Nitrino/en_core_web_md

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tags: - spacy - token-classification language: - en license: mit model-index: - name: en_core_web_md results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.8531330602 - name: NER Recall type: recall value: 0.8448016827 - name: NER F Score type: f_score value: 0.8489469314 - task: name: POS type: token-classification metrics: - name: POS Accuracy type: accuracy value: 0.9736958159 - task: name: SENTER type: token-classification metrics: - name: SENTER Precision type: precision value: 0.9144345238 - name: SENTER Recall type: recall value: 0.8918134442 - name: SENTER F Score type: f_score value: 0.9029823331 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Dependencies Accuracy type: accuracy value: 0.9186827918 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Dependencies Accuracy type: accuracy value: 0.9186827918

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

English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.

Feature	Description
Name	en_core_web_md
Version	3.2.0
spaCy	>=3.2.0,<3.3.0
Default Pipeline	tok2vec, tagger, parser, attribute_ruler, lemmatizer, ner
Components	tok2vec, tagger, parser, senter, attribute_ruler, lemmatizer, ner
Vectors	684830 keys, 20000 unique vectors (300 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) GloVe Common Crawl (Jeffrey Pennington, Richard Socher, and Christopher D. Manning)
License	MIT
Author	Explosion

Label Scheme

View label scheme (114 labels for 4 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, ````
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
senter	I, S
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	99.93
TOKEN_P	99.57
TOKEN_R	99.58
TOKEN_F	99.57
TAG_ACC	97.37
SENTS_P	91.44
SENTS_R	89.18
SENTS_F	90.30
DEP_UAS	91.87
DEP_LAS	90.07
ENTS_P	85.31
ENTS_R	84.48
ENTS_F	84.89