readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - renet
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model_index:
  - name: BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-renet
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: renet
        type: renet
      metric:
        name: Accuracy
        type: accuracy
        value: 0.8640646029609691
  ---
  
  # BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-renet
  
  A model for detecting gene disease associations from abstracts. The model classifies as 0 for no association, or 1 for some association.
  
  This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the [RENET2](https://github.com/sujunhao/RENET2) dataset. Note that this considers only the abstract data, and not the full text information, from RENET2. 
  
  It achieves the following results on the evaluation set:
  - Loss: 0.7226
  - Precision: 0.7799
  - Recall: 0.8211
  - F1: 0.8
  - Accuracy: 0.8641
  - Auc: 0.9325
  
  ## Training procedure
  The abstract dataset from RENET2 was split into 85% train, 15% evaluation being grouped by PMIDs and stratified by labels. That is, no data from the same PMID was seen in multiple both the training and the evaluation set.
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 1
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  
  ### Framework versions
  
  - Transformers 4.9.0.dev0
  - Pytorch 1.10.0.dev20210630+cu113
  - Datasets 1.8.0
  - Tokenizers 0.10.3
  