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_grantss |
| 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, EVENT, GPE, JOURNAL, KEYWORD, LICENSE, MEDIUM, METASTD, MONEY, ORG, PERSON, POSITION, PRODUCT, RECOGNITION, REF, REPOSITORY, WEBSITE |
Accuracy
| Type |
Score |
ENTS_F |
71.14 |
ENTS_P |
76.91 |
ENTS_R |
66.18 |
TOK2VEC_LOSS |
1412244.09 |
NER_LOSS |
1039417.96 |
