README.md

metadata

tags:
  - spacy
  - token-classification
language:
  - en
license: mit
model-index:
  - name: en_core_web_lg
    results:
      - task:
          name: NER
          type: token-classification
        metrics:
          - name: NER Precision
            type: precision
            value: 0.8537926165
          - name: NER Recall
            type: recall
            value: 0.8478064904
          - name: NER F Score
            type: f_score
            value: 0.850789024
      - task:
          name: POS
          type: token-classification
        metrics:
          - name: POS Accuracy
            type: accuracy
            value: 0.9728657496
      - task:
          name: SENTER
          type: token-classification
        metrics:
          - name: SENTER Precision
            type: precision
            value: 0.9029447521
          - name: SENTER Recall
            type: recall
            value: 0.8819183323
          - name: SENTER F Score
            type: f_score
            value: 0.8923076923
      - task:
          name: UNLABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Unlabeled Dependencies Accuracy
            type: accuracy
            value: 0.9184566079
      - task:
          name: LABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Labeled Dependencies Accuracy
            type: accuracy
            value: 0.9184566079

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

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

Feature	Description
Name	en_core_web_lg
Version	3.1.0
spaCy	>=3.1.0,<3.2.0
Default Pipeline	tok2vec, tagger, parser, attribute_ruler, lemmatizer, ner
Components	tok2vec, tagger, parser, senter, attribute_ruler, lemmatizer, ner
Vectors	684830 keys, 684830 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
TAG_ACC	97.29
DEP_UAS	91.85
DEP_LAS	90.02
ENTS_P	85.38
ENTS_R	84.78
ENTS_F	85.08
SENTS_P	90.29
SENTS_R	88.19
SENTS_F	89.23