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---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_pipeline
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9175338189
- name: NER Recall
type: recall
value: 0.9087863953
- name: NER F Score
type: f_score
value: 0.9131391586
---
This model was trained with spaCy (distilbert-base-uncased transformer) to perform NER on resumes.
| Feature | Description |
| --- | --- |
| **Name** | `en_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.7.2,<3.8.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (4 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `COMPANY`, `DIPLOMA`, `JOB_TITLE`, `SKILL` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 91.31 |
| `ENTS_P` | 91.75 |
| `ENTS_R` | 90.88 |
|