readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  widget:
  - text: "SAMPLE 32,441 archived appendix samples fixed in formalin and embedded in paraffin and tested for the presence of abnormal prion protein (PrP)."
  model-index:
  - name: BioBert-PubMed200kRCT
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # BioBert-PubMed200kRCT
  
  This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) on the [PubMed200kRCT](https://github.com/Franck-Dernoncourt/pubmed-rct/tree/master/PubMed_200k_RCT) dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2832
  - Accuracy: 0.8934
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  The model can be used for text classification tasks of Randomized Controlled Trials that does not have any structure. The text can be classified as one of the following:
  * BACKGROUND
  * CONCLUSIONS
  * METHODS
  * OBJECTIVE
  * RESULTS
  
  The model can be directly used like this:
  
  ```python
  from transformers import TextClassificationPipeline
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  model = AutoModelForSequenceClassification.from_pretrained("pritamdeka/BioBert-PubMed200kRCT")
  tokenizer = AutoTokenizer.from_pretrained("pritamdeka/BioBert-PubMed200kRCT")
  pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
  pipe("Treatment of 12 healthy female subjects with CDCA for 2 days resulted in increased BAT activity.")
  ```
  Results will be shown as follows:
  
  ```python
  [[{'label': 'BACKGROUND', 'score': 0.0027583304326981306},
    {'label': 'CONCLUSIONS', 'score': 0.044541116803884506},
    {'label': 'METHODS', 'score': 0.19493348896503448},
    {'label': 'OBJECTIVE', 'score': 0.003996663726866245},
    {'label': 'RESULTS', 'score': 0.7537703514099121}]]
  ```
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 64
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 2.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.3587        | 0.14  | 5000  | 0.3137          | 0.8834   |
  | 0.3318        | 0.29  | 10000 | 0.3100          | 0.8831   |
  | 0.3286        | 0.43  | 15000 | 0.3033          | 0.8864   |
  | 0.3236        | 0.58  | 20000 | 0.3037          | 0.8862   |
  | 0.3182        | 0.72  | 25000 | 0.2939          | 0.8876   |
  | 0.3129        | 0.87  | 30000 | 0.2910          | 0.8885   |
  | 0.3078        | 1.01  | 35000 | 0.2914          | 0.8887   |
  | 0.2791        | 1.16  | 40000 | 0.2975          | 0.8874   |
  | 0.2723        | 1.3   | 45000 | 0.2913          | 0.8906   |
  | 0.2724        | 1.45  | 50000 | 0.2879          | 0.8904   |
  | 0.27          | 1.59  | 55000 | 0.2874          | 0.8911   |
  | 0.2681        | 1.74  | 60000 | 0.2848          | 0.8928   |
  | 0.2672        | 1.88  | 65000 | 0.2832          | 0.8934   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0.dev0
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.4
  - Tokenizers 0.11.6
  
  
  ## Citing & Authors
  
  <!--- Describe where people can find more information -->
  
  If you use the model kindly cite the following work
  
  ```
  @inproceedings{deka2022evidence,
    title={Evidence Extraction to Validate Medical Claims in Fake News Detection},
    author={Deka, Pritam and Jurek-Loughrey, Anna and others},
    booktitle={International Conference on Health Information Science},
    pages={3--15},
    year={2022},
    organization={Springer}
  }
  ```