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en_core_web_lg

Token Classification
spaCy
English
Eval Results
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en_core_web_lg / README.md
Adriane Boyd
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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: 0.8636641533
          - name: NER Recall
            type: recall
            value: 0.8489583333
          - name: NER F Score
            type: f_score
            value: 0.8562481059
      - task:
          name: TAG
          type: token-classification
        metrics:
          - name: TAG (XPOS) Accuracy
            type: accuracy
            value: 0.9734404547
      - task:
          name: UNLABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Unlabeled Attachment Score (UAS)
            type: f_score
            value: 0.9204363007
      - task:
          name: LABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Labeled Attachment Score (LAS)
            type: f_score
            value: 0.9023174614
      - task:
          name: SENTS
          type: token-classification
        metrics:
          - name: Sentences F-Score
            type: f_score
            value: 0.90444794

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 3.4.0
spaCy >=3.4.0,<3.5.0
Default Pipeline tok2vec, tagger, parser, attribute_ruler, lemmatizer, ner
Components tok2vec, tagger, parser, senter, attribute_ruler, lemmatizer, ner
Vectors 514157 keys, 514157 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)
Explosion Vectors (OSCAR 2109 + Wikipedia + OpenSubtitles + WMT News Crawl) (Explosion)
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 99.93
TOKEN_P 99.57
TOKEN_R 99.58
TOKEN_F 99.57
TAG_ACC 97.34
SENTS_P 91.79
SENTS_R 89.14
SENTS_F 90.44
DEP_UAS 92.04
DEP_LAS 90.23
ENTS_P 86.37
ENTS_R 84.90
ENTS_F 85.62