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---
language:
- en
tags:
- IMF
- sentiment
- BERT
widget:
- text: The new revenue administration combatted the underground economy.
---
**IMFBERT** is built by fine-tuning the
[siebert/sentiment-roberta-large-english](https://huggingface.co/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:
<ins>APA format</ins>:
> 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.
<ins>Bibtex format</ins>:
```
@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}
}
``` |