| --- |
| 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](https://ieeexplore.ieee.org/abstract/document/9864926): |
|
|
| 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} |
| } |
| ``` |
