RaThorat/en_ncv

Introduction

Three variants of the model is built with Spacy3 for grant applications. A simple named entity recognition custom model from scratch with annotation tool prodi.gy. Github info: https://github.com/RaThorat/ner_model_prodigy The most general model is 'en_grantss'. The model 'en_ncv' is more suitable to extract entities from narrative CV's.

Feature	Description
Name	en_ncv
Version	0.0.0
spaCy	>=3.4.3,<3.5.0
Default Pipeline	tok2vec, ner
Components	tok2vec, ner
Vectors	0 keys, 0 unique vectors (0 dimensions)
Sources	narrative CVs
License	n/a
Author	Rahul Thorat

Label Scheme

View label scheme (12 labels for 1 components)

Component	Labels
ner	ACTIVITY, GPE, KEYWORD, MEDIUM, MONEY, ORG, PERSON, POSITION, RECOGNITION, REPOSITORY, WEBSITE, YEAR

Accuracy

Type	Score
ENTS_F	66.19
ENTS_P	70.12
ENTS_R	62.67
TOK2VEC_LOSS	786695.63
NER_LOSS	965558.77