faycadnz/IMFBERT_binary

1---2language:3  - en4tags:5  - IMF6  - sentiment7  - BERT8widget:9  - text: The new revenue administration combatted the underground economy.10---11 12**IMFBERT** is built by fine-tuning the 13[siebert/sentiment-roberta-large-english](https://huggingface.co/siebert/sentiment-roberta-large-english) 14model with IMF (International Monetary Fund) 15Executive Board meeting minutes (around 150,000 sentences). 16This model is suitable for English. Labels in this model are:17- 1 : Positive18- 0 : Negative19 20 21# Example Usage22 23```24from transformers import pipeline25sentiment_classification = pipeline(task = 'sentiment-analysis', model = 'faycadnz/IMFBERT_binary')26sentiment_classification('They remain vulnerable to external shocks.')27```28 29 30# Citation31 32If you find this repository useful in your research, please cite [the following paper](https://ieeexplore.ieee.org/abstract/document/9864926):33 34APA format:35 36> 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.37 38Bibtex format:39 40```41@inproceedings{deniz2022understanding,42  title={Understanding IMF Decision-Making with Sentiment Analysis},43  author={Deniz, Ay{\c{c}}a and Angin, Merih and Angin, Pelin},44  booktitle={2022 30th Signal Processing and Communications Applications Conference (SIU)},45  pages={1--4},46  year={2022},47  organization={IEEE}48}49```