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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_kyc_nerre |
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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: 0.6862745098 |
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- name: NER Recall |
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type: recall |
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value: 0.7954545455 |
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- name: NER F Score |
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type: f_score |
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value: 0.7368421053 |
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--- |
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| Feature | Description | Note |
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| --- | --- | --- | |
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| **Name** | `en_kyc_nerre` || |
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| **Version** | `0.0.0` | test run, official version will start from 1.xx.xx| |
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| **spaCy** | `>=3.6.1,<3.7.0` || |
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| **Default Pipeline** | `transformer`, `ner` || |
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| **Components** | `transformer`, `ner` || |
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| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) || |
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| **Sources** | n/a || |
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| **License** | n/a || |
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| **Author** | hjiangAnthony || |
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### Purpose |
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Identifying PERSON, CRIME, and PROCECUTION entities; supporting relation extraction for the next step |
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### Label Scheme |
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<details> |
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<summary>View label scheme (3 labels for 1 components)</summary> |
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| Component | Labels | |
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| --- | --- | |
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| **`ner`** | `CRIME`, `PERSON`, `PROCECUTION` | |
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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` | 73.68 | |
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| `ENTS_P` | 68.63 | |
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| `ENTS_R` | 79.55 | |
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| `TRANSFORMER_LOSS` | 12977.28 | |
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| `NER_LOSS` | 94024.87 | |
