Details: https://spacy.io/models/de#de_core_news_sm
File checksum:
87fe081677c54615b8f5b3e701b8279c929dc9b5ed2aed1545e2494b5cae8b01
German multi-task CNN trained on the TIGER and WikiNER corpora. Assigns context-specific token vectors, POS tags, dependency parses and named entities.
| Feature | Description |
|---|---|
| Name | de_core_news_sm
|
| Version | 2.3.0
|
| spaCy | >=2.3.0,<2.4.0
|
| Model size | 14 MB |
| Pipeline | tagger, parser, ner
|
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | TIGER Corpus WikiNER |
| License | MIT
|
| Author | Explosion |
Label Scheme
| Component | Labels |
|---|---|
tagger
| $(, $,, $., ADJA, ADJD, ADV, APPO, APPR, APPRART, APZR, ART, CARD, FM, ITJ, KOKOM, KON, KOUI, KOUS, NE, NN, NNE, PDAT, PDS, PIAT, PIS, PPER, PPOSAT, PPOSS, PRELAT, PRELS, PRF, PROAV, PTKA, PTKANT, PTKNEG, PTKVZ, PTKZU, PWAT, PWAV, PWS, TRUNC, VAFIN, VAIMP, VAINF, VAPP, VMFIN, VMINF, VMPP, VVFIN, VVIMP, VVINF, VVIZU, VVPP, XY, _SP
|
parser
| ROOT, ac, adc, ag, ams, app, avc, cc, cd, cj, cm, cp, cvc, da, dep, dm, ep, ju, mnr, mo, ng, nk, nmc, oa, oc, og, op, par, pd, pg, ph, pm, pnc, punct, rc, re, rs, sb, sbp, svp, uc, vo
|
ner
| LOC, MISC, ORG, PER
|
Accuracy
| Type | Score |
|---|---|
LAS
| 90.13 |
UAS
| 92.32 |
TOKEN_ACC
| 99.92 |
TAGS_ACC
| 97.53 |
ENTS_F
| 83.35 |
ENTS_P
| 83.92 |
ENTS_R
| 82.78 |
Because the model is trained on Wikipedia, it may perform inconsistently on many genres, such as social media text. The NER accuracy refers to the "silver standard" annotations in the WikiNER corpus. Accuracy on these annotations tends to be higher than correct human annotations.
Installation
pip install spacy
python -m spacy download de_core_news_sm