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zh_core_web_lg
spacy/zh_core_web_lg
zh_core_web_lg is a token classification model from spacy. 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.
Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attributeruler.
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Repo size
5.6 GB
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4
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At a glance
- Task
- Token Classification
- Library
- spacy
- License
- mit
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Oct 10, 2023
- SHA
- 39450a89
Try a prompt
Model card
From the Hugging Face model README
Details: https://spacy.io/models/zh#zh_core_web_lg
Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler.
| Feature | Description |
|---|---|
| Name | zh_core_web_lg |
| Version | 3.7.0 |
| spaCy | >=3.7.0,<3.8.0 |
| Default Pipeline | tok2vec, tagger, parser, attribute_ruler, ner |
| Components | tok2vec, tagger, parser, senter, attribute_ruler, ner |
| Vectors | 500000 keys, 500000 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)<br />CoreNLP Universal Dependencies Converter (Stanford NLP Group)<br />Explosion fastText Vectors (cbow, OSCAR Common Crawl + Wikipedia) (Explosion) |
| License | MIT |
| Author | Explosion |
Label Scheme
<details> <summary>View label scheme (100 labels for 3 components)</summary>| Component | Labels |
|---|---|
tagger | AD, AS, BA, CC, CD, CS, DEC, DEG, DER, DEV, DT, ETC, FW, IJ, INF, JJ, LB, LC, M, MSP, NN, NR, NT, OD, ON, P, PN, PU, SB, SP, URL, VA, VC, VE, VV, X, _SP |
parser | ROOT, acl, advcl:loc, advmod, advmod:dvp, advmod:loc, advmod:rcomp, amod, amod:ordmod, appos, aux:asp, aux:ba, aux:modal, aux:prtmod, auxpass, case, cc, ccomp, compound:nn, compound:vc, conj, cop, dep, det, discourse, dobj, etc, mark, mark:clf, name, neg, nmod, nmod:assmod, nmod:poss, nmod:prep, nmod:range, nmod:tmod, nmod:topic, nsubj, nsubj:xsubj, nsubjpass, nummod, parataxis:prnmod, punct, xcomp |
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 | 95.85 |
TOKEN_P | 94.58 |
TOKEN_R | 91.36 |
TOKEN_F | 92.94 |
TAG_ACC | 90.33 |
SENTS_P | 78.05 |
SENTS_R | 72.63 |
SENTS_F | 75.24 |
DEP_UAS | 70.86 |
DEP_LAS | 65.71 |
ENTS_P | 73.55 |
ENTS_R | 69.25 |
ENTS_F | 71.34 |
- Task
- Token Classification
- Library
- spacy
- License
- mit
- Languages
- zh
- Created
- Mar 2, 2022
- Updated
- Oct 10, 2023