Details: https://spacy.io/models/zh#zh_core_web_sm
File checksum:
3e6152041b1488763bbfe9f5bcf94be4fac1a8ee2a54b9a7eeac2be9c27cc727
Chinese multi-task CNN trained on OntoNotes. Assigns context-specific token vectors, POS tags, dependency parse and named entities.
| Feature | Description |
|---|---|
| Name | zh_core_web_sm
|
| Version | 2.3.0
|
| spaCy | >=2.3.0,<2.4.0
|
| Model size | 45 MB |
| Pipeline | tagger, parser, ner
|
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | OntoNotes 5 |
| License | MIT
|
| Author | Explosion |
Label Scheme
| 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 |
|---|---|
LAS
| 63.21 |
UAS
| 68.55 |
TOKEN_ACC
| 94.58 |
TAGS_ACC
| 89.63 |
ENTS_F
| 66.57 |
ENTS_P
| 69.58 |
ENTS_R
| 63.80 |
Installation
pip install spacy
python -m spacy download zh_core_web_sm