metadata
tags:
- spacy
- token-classification
language:
- en
license: mit
model-index:
- name: en_statistics
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.853733758
- name: NER Recall
type: recall
value: 0.8456530449
- name: NER F Score
type: f_score
value: 0.8496741892
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9727831973
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9186878782
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.9005160534
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.8923519379
English pipeline that provides statistics, readability and formality scores.
| Feature | Description |
|---|---|
| Name | en_statistics |
| Version | 0.0.1 |
| spaCy | >=3.1.1,<3.2.0 |
| Default Pipeline | tok2vec, tagger, parser, attribute_ruler, lemmatizer, ner, syllables, formality, readability |
| Components | tok2vec, tagger, parser, senter, attribute_ruler, lemmatizer, ner, syllables, formality, readability |
| Vectors | 684830 keys, 20000 unique vectors (300 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) GloVe Common Crawl (Jeffrey Pennington, Richard Socher, and Christopher D. Manning) |
| License | MIT |
| Author | Contentologie |
Label Scheme
View label scheme (114 labels for 4 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, ```` |
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 |
senter |
I, S |
ner |
CARDINAL, DATE, EVENT, FAC, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK_OF_ART |
Accuracy
| Type | Score |
|---|---|
TOKEN_ACC |
99.93 |
TAG_ACC |
97.28 |
DEP_UAS |
91.87 |
DEP_LAS |
90.05 |
ENTS_P |
85.37 |
ENTS_R |
84.57 |
ENTS_F |
84.97 |
SENTS_P |
90.49 |
SENTS_R |
88.01 |
SENTS_F |
89.24 |