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Update spaCy pipeline
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metadata
tags:
  - spacy
  - token-classification
language:
  - en
model-index:
  - name: en_engagement_LSTM_f1
    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: 0.9362363919
Feature Description
Name en_engagement_LSTM_f1
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 ENTERTAIN, COUNTER, PROCLAIM, MONOGLOSS, DENY, ATTRIBUTION, JUSTIFYING, SOURCES, ENDOPHORIC, CITATION

Accuracy

Type Score
DEP_UAS 0.00
DEP_LAS 0.00
DEP_LAS_PER_TYPE 0.00
SENTS_P 92.71
SENTS_R 94.55
SENTS_F 93.62
TAG_ACC 0.00
ENTS_F 0.00
ENTS_P 0.00
ENTS_R 0.00
LEMMA_ACC 0.00
SPANS_SC_F 76.29
SPANS_SC_P 78.52
SPANS_SC_R 74.18
TRAINABLE_TRANSFORMER_LOSS 143.59
SPANCAT_LOSS 84050.85