metadata
tags:
- spacy
- token-classification
language:
- en
license: mit
model-index:
- name: en_pakistan_caselaw_ner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8429743795
- name: NER Recall
type: recall
value: 0.8436498397
- name: NER F Score
type: f_score
value: 0.8433119744
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.972937262
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9176955214
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.8991533598
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9068156724
English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
|---|---|
| Name | en_pakistan_caselaw_ner |
| Version | 3.8.0 |
| spaCy | >=3.8.0,<3.9.0 |
| 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 | Kevin Cole |
Label Scheme
View label scheme (116 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 |
CARDINAL, CASE_NUMBER, DATE, EVENT, FAC, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PETITIONER, PRODUCT, QUANTITY, RESPONDENT, 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 |
97.29 |
SENTS_P |
92.01 |
SENTS_R |
89.39 |
SENTS_F |
90.68 |
DEP_UAS |
91.77 |
DEP_LAS |
89.92 |
ENTS_P |
84.30 |
ENTS_R |
84.36 |
ENTS_F |
84.33 |