de_core_news_sm-2.2.0

Details: https://spacy.io/models/de#de_core_news_sm

File checksum: 8b79574382b1e06b24f67e76d652d60d00750405c23e7a078dc4cc53aad5e219

German multi-task CNN trained on the TIGER and WikiNER corpus. Assigns context-specific token vectors, POS tags, dependency parse and named entities. Supports identification of PER, LOC, ORG and MISC entities.

Feature	Description
Name	de_core_news_sm
Version	2.2.0
spaCy	>=2.2.0
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	88.63
UAS	90.75
TOKEN_ACC	95.88
TAGS_ACC	96.29
ENTS_F	83.11
ENTS_P	83.57
ENTS_R	82.66

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