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en_core_web_lg

Token Classification
spaCy
English
Eval Results
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metadata
tags:
  - spacy
  - token-classification
language:
  - en
license: MIT
model-index:
  - name: en_core_web_lg
    results:
      - tasks:
          name: NER
          type: token-classification
          metrics:
            - name: Precision
              type: precision
              value: 0.8537926165
            - name: Recall
              type: recall
              value: 0.8478064904
            - name: F Score
              type: f_score
              value: 0.850789024
      - tasks:
          name: POS
          type: token-classification
          metrics:
            - name: Accuracy
              type: accuracy
              value: 0.9728657496
      - tasks:
          name: SENTER
          type: token-classification
          metrics:
            - name: Precision
              type: precision
              value: 0.9029447521
            - name: Recall
              type: recall
              value: 0.8819183323
            - name: F Score
              type: f_score
              value: 0.8923076923
      - tasks:
          name: UNLABELED_DEPENDENCIES
          type: token-classification
          metrics:
            - name: Accuracy
              type: accuracy
              value: 0.9184566079
      - tasks:
          name: LABELED_DEPENDENCIES
          type: token-classification
          metrics:
            - name: 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