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longluu/Clinical-NER-NCBI-Disease-GatorTronS
Clinical-NER-NCBI-Disease-GatorTronS is a token classification model from longluu. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
The model is an NER LLM algorithm that can classify each word in a text into different clinical categories.
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From the Hugging Face model README
The model is an NER LLM algorithm that can classify each word in a text into different clinical categories.
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'].
The github code associated with the model can be found here: https://github.com/longluu/LLM-NER-clinical-text.
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.
The hyperparameters are --batch_size 24 --num_train_epochs 5 --learning_rate 5e-5 --weight_decay 0.01
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.
Here we use several metrics for classification tasks including macro-average F1, precision, recall and Matthew correlation.
{'f1': 0.876008064516129, 'precision': 0.9052083333333333, 'recall': 0.8486328125}
Feel free to reach out to me at [email protected] if you have any question or suggestion.