metadata
tags:
- spacy
- token-classification
language:
- nl
model-index:
- name: nl_ner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.5
- name: NER Recall
type: recall
value: 0.4
- name: NER F Score
type: f_score
value: 0.4444444444
| Feature | Description |
|---|---|
| Name | nl_ner |
| 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 |
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.
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 0.1
46 documenten (https://github.com/RaThorat/my-chatbot-project/tree/main/Data/txt) uit de DUS-i website gedownload, schoongemaakt, samengesteld in combined_documents.txt
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 |
44.44 |
ENTS_P |
50.00 |
ENTS_R |
40.00 |
TOK2VEC_LOSS |
6462.80 |
NER_LOSS |
14799.70 |