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metadata
tags:
  - spacy
  - token-classification
language:
  - en
model-index:
  - name: en_acnl_roberta_pipeline
    results:
      - task:
          name: POS
          type: token-classification
        metrics:
          - name: POS Accuracy
            type: accuracy
            value: 0.9805220696
      - task:
          name: SENTER
          type: token-classification
        metrics:
          - name: SENTER Precision
            type: precision
            value: 0.9380376859
          - name: SENTER Recall
            type: recall
            value: 0.954223356
          - name: SENTER F Score
            type: f_score
            value: 0.946061298
      - task:
          name: UNLABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Unlabeled Dependencies Accuracy
            type: accuracy
            value: 0.9597776646
      - task:
          name: LABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Labeled Dependencies Accuracy
            type: accuracy
            value: 0.9597776646
license: cc-by-4.0
datasets:
  - conll2012_ontonotesv5
metrics:
  - f1
library_name: spacy
pipeline_tag: text-classification
Feature Description
Name en_acnl_roberta_pipeline
Version 0.0.1
spaCy >=3.1.3,<3.2.0
Default Pipeline transformer, tagger, parser
Components transformer, tagger, parser
Vectors 0 keys, 0 unique vectors (0 dimensions)
Sources OntoNotes
License CC BY-SA 4.0
Author Daniel Vasić

Label Scheme

View label scheme (87 labels for 2 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, VERB, WDT, WP, WP$, WRB, XX, ````
parser ROOT, acl, acomp, advcl, advmod, amod, appos, aux, auxpass, case, cc, ccomp, compound, conj, dative, dep, det, dobj, intj, mark, meta, neg, nmod, npadvmod, nummod, parataxis, pcomp, pobj, poss, preconj, predet, prep, prt, punct, quantmod, relcl, xcomp

Accuracy

Type Score
TAG_ACC 98.05
DEP_UAS 95.98
DEP_LAS 94.83
SENTS_P 93.80
SENTS_R 95.42
SENTS_F 94.61
TRANSFORMER_LOSS 3784861.59
TAGGER_LOSS 698704.80
PARSER_LOSS 5540167.00