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Update spaCy pipeline
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metadata
tags:
  - spacy
  - token-classification
language:
  - en
license: cc-by-sa-3.0
model-index:
  - name: en_ner_bionlp13cg_md
    results:
      - task:
          name: NER
          type: token-classification
        metrics:
          - name: NER Precision
            type: precision
            value: 0.7980221756
          - name: NER Recall
            type: recall
            value: 0.7643513203
          - name: NER F Score
            type: f_score
            value: 0.7808239261
      - 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

Spacy Models for Biomedical Text.

Feature Description
Name en_ner_bionlp13cg_md
Version 0.5.3
spaCy >=3.6.1,<3.7.0
Default Pipeline tok2vec, tagger, attribute_ruler, lemmatizer, parser, ner
Components tok2vec, tagger, attribute_ruler, lemmatizer, parser, ner
Vectors 4087446 keys, 50000 unique vectors (200 dimensions)
Sources BIONLP13CG
OntoNotes 5
Common Crawl
GENIA 1.0
License CC BY-SA 3.0
Author Allen Institute for Artificial Intelligence

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, ````
parser ROOT, acl, acl:relcl, acomp, advcl, advmod, amod, amod@nmod, appos, attr, aux, auxpass, case, cc, cc:preconj, ccomp, compound, compound:prt, conj, cop, csubj, dative, dep, det, det:predet, dobj, expl, intj, mark, meta, mwe, neg, nmod, nmod:npmod, nmod:poss, nmod:tmod, nsubj, nsubjpass, nummod, parataxis, pcomp, pobj, preconj, predet, prep, punct, quantmod, xcomp
ner AMINO_ACID, ANATOMICAL_SYSTEM, CANCER, CELL, CELLULAR_COMPONENT, DEVELOPING_ANATOMICAL_STRUCTURE, GENE_OR_GENE_PRODUCT, IMMATERIAL_ANATOMICAL_ENTITY, MULTI_TISSUE_STRUCTURE, ORGAN, ORGANISM, ORGANISM_SUBDIVISION, ORGANISM_SUBSTANCE, PATHOLOGICAL_FORMATION, SIMPLE_CHEMICAL, TISSUE

Accuracy

Type Score
TAG_ACC 0.00
LEMMA_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
ENTS_F 78.08
ENTS_P 79.80
ENTS_R 76.44
NER_LOSS 588700.34