README.md

metadata

tags:
  - spacy
  - token-classification
language:
  - en
license: mit
model-index:
  - name: en_core_web_sm_job
    results:
      - task:
          name: NER
          type: token-classification
        metrics:
          - name: NER Precision
            type: precision
            value: 0.8454836771
          - name: NER Recall
            type: recall
            value: 0.8456530449
          - name: NER F Score
            type: f_score
            value: 0.8455683525
      - task:
          name: TAG
          type: token-classification
        metrics:
          - name: TAG (XPOS) Accuracy
            type: accuracy
            value: 0.97246532
      - task:
          name: UNLABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Unlabeled Attachment Score (UAS)
            type: f_score
            value: 0.9175304332
      - task:
          name: LABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Labeled Attachment Score (LAS)
            type: f_score
            value: 0.89874821
      - task:
          name: SENTS
          type: token-classification
        metrics:
          - name: Sentences F-Score
            type: f_score
            value: 0.9059485531

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

Feature	Description
Name	en_core_web_sm_job
Version	3.6.0
spaCy	>=3.6.0,<3.7.0
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 (116 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, EXPERIENCE, FAC, FACILITY, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, PROFESSION, 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	97.25
SENTS_P	92.02
SENTS_R	89.21
SENTS_F	90.59
DEP_UAS	91.75
DEP_LAS	89.87
ENTS_P	84.55
ENTS_R	84.57
ENTS_F	84.56