metadata
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_ncv
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.7012058955
- name: NER Recall
type: recall
value: 0.626746507
- name: NER F Score
type: f_score
value: 0.6618887015
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 |