readme: ---
  tags:
  - spacy
  - token-classification
  language:
  - en
  model-index:
  - name: en_student_name_detector
    results:
    - task:
        name: NER
        type: token-classification
      metrics:
      - name: NER Precision
        type: precision
        value: 0.8311688312
      - name: NER Recall
        type: recall
        value: 0.8421052632
      - name: NER F Score
        type: f_score
        value: 0.8366013072
  ---
  | Feature | Description |
  | --- | --- |
  | **Name** | `en_student_name_detector` |
  | **Version** | `0.0.1` |
  | **spaCy** | `>=3.4.1,<3.5.0` |
  | **Default Pipeline** | `transformer`, `ner` |
  | **Components** | `transformer`, `ner` |
  | **Sources** | [longformer](https://huggingface.co/allenai/longformer-base-4096) |
  | **License** | [Apache 2.0](https://huggingface.co/langdonholmes/en_student_name_detector/blob/main/LICENSE) |
  | **Author** | [Langdon Holmes](https://huggingface.co/langdonholmes) |
  
  ### Label Scheme
  
  <details>
  
  <summary>View label scheme (1 labels for 1 components)</summary>
  
  | Component | Labels |
  | --- | --- |
  | **`ner`** | `STUDENT` |
  
  </details>
  
  ### Accuracy
  
  | Type | Score |
  | --- | --- |
  | `ENTS_F` | 83.66 |
  | `ENTS_P` | 83.12 |
  | `ENTS_R` | 84.21 |
  | `TRANSFORMER_LOSS` | 56255026.35 |
  | `NER_LOSS` | 31154.89 |
  
  
  ### Training Data
  
  6,293 student writing assignments were submitted as PDF files. All documents were reflection assignments in response to the same prompt in the same online course. Student names were labeled by human raters (one rater per document). A preliminary model was trained and all disagreements between this model and the human annotations were adjudicated by two additional reviewers. The training dataset includes all 6,293 documents, 845 of which include student names. There are 1,155 student name annotations in total.
  
  ### To Use
  
  This model has been packaged using spaCy. It is available as a huggingface model or a pip package. Performance of the model should be evaluated on in-domain data before deployment in production, particularly when confidential information is involved.