pritamdeka / BioBert-PubMed200kRCT

BioBert-PubMed200kRCT

This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.1 on the PubMed200kRCT 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:

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:

[[{'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

Framework versions

• Transformers 4.18.0.dev0
• Pytorch 1.10.0+cu111
• Datasets 1.18.4
• Tokenizers 0.11.6

Citing & Authors

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}
}