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en_pakistan_caselaw_ner
ktcole/en_pakistan_caselaw_ner
en_pakistan_caselaw_ner is a token classification model from ktcole. Use it when you need labels on individual words, such as names. It is set up for spacy. The card lists the license as mit.
English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attributeruler, lemmatizer.
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Repo size
26.4 MB
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At a glance
- Task
- Token Classification
- Library
- spacy
- License
- mit
- Access
- Public
- Created
- Nov 6, 2024
- Updated
- Nov 6, 2024
- SHA
- 185d3d95
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Model card
From the Hugging Face model README
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)<br>ClearNLP Constituent-to-Dependency Conversion (Emory University)<br>WordNet 3.0 (Princeton University) |
| License | MIT |
| Author | Kevin Cole |
Label Scheme
<details> <summary>View label scheme (116 labels for 3 components)</summary>| 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 |
- Task
- Token Classification
- Library
- spacy
- License
- mit
- Languages
- en
- Created
- Nov 6, 2024
- Updated
- Nov 6, 2024