12 @@ p The L2 norm of the lexeme's vector representation.
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+cell bool
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+cell Is the lexeme a quotation mark?
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+row
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+cell #[code is_currency]
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+tag-new("2.0.8")
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+cell bool
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+cell Is the lexeme a currency symbol?
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+row
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+cell #[code like_url]
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+cell bool
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@ -111,6 +111,25 @@ p Match a stream of documents, yielding them in turn.
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| parallel, if the #[code Matcher] implementation supports
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| multi-threading.
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+row
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+cell #[code return_matches]
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+tag-new(2.1)
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+cell bool
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+cell
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| Yield the match lists along with the docs, making results
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| #[code (doc, matches)] tuples.
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+row
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+cell #[code as_tuples]
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+tag-new(2.1)
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+cell bool
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+cell
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| Interpret the input stream as #[code (doc, context)] tuples, and
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| yield #[code (result, context)] tuples out. If both
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| #[code return_matches] and #[code as_tuples] are #[code True],
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| the output will be a sequence of
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| #[code ((doc, matches), context)] tuples.
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+row("foot")
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+cell yields
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+cell #[code Doc]
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@ -209,7 +209,7 @@ p
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+row
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+cell #[code drop]
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+cell int
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+cell float
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+cell The dropout rate.
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+row
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@ -740,6 +740,12 @@ p The L2 norm of the token's vector representation.
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+cell bool
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+cell Is the token a quotation mark?
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+row
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+cell #[code is_currency]
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+tag-new("2.0.8")
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+cell bool
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+cell Is the token a currency symbol?
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+row
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+cell #[code like_url]
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+cell bool
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Before Width: | Height: | Size: 378 KiB |
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@ -76,11 +76,13 @@
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},
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"MODEL_LICENSES": {
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"CC BY 4.0": "https://creativecommons.org/licenses/by/4.0/",
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"CC BY-SA": "https://creativecommons.org/licenses/by-sa/3.0/",
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"CC BY-SA 3.0": "https://creativecommons.org/licenses/by-sa/3.0/",
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"CC BY-SA 4.0": "https://creativecommons.org/licenses/by-sa/4.0/",
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"CC BY-NC": "https://creativecommons.org/licenses/by-nc/3.0/",
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"CC BY-NC 3.0": "https://creativecommons.org/licenses/by-nc/3.0/",
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"CC-BY-NC-SA 3.0": "https://creativecommons.org/licenses/by-nc-sa/3.0/",
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"GPL": "https://www.gnu.org/licenses/gpl.html",
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"LGPL": "https://www.gnu.org/licenses/lgpl.html"
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},
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@ -40,7 +40,7 @@ p
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+item
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| Make the #[strong model data] available to the #[code Language] class
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| by calling #[+api("language#from_disk") #[code from_disk]] with the
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| path to the model data ditectory.
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| path to the model data directory.
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p
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| So when you call this...
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@ -53,7 +53,7 @@ p
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| pipeline #[code.u-break ["tagger", "parser", "ner"]]. spaCy will then
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| initialise #[code spacy.lang.en.English], and create each pipeline
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| component and add it to the processing pipeline. It'll then load in the
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| model's data from its data ditectory and return the modified
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| model's data from its data directory and return the modified
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| #[code Language] class for you to use as the #[code nlp] object.
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p
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@ -37,7 +37,7 @@ p
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+cell.u-text-label.u-color-theme=label
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for cell in cells
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+cell.u-text-center
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- var result = cell > 0.5 ? ["yes", "similar"] : cell != 1 ? ["no", "dissimilar"] : ["neutral", "identical"]
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- var result = cell < 0.5 ? ["no", "dissimilar"] : cell != 1 ? ["yes", "similar"] : ["neutral", "identical"]
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| #[code=cell.toFixed(2)] #[+procon(...result)]
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p
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@ -163,7 +163,7 @@ p
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nlp = English().from_disk('/path/to/nlp')
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p
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| spay's serialization API has been made consistent across classes and
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| spaCy's serialization API has been made consistent across classes and
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| objects. All container classes, i.e. #[code Language], #[code Doc],
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| #[code Vocab] and #[code StringStore] now have a #[code to_bytes()],
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| #[code from_bytes()], #[code to_disk()] and #[code from_disk()] method
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@ -68,7 +68,7 @@ p
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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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| #[+a("https://github.com/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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