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README.md
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license: mit
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license: mit
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# Model Card for Model longluu/Clinical-NER-NCBI-Disease-GatorTronS
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The model is an NER LLM algorithm that can classify each word in a text into different clinical categories.
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## Model Details
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### Model Description
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The base pretrained model is GatorTronS which was trained on billions of words in various clinical texts (https://huggingface.co/UFNLP/gatortronS).
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Then using the NCBI Disease dataset (https://www.sciencedirect.com/science/article/pii/S1532046413001974?via%3Dihub),
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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'].
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### Model Sources [optional]
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The github code associated with the model can be found here: https://github.com/longluu/LLM-NER-clinical-text.
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## Training Details
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### Training Data
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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.
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The preprocessed data for LLM training can be found here https://huggingface.co/datasets/ncbi_disease.
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#### Training Hyperparameters
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The hyperparameters are --batch_size 24
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--num_train_epochs 5
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--learning_rate 5e-5
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--weight_decay 0.01
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Testing Data
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The model was trained and validated on train and validation sets. Then it was tested on a separate test set.
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Note that some concepts in the test set were not available in the train and validatin sets.
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#### Metrics
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Here we use several metrics for classification tasks including macro-average F1, precision, recall and Matthew correlation.
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### Results
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{'f1': 0.9230959441861525,
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'precision': 0.8998375309216448,
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'recall': 0.948772382840148,
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'matthews_correlation': 0.8978492834665438}
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## Model Card Contact
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Feel free to reach out to me at thelong20.4@gmail.com if you have any question or suggestion.
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