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README.md

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+# 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 GatorTron-base which was trained on billions of words in various clinical texts (https://huggingface.co/UFNLP/gatortron-base). 
+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 4
+                        --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': 0.6271402249699903,
+ 'precision': 0.6691625224055963,
+ 'recall': 0.6085333637974402,
+ 'matthews_correlation': 0.720898121696139}
+
+## Model Card Contact
+Feel free to reach out to me at [email protected] if you have any question or suggestion.