Named Entity Linking model for Serbian language
| Feature |
Description |
| Name |
sr_SRPCNNEL |
| Version |
1.0.0 |
| spaCy |
>=3.5.2,<3.6.0 |
| Default Pipeline |
tok2vec, tagger, ner, sentencizer, entity_linker |
| Components |
tok2vec, tagger, ner, sentencizer, entity_linker |
| Vectors |
0 keys, 0 unique vectors (0 dimensions) |
| Sources |
n/a |
| License |
n/a |
| Author |
Milica Ikonić Nešić, Saša Petalinkar, Ranka Stanković, Miloš Utvić, Olivera Kitanović |
Label Scheme
View label scheme (23 labels for 2 components)
| Component |
Labels |
tagger |
ADJ, ADP, ADV, AUX, CCONJ, DET, INTJ, NOUN, NUM, PART, PRON, PROPN, PUNCT, SCONJ, VERB, X |
ner |
DEMO, EVENT, LOC, ORG, PERS, ROLE, WORK |
Accuracy
| Type |
Score |
TAG_ACC |
96.49 |
ENTS_F |
93.38 |
ENTS_P |
93.35 |
ENTS_R |
93.40 |
TOK2VEC_LOSS |
205472.21 |
TAGGER_LOSS |
502844.65 |
NER_LOSS |
148170.50 |
!pip install https://huggingface.co/Tanor/sr_SRPCNNEL/resolve/main/sr_SRPCNNEL-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("sr_SRPCNNEL") # Importing as module. import sr_SRPCNNEL nlp = sr_SRPCNNEL.load()