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en_pipeline

Text Classification
spaCy
English
token-classification
Eval Results
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metadata
tags:
  - spacy
  - token-classification
  - text-classification
language:
  - en
model-index:
  - name: en_pipeline
    results:
      - task:
          name: NER
          type: token-classification
        metrics:
          - name: NER Precision
            type: precision
            value: 0.9970902037
          - name: NER Recall
            type: recall
            value: 0.9970902037
          - name: NER F Score
            type: f_score
            value: 0.9970902037
      - task:
          name: TAG
          type: token-classification
        metrics:
          - name: TAG (XPOS) Accuracy
            type: accuracy
            value: 0.9993694107
      - task:
          name: POS
          type: token-classification
        metrics:
          - name: POS (UPOS) Accuracy
            type: accuracy
            value: 0.9993694107
      - task:
          name: MORPH
          type: token-classification
        metrics:
          - name: Morph (UFeats) Accuracy
            type: accuracy
            value: 0.9993120844
      - task:
          name: UNLABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Unlabeled Attachment Score (UAS)
            type: f_score
            value: 0.9979572295
      - task:
          name: LABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Labeled Attachment Score (LAS)
            type: f_score
            value: 0.9890201085
      - task:
          name: SENTS
          type: token-classification
        metrics:
          - name: Sentences F-Score
            type: f_score
            value: 1
Feature Description
Name en_pipeline
Version 0.0.0
spaCy >=3.2.3,<3.3.0
Default Pipeline tok2vec, tagger, morphologizer, parser, ner, textcat
Components tok2vec, tagger, morphologizer, parser, ner, textcat
Vectors 684830 keys, 684830 unique vectors (300 dimensions)
Sources n/a
License n/a
Author n/a

Label Scheme

View label scheme (197 labels for 5 components)
Component Labels
tagger $, '', ,, -LRB-, -RRB-, ., :, CC, CD, DT, EX, FW, HYPH, IN, JJ, JJR, JJS, MD, NFP, NN, NNP, NNPS, NNS, PDT, POS, PRP, PRP$, RB, RBR, RBS, RP, TO, UH, VB, VBD, VBG, VBN, VBP, VBZ, WDT, WP, WP$, WRB, XX, _SP, ````
morphologizer Definite=Def|POS=DET|PronType=Art, Number=Sing|POS=NOUN, POS=ADP, Degree=Pos|POS=ADJ, Number=Plur|POS=NOUN, Number=Sing|POS=PROPN, POS=PUNCT|PunctSide=Ini|PunctType=Quot, Mood=Ind|Number=Sing|POS=AUX|Person=3|Tense=Pres|VerbForm=Fin, POS=AUX|Tense=Past|VerbForm=Part, Aspect=Prog|POS=VERB|Tense=Pres|VerbForm=Part, POS=ADV, POS=VERB|Tense=Past|VerbForm=Fin, Mood=Ind|Number=Sing|POS=AUX|Person=3|Tense=Past|VerbForm=Fin, Aspect=Perf|POS=VERB|Tense=Past|VerbForm=Part, POS=PART, POS=PUNCT|PunctType=Comm, POS=PRON, POS=SCONJ, POS=VERB|VerbForm=Inf, POS=PUNCT|PunctType=Peri, Case=Nom|Number=Plur|POS=PRON|Person=3|PronType=Prs, Mood=Ind|POS=AUX|Tense=Pres|VerbForm=Fin, Degree=Cmp|POS=ADJ, ConjType=Cmp|POS=CCONJ, Number=Sing|POS=PRON|PronType=Dem, Number=Sing|POS=VERB|Person=3|Tense=Pres|VerbForm=Fin, Number=Plur|POS=PRON|Person=1|Poss=Yes|PronType=Prs, POS=SPACE, Definite=Ind|POS=DET|PronType=Art, Number=Plur|POS=PRON|Person=3|Poss=Yes|PronType=Prs, Mood=Ind|POS=AUX|Tense=Past|VerbForm=Fin, POS=PRON|PronType=Rel, Degree=Sup|POS=ADJ, POS=VERB|Tense=Pres|VerbForm=Fin, POS=AUX|VerbForm=Fin, POS=AUX|VerbForm=Inf, Case=Nom|Number=Plur|POS=PRON|Person=1|PronType=Prs, NumType=Card|POS=NUM, Number=Plur|POS=PROPN, Mood=Ind|Number=Sing|POS=VERB|Person=3|Tense=Pres|VerbForm=Fin, Degree=Sup|POS=ADV, POS=PUNCT|PunctSide=Fin|PunctType=Quot, POS=DET, Number=Plur|POS=DET|PronType=Dem, Case=Acc|Number=Plur|POS=PRON|Person=1|PronType=Prs, POS=PUNCT|PunctType=Dash, Degree=Cmp|POS=ADV, Case=Nom|Gender=Masc|Number=Sing|POS=PRON|Person=3|PronType=Prs, Number=Sing|POS=DET|PronType=Dem, POS=AUX|VerbForm=Ger, POS=AUX, Case=Nom|Gender=Neut|Number=Sing|POS=PRON|Person=3|PronType=Prs, Mood=Ind|POS=VERB|Tense=Pres|VerbForm=Fin, Case=Acc|Number=Plur|POS=PRON|Person=3|PronType=Prs, Case=Acc|Number=Plur|POS=PRON|Person=3|PronType=Prs|Reflex=Yes, Foreign=Yes|POS=X, POS=ADV|PronType=Dem, POS=PART|Polarity=Neg, Number=Plur|POS=PRON|PronType=Dem, POS=AUX|Tense=Past|VerbForm=Fin, POS=PUNCT|PunctSide=Ini|PunctType=Brck, POS=PUNCT|PunctSide=Fin|PunctType=Brck, Gender=Neut|Number=Sing|POS=PRON|Person=3|PronType=Prs, Gender=Neut|Number=Sing|POS=PRON|Person=3|Poss=Yes|PronType=Prs, Degree=Pos|POS=ADV, Mood=Ind|POS=VERB|Tense=Past|VerbForm=Fin, Gender=Masc|Number=Sing|POS=PRON|Person=3|Poss=Yes|PronType=Prs, Case=Nom|Gender=Fem|Number=Sing|POS=PRON|Person=3|PronType=Prs, POS=SYM, Number=Sing|POS=PRON|Person=1|Poss=Yes|PronType=Prs, Case=Nom|POS=PRON|Person=2|PronType=Prs, Case=Nom|Number=Sing|POS=PRON|Person=1|PronType=Prs, POS=PUNCT, Mood=Ind|Number=Sing|POS=VERB|Person=3|Tense=Past|VerbForm=Fin, Case=Acc|Number=Sing|POS=PRON|Person=1|PronType=Prs, Case=Acc|Gender=Fem|Number=Sing|POS=PRON|Person=3|PronType=Prs, POS=AUX|VerbType=Mod, POS=DET|Poss=Yes, Case=Acc|Gender=Masc|Number=Sing|POS=PRON|Person=3|PronType=Prs, Gender=Fem|Number=Sing|POS=PRON|Person=3|Poss=Yes|PronType=Prs, Number=Sing|POS=PRON|PronType=Ind, Case=Acc|Gender=Neut|Number=Sing|POS=PRON|Person=3|PronType=Prs|Reflex=Yes, Case=Acc|Gender=Neut|Number=Sing|POS=PRON|Person=3|PronType=Prs, POS=INTJ, Case=Acc|Gender=Masc|Number=Sing|POS=PRON|Person=3|PronType=Prs|Reflex=Yes, NumType=Mult|POS=ADV, POS=PRON|Person=2|Poss=Yes|PronType=Prs, Number=Sing|POS=AUX, Mood=Ind|Number=Sing|POS=AUX|Person=1|Tense=Pres|VerbForm=Fin, Case=Acc|Number=Plur|POS=PRON|Person=1|PronType=Prs|Reflex=Yes, Definite=Ind|POS=PRON|PronType=Art, Aspect=Prog|POS=AUX|Tense=Pres|VerbForm=Part, POS=X, Case=Acc|POS=PRON|Person=2|PronType=Prs
parser ROOT, acl, acomp, advcl, advmod, agent, amod, appos, attr, aux, auxpass, case, cc, ccomp, compound, conj, dep, det, dobj, expl, mark, neg, nmod, npadvmod, nsubj, nsubjpass, nummod, pcomp, pobj, poss, prep, prt, punct, quantmod, relcl, xcomp
ner CARDINAL, DATE, FAC, GPE, LANGUAGE, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK_OF_ART
textcat Blaming_Geopolitics, Blaming_Government, Blaming_Migrants, No_Frustration, Uses_Infrastructure

Accuracy

Type Score
TAG_ACC 99.94
POS_ACC 99.94
MORPH_ACC 99.93
DEP_UAS 99.80
DEP_LAS 98.90
SENTS_P 100.00
SENTS_R 100.00
SENTS_F 100.00
ENTS_F 99.71
ENTS_P 99.71
ENTS_R 99.71
CATS_SCORE 99.43
CATS_MICRO_P 99.43
CATS_MICRO_R 99.43
CATS_MICRO_F 99.43
CATS_MACRO_P 99.43
CATS_MACRO_R 99.43
CATS_MACRO_F 99.43
CATS_MACRO_AUC 100.00
CATS_MACRO_AUC_PER_TYPE 0.00
TOK2VEC_LOSS 441813.62
TAGGER_LOSS 3246.21
MORPHOLOGIZER_LOSS 3554.80
PARSER_LOSS 333496.66
NER_LOSS 6933.11
TEXTCAT_LOSS 1.50