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--- |
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tags: |
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- spacy |
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- token-classification |
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language: |
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- en |
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model-index: |
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- name: en_grant |
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results: |
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- task: |
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name: NER |
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type: token-classification |
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metrics: |
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- name: NER Precision |
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type: precision |
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value: 0.8150708215 |
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- name: NER Recall |
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type: recall |
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value: 0.7125309559 |
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- name: NER F Score |
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type: f_score |
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value: 0.760359408 |
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--- |
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## Introduction |
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Three variants of the model is built with Spacy3 for grant applications. |
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A simple named entity recognition custom model from scratch with annotation tool prodi.gy. |
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Github info: https://github.com/RaThorat/ner_model_prodigy |
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The most general model is 'en_grantss'. The model en_ncv is more suitable to extract entities from narrative CV's. |
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The model en_grant is the first model in the series. |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `en_grant` | |
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| **Version** | `0.0.0` | |
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| **spaCy** | `>=3.4.3,<3.5.0` | |
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| **Default Pipeline** | `tok2vec`, `ner` | |
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| **Components** | `tok2vec`, `ner` | |
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| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | |
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| **Sources** | research grant applications | |
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| **License** | n/a | |
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| **Author** | [Rahul Thorat]() | |
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### Label Scheme |
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<details> |
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<summary>View label scheme (18 labels for 1 components)</summary> |
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| Component | Labels | |
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| --- | --- | |
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| **`ner`** | `ACTIVITY`, `DISCIPLINE`, `GPE`, `JOURNAL`, `KEYWORD`, `LICENSE`, `MEDIUM`, `METASTD`, `MONEY`, `ORG`, `PERSON`, `POSITION`, `PRODUCT`, `RECOGNITION`, `REF`, `REPOSITORY`, `WEBSITE`, `YEAR` | |
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</details> |
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### Accuracy |
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| Type | Score | |
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| --- | --- | |
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| `ENTS_F` | 76.04 | |
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| `ENTS_P` | 81.51 | |
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| `ENTS_R` | 71.25 | |
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| `TOK2VEC_LOSS` | 9725604.63 | |
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| `NER_LOSS` | 930155.13 | |
