Downloads · 30 days
8
5% of all-time downloads
en_ncv
RaThorat/en_ncv
en_ncv is a token classification model from RaThorat. Use it when you need labels on individual words, such as names. It is set up for spacy.
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/nermodelprod…
All-time downloads
174
Public
Repo size
12.2 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
Other6.2 MB · 44%
At a glance
- Task
- Token Classification
- Library
- spacy
- Access
- Public
- Created
- Dec 15, 2022
- Updated
- Nov 13, 2023
- SHA
- 0afc742a
Try a prompt
Model card
From the Hugging Face model README
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
<details> <summary>View label scheme (12 labels for 1 components)</summary>| 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 |
- Task
- Token Classification
- Library
- spacy
- Languages
- en
- Created
- Dec 15, 2022
- Updated
- Nov 13, 2023