Edit model card

IMFBERT is built by fine-tuning the siebert/sentiment-roberta-large-english model with IMF (International Monetary Fund) Executive Board meeting minutes (around 150,000 sentences). This model is suitable for English. Labels in this model are:

  • 1 : Positive
  • 0 : Negative

Example Usage

from transformers import pipeline
sentiment_classification = pipeline(task = 'sentiment-analysis', model = 'faycadnz/IMFBERT_binary')
sentiment_classification('They remain vulnerable to external shocks.')

Citation

If you find this repository useful in your research, please cite the following paper:

APA format:

Deniz, A., Angin, M., & Angin, P. (2022, May). Understanding IMF Decision-Making with Sentiment Analysis. In 2022 30th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.

Bibtex format:

@inproceedings{deniz2022understanding,
  title={Understanding IMF Decision-Making with Sentiment Analysis},
  author={Deniz, Ay{\c{c}}a and Angin, Merih and Angin, Pelin},
  booktitle={2022 30th Signal Processing and Communications Applications Conference (SIU)},
  pages={1--4},
  year={2022},
  organization={IEEE}
}
Downloads last month
6
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.