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---
tags:
- spacy
- token-classification
language:
- he
model-index:
- name: he_subref_ner
  results:
  - task:
      name: NER
      type: token-classification
    metrics:
    - name: NER Precision
      type: precision
      value: 0.9611848825
    - name: NER Recall
      type: recall
      value: 0.9651282051
    - name: NER F Score
      type: f_score
      value: 0.9631525077
---
# Description

This model is designed to be used in conjunction with the [he-ref-ner](https://huggingface.co/Sefaria/he_ref_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** | `he_subref_ner` |
| **Version** | `1.0.0` |
| **spaCy** | `>=3.4.1,<3.5.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 394654 keys, 394654 unique vectors (50 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |

### Label Scheme

<details>

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

| Component | Labels |
| --- | --- |
| **`ner`** | `讚讛`, `讻讜转专转`, `诇讗-专爪讬祝`, `诇拽诪谉-诇讛诇谉`, `诪住驻专`, `住讬诪谉-讟讜讜讞`, `砖诐` |

</details>

### Accuracy

| Type | Score |
| --- | --- |
| `ENTS_F` | 96.32 |
| `ENTS_P` | 96.12 |
| `ENTS_R` | 96.51 |
| `TOK2VEC_LOSS` | 11226.82 |
| `NER_LOSS` | 2452.62 |