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 |
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Evaluation results
- NER Precisionself-reported0.701
- NER Recallself-reported0.627
- NER F Scoreself-reported0.662