---
license: mit
---

# Model Card for Model longluu/Clinical-NER-MedMentions-GatorTronS
The model is an NER LLM algorithm that can classify each word in a text into different clinical categories. 

## Model Details

### Model Description
The base pretrained model is GatorTronS which was trained on billions of words in various clinical texts (https://huggingface.co/UFNLP/gatortronS). 
Then using the MedMentions dataset (https://arxiv.org/pdf/1902.09476v1.pdf), I fine-tuned the model for NER task in which the model can classify each word in a text into different clinical categories.
The category system is a simplified version of UMLS concept system and consists of 21 categories: 
"['Living Beings', 'Virus']", "['Living Beings', 'Bacterium']", "['Anatomy', 'Anatomical Structure']", "['Anatomy', 'Body System']", "['Anatomy', 'Body Substance']", "['Disorders', 'Finding']", "['Disorders', 'Injury or Poisoning']", "['Phenomena', 'Biologic Function']", "['Procedures', 'Health Care Activity']", "['Procedures', 'Research Activity']", "['Devices', 'Medical Device']", "['Concepts & Ideas', 'Spatial Concept']", "['Occupations', 'Biomedical Occupation or Discipline']", "['Organizations', 'Organization']", "['Living Beings', 'Professional or Occupational Group']", "['Living Beings', 'Population Group']", "['Chemicals & Drugs', 'Chemical']", "['Objects', 'Food']", "['Concepts & Ideas', 'Intellectual Product']", "['Physiology', 'Clinical Attribute']", "['Living Beings', 'Eukaryote']", 'None'

### Model Sources [optional]
The github code associated with the model can be found here: https://github.com/longluu/LLM-NER-clinical-text.

## Training Details

### Training Data

The MedMentions dataset contain 4,392 abstracts released in PubMed®1 between January 2016 and January 2017. The abstracts were manually annotated for biomedical concepts. Details are provided in https://arxiv.org/pdf/1902.09476v1.pdf and data is in https://github.com/chanzuckerberg/MedMentions. 
[More Information Needed]

#### Training Hyperparameters

The hyperparameters are --batch_size 6
                        --num_train_epochs 6 
                        --learning_rate 5e-5 
                        --weight_decay 0.01 

## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
The model was trained and validated on train and validation sets. Then it was tested on a separate test set. 
Note that some concepts in the test set were not available in the train and validatin sets.

#### Metrics
Here we use several metrics for classification tasks including macro-average F1, precision, recall and Matthew correlation.

### Results
{'f1': 0.63101050849362,
 'precision': 0.6714479559316552,
 'recall': 0.6145681950682534,
 'matthews_correlation': 0.7203455851532575}

## Model Card Contact
Feel free to reach out to me at thelong20.4@gmail.com if you have any question or suggestion.