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RaThorat/
en_grant

Name: en_grant
Availability: InStock

spacy

token-classification

model-index

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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. The model en_grant is the first model in the series.

Feature	Description
Name	`en_grant`
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	research grant applications
License	n/a
Author	Rahul Thorat

Label Scheme

View label scheme (18 labels for 1 components)

Component	Labels
`ner`	`ACTIVITY`, `DISCIPLINE`, `GPE`, `JOURNAL`, `KEYWORD`, `LICENSE`, `MEDIUM`, `METASTD`, `MONEY`, `ORG`, `PERSON`, `POSITION`, `PRODUCT`, `RECOGNITION`, `REF`, `REPOSITORY`, `WEBSITE`, `YEAR`

Accuracy

Type	Score
`ENTS_F`	76.04
`ENTS_P`	81.51
`ENTS_R`	71.25
`TOK2VEC_LOSS`	9725604.63
`NER_LOSS`	930155.13

RaThorat/en_grant

Introduction

Label Scheme

Accuracy

RaThorat/
en_grant