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Update v2 docs and benchmarks
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@ -22,7 +22,7 @@ p
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| entirely new #[strong deep learning-powered models] for spaCy's tagger,
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| parser and entity recognizer. The new models are #[strong 20x smaller]
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| than the linear models that have powered spaCy until now: from 300 MB to
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| only 14 MB.
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| only 15 MB.
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p
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| We've also made several usability improvements that are
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@ -247,12 +247,12 @@ p
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| #[code spacy.lang.xx]
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+row
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+cell #[code spacy.orth]
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+cell #[code spacy.lang.xx.lex_attrs]
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+cell #[code orth]
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+cell #[code lang.xx.lex_attrs]
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+row
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+cell #[code cli.model]
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+cell -
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+cell #[code syntax.syntax_iterators]
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+cell #[code lang.xx.syntax_iterators]
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+row
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+cell #[code Language.save_to_directory]
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@ -266,8 +266,6 @@ p
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+cell
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| #[code Vocab.load]
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| #[code Vocab.load_lexemes]
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| #[code Vocab.load_vectors]
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| #[code Vocab.load_vectors_from_bin_loc]
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+cell
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| #[+api("vocab#from_disk") #[code Vocab.from_disk]]
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| #[+api("vocab#from_bytes") #[code Vocab.from_bytes]]
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@ -275,10 +273,24 @@ p
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+row
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+cell
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| #[code Vocab.dump]
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+cell
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| #[+api("vocab#to_disk") #[code Vocab.to_disk]]#[br]
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| #[+api("vocab#to_bytes") #[code Vocab.to_bytes]]
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+row
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+cell
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| #[code Vocab.load_vectors]
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| #[code Vocab.load_vectors_from_bin_loc]
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+cell
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| #[+api("vectors#from_disk") #[code Vectors.from_disk]]
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| #[+api("vectors#from_bytes") #[code Vectors.from_bytes]]
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+row
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+cell
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| #[code Vocab.dump_vectors]
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+cell
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| #[+api("vocab#to_disk") #[code Vocab.to_disk]]
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| #[+api("vocab#to_bytes") #[code Vocab.to_bytes]]
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| #[+api("vectors#to_disk") #[code Vectors.to_disk]]
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| #[+api("vectors#to_bytes") #[code Vectors.to_bytes]]
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+row
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+cell
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@ -296,7 +308,9 @@ p
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+row
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+cell #[code Tokenizer.load]
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+cell -
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+cell
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| #[+api("tokenizer#from_disk") #[code Tokenizer.from_disk]]
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| #[+api("tokenizer#from_bytes") #[code Tokenizer.from_bytes]]
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+row
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+cell #[code Tagger.load]
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@ -342,6 +356,10 @@ p
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+cell #[code Token.is_ancestor_of]
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+cell #[+api("token#is_ancestor") #[code Token.is_ancestor]]
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+row
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+cell #[code cli.model]
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+cell -
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+h(2, "migrating") Migrating from spaCy 1.x
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p
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@ -466,18 +484,27 @@ p
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+h(2, "benchmarks") Benchmarks
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+under-construction
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+aside("Data sources")
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| #[strong Parser, tagger, NER:] #[+a("https://www.gabormelli.com/RKB/OntoNotes_Corpus") OntoNotes 5]#[br]
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| #[strong Word vectors:] #[+a("http://commoncrawl.org") Common Crawl]#[br]
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p The evaluation was conducted on raw text with no gold standard information.
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+table(["Model", "Version", "Type", "UAS", "LAS", "NER F", "POS", "w/s"])
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+row
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+cell #[code en_core_web_sm]
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for cell in ["2.0.0", "neural", "", "", "", "", ""]
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+cell=cell
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mixin benchmark-row(name, details, values, highlight, style)
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+row(style)
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+cell #[code=name]
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for cell in details
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+cell=cell
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for cell, i in values
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+cell.u-text-right
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if highlight && highlight[i]
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strong=cell
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else
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!=cell
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+row
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+cell #[code es_dep_web_sm]
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for cell in ["2.0.0", "neural", "", "", "", "", ""]
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+cell=cell
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+row("divider")
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+cell #[code en_core_web_sm]
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for cell in ["1.1.0", "linear", "", "", "", "", ""]
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+cell=cell
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+benchmark-row("en_core_web_sm", ["2.0.0", "neural"], ["91.2", "89.2", "82.6", "96.6", "10,300"], [1, 1, 1, 0, 0])
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+benchmark-row("en_core_web_sm", ["1.2.0", "linear"], ["86.6", "83.8", "78.5", "96.6", "25,700"], [0, 0, 0, 0, 1], "divider")
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+benchmark-row("en_core_web_md", ["1.2.1", "linear"], ["90.6", "88.5", "81.4", "96.7", "18,800"], [0, 0, 0, 1, 0])
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