README.md

metadata

tags:
  - spacy
  - token-classification
language:
  - en
model-index:
  - name: en_egw_entity_extractor
    results:
      - task:
          name: NER
          type: token-classification
        metrics:
          - name: NER Precision
            type: precision
            value: 0.892504864
          - name: NER Recall
            type: recall
            value: 0.8951781257
          - name: NER F Score
            type: f_score
            value: 0.8938394961
      - task:
          name: TAG
          type: token-classification
        metrics:
          - name: TAG (XPOS) Accuracy
            type: accuracy
            value: 0.9731235568
      - task:
          name: UNLABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Unlabeled Attachment Score (UAS)
            type: f_score
            value: 0.9383889999
      - task:
          name: LABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Labeled Attachment Score (LAS)
            type: f_score
            value: 0.9226387112
      - task:
          name: SENTS
          type: token-classification
        metrics:
          - name: Sentences F-Score
            type: f_score
            value: 0.9496868166

Feature	Description
Name	en_egw_entity_extractor
Version	1.0.0
spaCy	>=3.7.2,<3.8.0
Default Pipeline	transformer, tagger, parser, ner
Components	transformer, tagger, parser, ner
Vectors	514157 keys, 514157 unique vectors (300 dimensions)
Sources	n/a
License	n/a
Author	n/a

Label Scheme

View label scheme (100 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	DATE, GPE, LOC, PERSON, REFERENCE

Accuracy

Type	Score
TAG_ACC	97.31
DEP_UAS	93.84
DEP_LAS	92.26
SENTS_P	95.17
SENTS_R	94.77
SENTS_F	94.97
ENTS_F	89.38
ENTS_P	89.25
ENTS_R	89.52
TRANSFORMER_LOSS	10291319.95
TAGGER_LOSS	1200598.50
PARSER_LOSS	5348475.42
NER_LOSS	297327.96