github explosion/spacy-models en_core_web_sm-2.3.0

Downloads

Details: https://spacy.io/models/en#en_core_web_sm

File checksum: ea8c87848b4a97ced174919e08c00c7888a30495ace38d281d455ee270da2c12

English multi-task CNN trained on OntoNotes. Assigns context-specific token vectors, POS tags, dependency parse and named entities.

Feature Description
Name en_core_web_sm
Version 2.3.0
spaCy >=2.3.0,<2.4.0
Model size 11 MB
Pipeline  tagger, parser, ner
Vectors 0 keys, 0 unique vectors (0 dimensions)
Sources OntoNotes 5
License MIT
Author Explosion

Label Scheme

Component Labels
tagger  $, '', ,, -LRB-, -RRB-, ., :, ADD, AFX, CC, CD, DT, EX, FW, HYPH, IN, JJ, JJR, JJS, LS, MD, NFP, NN, NNP, NNPS, NNS, PDT, POS, PRP, PRP$, RB, RBR, RBS, RP, SYM, TO, UH, VB, VBD, VBG, VBN, VBP, VBZ, WDT, WP, WP$, WRB, XX, _SP, ````
parser  ROOT, acl, acomp, advcl, advmod, agent, amod, appos, attr, aux, auxpass, case, cc, ccomp, compound, conj, csubj, csubjpass, dative, dep, det, dobj, expl, intj, mark, meta, neg, nmod, npadvmod, nsubj, nsubjpass, nummod, oprd, parataxis, pcomp, pobj, poss, preconj, predet, prep, prt, punct, quantmod, relcl, xcomp
ner  CARDINAL, DATE, EVENT, FAC, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK_OF_ART

Accuracy

Type Score
LAS  89.68
UAS  91.56
TOKEN_ACC  99.76
TAGS_ACC  97.05
ENTS_F  85.36
ENTS_P  85.46
ENTS_R  85.25

Installation

pip install spacy
python -m spacy download en_core_web_sm

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