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Update spaCy pipeline
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metadata
tags:
  - spacy
  - token-classification
language:
  - en
model-index:
  - name: en_engagement_LSTM_f2
    results:
      - task:
          name: NER
          type: token-classification
        metrics:
          - name: NER Precision
            type: precision
            value: 0
          - name: NER Recall
            type: recall
            value: 0
          - name: NER F Score
            type: f_score
            value: 0
      - 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: 0.9228764982
Feature Description
Name en_engagement_LSTM_f2
Version 1.0.0
spaCy >=3.4.4,<3.5.0
Default Pipeline transformer, parser, tagger, ner, attribute_ruler, lemmatizer, trainable_transformer, spancat
Components transformer, parser, tagger, ner, attribute_ruler, lemmatizer, trainable_transformer, spancat
Vectors 0 keys, 0 unique vectors (0 dimensions)
Sources n/a
License n/a
Author n/a

Label Scheme

View label scheme (122 labels for 4 components)
Component Labels
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
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, ````
ner CARDINAL, DATE, EVENT, FAC, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK_OF_ART
spancat ATTRIBUTION, COUNTER, SOURCES, CITATION, DENY, ENTERTAIN, MONOGLOSS, PROCLAIM, ENDOPHORIC, JUSTIFYING

Accuracy

Type Score
DEP_UAS 0.00
DEP_LAS 0.00
DEP_LAS_PER_TYPE 0.00
SENTS_P 91.43
SENTS_R 93.16
SENTS_F 92.29
TAG_ACC 0.00
ENTS_F 0.00
ENTS_P 0.00
ENTS_R 0.00
LEMMA_ACC 0.00
SPANS_SC_F 74.80
SPANS_SC_P 75.85
SPANS_SC_R 73.77
TRAINABLE_TRANSFORMER_LOSS 226.60
SPANCAT_LOSS 81318.13