README.md

metadata

tags:
  - spacy
  - token-classification
language:
  - en
model-index:
  - name: en_engagement_LSTM
    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: 0.9144831558

---

tags: - spacy - token-classification language: - en model-index: - name: en_engagement_LSTM results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.0 - name: NER Recall type: recall value: 0.0 - name: NER F Score type: f_score value: 0.0 - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.0 - task: name: LEMMA type: token-classification metrics: - name: Lemma Accuracy type: accuracy value: 0.0 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.0 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.0 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 0.9144831558

Feature	Description
Name	en_engagement_LSTM
Version	1.1.7
spaCy	>=3.4.4,<4
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, ENTERTAIN, PROCLAIM, SOURCES, MONOGLOSS, CITATION, ENDOPHORIC, DENY, JUSTIFYING, COUNTER

Accuracy

Type	Score
DEP_UAS	0.00
DEP_LAS	0.00
DEP_LAS_PER_TYPE	0.00
SENTS_P	89.82
SENTS_R	93.14
SENTS_F	91.45
TAG_ACC	0.00
ENTS_F	0.00
ENTS_P	0.00
ENTS_R	0.00
LEMMA_ACC	0.00
SPANS_SC_F	77.22
SPANS_SC_P	79.33
SPANS_SC_R	75.22
TRAINABLE_TRANSFORMER_LOSS	885.71
SPANCAT_LOSS	104829.66