en-core-web-sm-vbspacy 3.8.0

Spacy model uploaded to PyPi. All credits go to the original devs.

Spacy model uploaded to PyPi. All credits go to the original devs.

English pipeline optimized for CPU.

Components:

• tok2vec
• tagger
• parser
• senter
• ner
• attribute_ruler
• lemmatizer.

Feature	Description
Name	en_core_web_sm_vbspacy
Version	3.8.0
spaCy	>=3.7.5,<3.9.0
Default Pipeline	tok2vec, tagger, parser, attribute_ruler, lemmatizer, ner
Components	tok2vec, tagger, parser, senter, attribute_ruler, lemmatizer, ner
Vectors	0 keys, 0 unique vectors (0 dimensions)
Sources	OntoNotes 5 (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston) ClearNLP Constituent-to-Dependency Conversion (Emory University) WordNet 3.0 (Princeton University)
License	MIT
Author	Explosion

Label Scheme

View label scheme (113 labels for 3 components)

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
TOKEN_ACC	99.86
TOKEN_P	99.57
TOKEN_R	99.58
TOKEN_F	99.57
TAG_ACC	97.29
SENTS_P	92.01
SENTS_R	89.39
SENTS_F	90.68
DEP_UAS	91.77
DEP_LAS	89.92
ENTS_P	84.30
ENTS_R	84.36
ENTS_F	84.33