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---
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 | |