README.md

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license: mit
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# Model Card for Model longluu/Clinical-NER-NCBI-Disease-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 NCBI Disease dataset (https://www.sciencedirect.com/science/article/pii/S1532046413001974?via%3Dihub), 
I fine-tuned the model for NER task in which the model can classify each word in a text into one of the categories ['no disease', 'disease', 'disease-continue'].

### 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
This dataset contains the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. Details are here https://www.sciencedirect.com/science/article/pii/S1532046413001974?via%3Dihub.

The preprocessed data for LLM training can be found here https://huggingface.co/datasets/ncbi_disease.

#### Training Hyperparameters

The hyperparameters are --batch_size 24
                        --num_train_epochs 5
                        --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': 0.9230959441861525,
 'precision': 0.8998375309216448,
 'recall': 0.948772382840148,
 'matthews_correlation': 0.8978492834665438}

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