---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_subref_ner
  results:
  - task:
      name: NER
      type: token-classification
    metrics:
    - name: NER Precision
      type: precision
      value: 0.9758551308
    - name: NER Recall
      type: recall
      value: 0.9837728195
    - name: NER F Score
      type: f_score
      value: 0.9797979798
---
# Description

This model is designed to be used in conjunction with the [en_torah_ner](https://huggingface.co/Sefaria/en_torah_ner) model. See the README there for how to integrate them.

The model takes citations as input and tags the parts of the citation as entities. This is very useful for parsing the citation.

# Technical details
| Feature | Description |
| --- | --- |
| **Name** | `en_subref_ner` |
| **Version** | `1.0.0` |
| **spaCy** | `>=3.4.1,<3.5.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 218765 keys, 218765 unique vectors (50 dimensions) |
| **Sources** | n/a |
| **License** | GPLv3 |
| **Author** | Sefaria |

### Label Scheme

<details>

<summary>View label scheme (7 labels for 1 components)</summary>

| Component | Labels |
| --- | --- |
| **`ner`** | `DH`, `dir-ibid`, `ibid`, `non-cts`, `number`, `range-symbol`, `title` |

</details>

### Accuracy

| Type | Score |
| --- | --- |
| `ENTS_F` | 97.98 |
| `ENTS_P` | 97.59 |
| `ENTS_R` | 98.38 |
| `TOK2VEC_LOSS` | 5193.13 |
| `NER_LOSS` | 1103.44 |