readme: ---
  license: mit
  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: PubMedBert-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. -->
  
  # PubMedBert-PubMed200kRCT
  
  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 [PubMed200kRCT](https://github.com/Franck-Dernoncourt/pubmed-rct/tree/master/PubMed_200k_RCT) dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2833
  - Accuracy: 0.8942
  
  ## 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/PubMedBert-PubMed200kRCT")
  tokenizer = AutoTokenizer.from_pretrained("pritamdeka/PubMedBert-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': 0.0028450002428144217},
    {'label': 'CONCLUSIONS', 'score': 0.2581048607826233},
    {'label': 'METHODS', 'score': 0.015086210332810879},
    {'label': 'OBJECTIVE', 'score': 0.0016815993003547192},
    {'label': 'RESULTS', 'score': 0.7222822904586792}]]
  ```
  
  ## 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
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.3604        | 0.14  | 5000  | 0.3162          | 0.8821   |
  | 0.3326        | 0.29  | 10000 | 0.3112          | 0.8843   |
  | 0.3293        | 0.43  | 15000 | 0.3044          | 0.8870   |
  | 0.3246        | 0.58  | 20000 | 0.3040          | 0.8871   |
  | 0.32          | 0.72  | 25000 | 0.2969          | 0.8888   |
  | 0.3143        | 0.87  | 30000 | 0.2929          | 0.8903   |
  | 0.3095        | 1.01  | 35000 | 0.2917          | 0.8899   |
  | 0.2844        | 1.16  | 40000 | 0.2957          | 0.8886   |
  | 0.2778        | 1.3   | 45000 | 0.2943          | 0.8906   |
  | 0.2779        | 1.45  | 50000 | 0.2890          | 0.8935   |
  | 0.2752        | 1.59  | 55000 | 0.2881          | 0.8919   |
  | 0.2736        | 1.74  | 60000 | 0.2835          | 0.8944   |
  | 0.2725        | 1.88  | 65000 | 0.2833          | 0.8942   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0.dev0
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - 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}
  }
  ```
  