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zh_core_web_trf
spacy/zh_core_web_trf
zh_core_web_trf 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 transformer pipeline (Transformer(name='bert-base-chinese', pieceencoder='bert-wordpiece', stride=152, type='bert', width=768, window=208, vocabsize=21128)). Components: transformer, tagger, parser, ner, attri…
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Repo size
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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
- c70655e8
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Model card
From the Hugging Face model README
Details: https://spacy.io/models/zh#zh_core_web_trf
Chinese transformer pipeline (Transformer(name='bert-base-chinese', piece_encoder='bert-wordpiece', stride=152, type='bert', width=768, window=208, vocab_size=21128)). Components: transformer, tagger, parser, ner, attribute_ruler.
| Feature | Description |
|---|---|
| Name | zh_core_web_trf |
| Version | 3.7.2 |
| spaCy | >=3.7.0,<3.8.0 |
| Default Pipeline | transformer, tagger, parser, attribute_ruler, ner |
| Components | transformer, tagger, parser, attribute_ruler, 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 />CoreNLP Universal Dependencies Converter (Stanford NLP Group)<br />bert-base-chinese (Hugging Face) |
| License | MIT |
| Author | Explosion |
Label Scheme
<details> <summary>View label scheme (99 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 |
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 | 91.75 |
SENTS_P | 70.92 |
SENTS_R | 67.57 |
SENTS_F | 69.21 |
DEP_UAS | 75.72 |
DEP_LAS | 71.45 |
ENTS_P | 76.09 |
ENTS_R | 72.18 |
ENTS_F | 74.08 |
- Task
- Token Classification
- Library
- spacy
- License
- mit
- Languages
- zh
- Created
- Mar 2, 2022
- Updated
- Oct 10, 2023