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en_core_web_trf

Token Classification
spaCy
English
Eval Results
Model card Files
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metadata
tags:
  - spacy
  - token-classification
language:
  - en
license: MIT
model-index:
  - name: en_core_web_trf
    results:
      - tasks:
          name: NER
          type: token-classification
          metrics:
            - name: Precision
              type: precision
              value: 0.898632744
            - name: Recall
              type: recall
              value: 0.8985877404
            - name: F Score
              type: f_score
              value: 0.8986102416
      - tasks:
          name: POS
          type: token-classification
          metrics:
            - name: Accuracy
              type: accuracy
              value: 0.9779597496
      - tasks:
          name: SENTER
          type: token-classification
          metrics:
            - name: Precision
              type: precision
              value: 0.9533882851
            - name: Recall
              type: recall
              value: 0.8621940761
            - name: F Score
              type: f_score
              value: 0.9055009007
      - tasks:
          name: UNLABELED_DEPENDENCIES
          type: token-classification
          metrics:
            - name: Accuracy
              type: accuracy
              value: 0.9529693229
      - tasks:
          name: LABELED_DEPENDENCIES
          type: token-classification
          metrics:
            - name: Accuracy
              type: accuracy
              value: 0.9529693229

Details: https://spacy.io/models/en#en_core_web_trf

English transformer pipeline (roberta-base). Components: transformer, tagger, parser, ner, attribute_ruler, lemmatizer.

Feature Description
Name en_core_web_trf
Version 3.1.0
spaCy >=3.1.0,<3.2.0
Default Pipeline transformer, tagger, parser, attribute_ruler, lemmatizer, ner
Components transformer, tagger, parser, attribute_ruler, lemmatizer, ner
Vectors 0 keys, 0 unique vectors (0 dimensions)
Sources OntoNotes 5 (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston)
ClearNLP Constituent-to-Dependency Conversion (Emory University)
WordNet 3.0 (Princeton University)
roberta-base (Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov)
License MIT
Author Explosion

Label Scheme

View label scheme (112 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, 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, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK_OF_ART

Accuracy

Type Score
TOKEN_ACC 99.93
TAG_ACC 97.80
DEP_UAS 95.30
DEP_LAS 93.94
ENTS_P 89.86
ENTS_R 89.86
ENTS_F 89.86
SENTS_P 95.34
SENTS_R 86.22
SENTS_F 90.55