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en_docusco_spacy_cd
browndw/en_docusco_spacy_cd
en_docusco_spacy_cd is a token classification model from browndw. 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 for part-of-speech and rhetorical tagging using a smaller 'common dictionary'.
All-time downloads
186
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Repo size
53.1 MB
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0
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Repo makeup
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.json11 MB · 42%
At a glance
- Task
- Token Classification
- Library
- spacy
- License
- mit
- Access
- Public
- Created
- Feb 13, 2023
- Updated
- Dec 1, 2025
- SHA
- 988b9f69
Try a prompt
Model card
From the Hugging Face model README
English pipeline for part-of-speech and rhetorical tagging using a smaller 'common dictionary'.
| Feature | Description |
|---|---|
| Name | en_docusco_spacy_cd |
| Version | 1.5 |
| spaCy | >=3.8.6,<3.9.0 |
| Default Pipeline | tok2vec, tagger, ner |
| Components | tok2vec, tagger, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | MIT |
| Author | David Brown |
Installation
pip install "en_docusco_spacy_cd @ https://huggingface.co/browndw/en_docusco_spacy_cd/resolve/main/en_docusco_spacy_cd-1.5-py3-none-any.whl"
Note that per naming requirements the wheel has the version number in place of any: en_docusco_spacy_cd-<version>-py3-none-any
Label Scheme
<details> <summary>View label scheme (293 labels for 2 components)</summary>| Component | Labels |
|---|---|
tagger | APPGE, AT, AT1, BCL21, BCL22, CC, CCB, CS, CS21, CS22, CS31, CS32, CS33, CS41, CS42, CS43, CS44, CSA, CSN, CST, CSW, CSW31, CSW32, CSW33, DA, DA1, DA2, DAR, DAT, DB, DB2, DD, DD1, DD2, DDQ, DDQGE, DDQGE31, DDQGE32, DDQGE33, DDQV, DDQV31, DDQV32, DDQV33, EX, FO, FU, FW, GE, IF, II, II21, II22, II31, II32, II33, II41, II42, II43, II44, IO, IW, JJ, JJ21, JJ22, JJ31, JJ32, JJ33, JJ41, JJ42, JJ43, JJ44, JJR, JJT, JK, MC, MC1, MC2, MC221, MC222, MCMC, MD, MF, ND1, NN, NN1, NN121, NN122, NN131, NN132, NN133, NN141, NN142, NN143, NN144, NN2, NN21, NN22, NN221, NN222, NN231, NN232, NN233, NN31, NN32, NN33, NNA, NNB, NNL1, NNL2, NNO, NNO2, NNT1, NNT131, NNT132, NNT133, NNT2, NNU, NNU1, NNU2, NNU21, NNU22, NP, NP1, NP2, NPD1, NPD2, NPM1, NPM2, PN, PN1, PN121, PN122, PN21, PN22, PNQO, PNQS, PNQS31, PNQS32, PNQS33, PNQV, PNQV31, PNQV32, PNQV33, PNX1, PPGE, PPH1, PPHO1, PPHO2, PPHS1, PPHS2, PPIO1, PPIO2, PPIS1, PPIS2, PPX1, PPX121, PPX122, PPX2, PPX221, PPX222, PPY, RA, RA21, RA22, REX, REX21, REX22, REX41, REX42, REX43, REX44, RG, RG21, RG22, RG31, RG32, RG33, RG41, RG42, RG43, RG44, RGQ, RGQV, RGQV31, RGQV32, RGQV33, RGR, RGT, RL, RL21, RL22, RL31, RL32, RL33, RP, RPK, RR, RR21, RR22, RR31, RR32, RR33, RR41, RR42, RR43, RR44, RR51, RR52, RR53, RR54, RR55, RRQ, RRQV, RRQV31, RRQV32, RRQV33, RRR, RRT, RT, RT21, RT22, RT31, RT32, RT33, RT41, RT42, RT43, RT44, TO, UH, UH21, UH22, UH31, UH32, UH33, VB0, VBDR, VBDZ, VBG, VBI, VBM, VBN, VBR, VBZ, VD0, VDD, VDG, VDI, VDN, VDZ, VH0, VHD, VHG, VHI, VHN, VHZ, VM, VM21, VM22, VMK, VV0, VVD, VVG, VVGK, VVI, VVN, VVNK, VVZ, XX, Y, ZZ1, ZZ2, ZZ221, ZZ222 |
ner | ActorsAbstractions, ActorsFirstPerson, ActorsPeople, ActorsPublicEntities, CitationAuthority, CitationControversy, CitationNeutral, ConfidenceHedged, ConfidenceHigh, OrganizationNarrative, OrganizationReasoning, PlanningFuture, PlanningStrategy, SentimentNegative, SentimentPositive, SignpostingAcademicWritingMoves, SignpostingMetadiscourse, StanceEmphatic, StanceModerated |
Accuracy
| Type | Score |
|---|---|
TAG_ACC | 97.68 |
ENTS_F | 81.76 |
ENTS_P | 82.60 |
ENTS_R | 80.95 |
TOK2VEC_LOSS | 210102533.04 |
TAGGER_LOSS | 4732599.11 |
NER_LOSS | 15047904.69 |
- Task
- Token Classification
- Library
- spacy
- License
- mit
- Languages
- en
- Created
- Feb 13, 2023
- Updated
- Dec 1, 2025