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en_NER_Features

Token Classification
spaCy
English
Eval Results
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en_NER_Features / README.md
Ramsha Ali
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metadata
tags:
  - spacy
  - token-classification
language:
  - en
license: mit
model-index:
  - name: en_NER_Features
    results:
      - task:
          name: NER
          type: token-classification
        metrics:
          - name: NER Precision
            type: precision
            value: 0.8454836771
          - name: NER Recall
            type: recall
            value: 0.8456530449
          - name: NER F Score
            type: f_score
            value: 0.8455683525
      - task:
          name: TAG
          type: token-classification
        metrics:
          - name: TAG (XPOS) Accuracy
            type: accuracy
            value: 0.97246532
      - task:
          name: UNLABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Unlabeled Attachment Score (UAS)
            type: f_score
            value: 0.9175304332
      - task:
          name: LABELED_DEPENDENCIES
          type: token-classification
        metrics:
          - name: Labeled Attachment Score (LAS)
            type: f_score
            value: 0.89874821
      - task:
          name: SENTS
          type: token-classification
        metrics:
          - name: Sentences F-Score
            type: f_score
            value: 0.9059485531

English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.Named Entity Recognition model trained on Google Play Store app descriptions to automatically identify app features from app description.

Feature Description
Name en_NER_Features
Version 3.7.1
spaCy >=3.7.2,<3.8.0
Default Pipeline tok2vec, tagger, parser, attribute_ruler, lemmatizer, ner
Components tok2vec, tagger, parser, senter, 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)
License MIT
Author Explosion

Label Scheme

View label scheme (115 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, _SP, ````
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 AppName, CARDINAL, DATE, EVENT, FAC, FunctionalFeature, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK_OF_ART

Accuracy

Type Score
TOKEN_ACC 99.86
TOKEN_P 99.57
TOKEN_R 99.58
TOKEN_F 99.57
TAG_ACC 97.25
SENTS_P 92.02
SENTS_R 89.21
SENTS_F 90.59
DEP_UAS 91.75
DEP_LAS 89.87
ENTS_P 84.55
ENTS_R 84.57
ENTS_F 84.56