Downloads · 30 days
18
15% of all-time downloads
zh_data_dev_spacy_trf_1
sigtica/zh_data_dev_spacy_trf_1
zh_data_dev_spacy_trf_1 is a token classification model from sigtica. 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 spacy model, based on the spacy stock zhcorewebtrf transformer-based model, used for regular day to day data engineering.
All-time downloads
120
Public
Repo size
887 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.whl415 MB · 47%
At a glance
- Task
- Token Classification
- Library
- spacy
- License
- mit
- Access
- Public
- Created
- Jul 19, 2024
- Updated
- Jul 19, 2024
- SHA
- 2d57c7ea
Try a prompt
Model card
From the Hugging Face model README
zh_data_dev_spacy_trf_1
Chinese spacy model, based on the spacy stock zh_core_web_trf transformer-based model, used for regular day to day data engineering.
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
- Jul 19, 2024
- Updated
- Jul 19, 2024