README.md

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