Edit model card

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
Downloads last month
4
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Evaluation results