You can deploy this model and start earning money today!

RaThorat/
en_ncv

Name: en_ncv
Rating: 2.5 (10 reviews)

spacy

token-classification

model-index

Views

No views yet

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

RaThorat/en_ncv

Introduction

Label Scheme

Accuracy

RaThorat/
en_ncv