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en_grant
RaThorat/en_grant
en_grant 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…
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At a glance
- Task
- Token Classification
- Library
- spacy
- Access
- Public
- Created
- Dec 15, 2022
- Updated
- Nov 13, 2023
- SHA
- 54135c31
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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. 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
<details> <summary>View label scheme (18 labels for 1 components)</summary>| 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 |
- Task
- Token Classification
- Library
- spacy
- Languages
- en
- Created
- Dec 15, 2022
- Updated
- Nov 13, 2023