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---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_grant
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8150708215
- name: NER Recall
type: recall
value: 0.7125309559
- name: NER F Score
type: f_score
value: 0.760359408
---
## 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` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 76.04 |
| `ENTS_P` | 81.51 |
| `ENTS_R` | 71.25 |
| `TOK2VEC_LOSS` | 9725604.63 |
| `NER_LOSS` | 930155.13 | |