---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_kyc_nerre
  results:
  - task:
      name: NER
      type: token-classification
    metrics:
    - name: NER Precision
      type: precision
      value: 0.6862745098
    - name: NER Recall
      type: recall
      value: 0.7954545455
    - name: NER F Score
      type: f_score
      value: 0.7368421053
---
| Feature | Description | Note
| --- | --- | --- |
| **Name** | `en_kyc_nerre` ||
| **Version** | `0.0.0` | test run, official version will start from 1.xx.xx|
| **spaCy** | `>=3.6.1,<3.7.0` ||
| **Default Pipeline** | `transformer`, `ner` ||
| **Components** | `transformer`, `ner` ||
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) ||
| **Sources** | n/a ||
| **License** | n/a ||
| **Author** | hjiangAnthony ||

### Purpose
Identifying PERSON, CRIME, and PROCECUTION entities; supporting relation extraction for the next step

### Label Scheme

<details>

<summary>View label scheme (3 labels for 1 components)</summary>

| Component | Labels |
| --- | --- |
| **`ner`** | `CRIME`, `PERSON`, `PROCECUTION` |

</details>

### Accuracy

| Type | Score |
| --- | --- |
| `ENTS_F` | 73.68 |
| `ENTS_P` | 68.63 |
| `ENTS_R` | 79.55 |
| `TRANSFORMER_LOSS` | 12977.28 |
| `NER_LOSS` | 94024.87 |