en_furniture_ner / README.md
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Update spaCy pipeline
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metadata
tags:
  - spacy
  - token-classification
language:
  - en
model-index:
  - name: en_furniture_ner
    results:
      - task:
          name: NER
          type: token-classification
        metrics:
          - name: NER Precision
            type: precision
            value: 0.9047619048
          - name: NER Recall
            type: recall
            value: 0.9047619048
          - name: NER F Score
            type: f_score
            value: 0.9047619048
      - task:
          name: TAG
          type: token-classification
        metrics:
          - name: TAG (XPOS) Accuracy
            type: accuracy
            value: 0
      - task:
          name: LEMMA
          type: token-classification
        metrics:
          - name: Lemma Accuracy
            type: accuracy
            value: 0
      - task:
          name: UNLABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Unlabeled Attachment Score (UAS)
            type: f_score
            value: 0
      - task:
          name: LABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Labeled Attachment Score (LAS)
            type: f_score
            value: 0
      - task:
          name: SENTS
          type: token-classification
        metrics:
          - name: Sentences F-Score
            type: f_score
            value: 0
Feature Description
Name en_furniture_ner
Version 0.0.0
spaCy >=3.5.3,<3.6.0
Default Pipeline tok2vec, tagger, parser, attribute_ruler, lemmatizer, ner
Components tok2vec, tagger, parser, attribute_ruler, lemmatizer, ner
Vectors 514157 keys, 514157 unique vectors (300 dimensions)
Sources n/a
License n/a
Author n/a

Label Scheme

View label scheme (114 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, FURNITURE, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK_OF_ART

Accuracy

Type Score
TAG_ACC 0.00
DEP_UAS 0.00
DEP_LAS 0.00
DEP_LAS_PER_TYPE 0.00
SENTS_P 0.00
SENTS_R 0.00
SENTS_F 0.00
LEMMA_ACC 0.00
ENTS_F 90.48
ENTS_P 90.48
ENTS_R 90.48
TOK2VEC_LOSS 0.00
NER_LOSS 5914.38