---
tags:
- spacy
- token-classification
language:
- en
license: mit
model-index:
- name: en_biobert_ner_symptom
  results:
  - task:
      name: NER
      type: token-classification
    metrics:
    - name: NER Precision
      type: precision
      value: 0.9997017596
    - name: NER Recall
      type: recall
      value: 0.9994036971
    - name: NER F Score
      type: f_score
      value: 0.9995527061
widget:
- text: "Patient X reported coughing and sneezing."
  example_title: "Example 1"
- text: "There was a case of rash and inflammation."
  example_title: "Example 2"
- text: "He complained of dizziness during the trip."
  example_title: "Example 3"
- text: "I felt distressed , giddy and nauseous during my stay in Florida."
  example_title: "Example 4"
- text: "Mr. Y complained of breathlesness and chest pain when he was driving back to his house."
  example_title: "Example 5"
---
BioBERT based NER model for medical symptoms

| Feature | Description |
| --- | --- |
| **Name** | `en_biobert_ner_symptom` |
| **Version** | `1.0.0` |
| **spaCy** | `>=3.5.1,<3.6.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | `MIT` |
| **Author** | [Sena Chae, Pratik Maitra, Padmini Srinivasan]() |



<em> Model Description </em>

The model was trained on a combined maccrobat and i2c2 dataset and is based on biobert. If you use this model kindly cite the paper below:

<em>
<b>Developing a BioBERT-based Natural Language Processing Algorithm for Acute Myeloid Leukemia Symptoms Identification from Clinical Notes - Sena Chae , Pratik Maitra , Padmini Srinivasan</b>
</em>

<em> How to use the Model </em>

<div class="wrapper">
    <span class="inner">

from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")
model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")

pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu
pipe("""The patient reported no recurrence of palpitations at follow-up 6 months after the ablation.""")

</span>
</div>
### Accuracy

| Type | Score |
| --- | --- |
| `ENTS_F` | 99.96 |
| `ENTS_P` | 99.97 |
| `ENTS_R` | 99.94 |
| `TRANSFORMER_LOSS` | 20456.83 |
| `NER_LOSS` | 38920.06 |