Feature | Description |
---|---|
Name | nl_pipeline |
Version | 0.0.0 |
spaCy | >=3.6.1,<3.7.0 |
Default Pipeline | tok2vec , ner |
Components | tok2vec , ner |
Vectors | 0 keys, 0 unique vectors (0 dimensions) |
Sources | n/a |
License | n/a |
Author | n/a |
Label Scheme
View label scheme (7 labels for 1 components)
Component | Labels |
---|---|
ner |
BEDRAG , LOC , ORG , PERSOON , PROJECT , SUB , TIJD |
Accuracy
Type | Score |
---|---|
ENTS_F |
61.05 |
ENTS_P |
63.50 |
ENTS_R |
58.78 |
TOK2VEC_LOSS |
41795.01 |
NER_LOSS |
26551.93 |
Description
Voor meer info: https://github.com/RaThorat/my-chatbot-project
Prodigy (ner.manual) is gebruikt om te annoteren van entiteiten zoals: PERSOON, ORGANISATIE, PROJECT, BEDRAG, LOCATIE, TIJDSPERIODE, SUBSIDIE, PRODUCT.
prodigy ner.manual ner_dataset nl_core_news_lg ./Data/combined_documents.txt --label PERSOON,ORG,PROJECT,BEDRAG,LOC,TIJD,SUB
prodigy train ./models --ner ner_dataset --lang nl --label-stats --verbose --eval-split 0.2
30 documenten (https://github.com/RaThorat/my-chatbot-project/tree/main/Data/txt) uit de DUS-i website gedownload, schoongemaakt, samengesteld in combined_documents.txt
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Evaluation results
- NER Precisionself-reported0.635
- NER Recallself-reported0.588
- NER F Scoreself-reported0.611