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--- |
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language: |
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- en |
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tags: |
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- IMF |
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- sentiment |
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- BERT |
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widget: |
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- text: The new revenue administration combatted the underground economy. |
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--- |
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|
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**IMFBERT** is built by fine-tuning the |
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[siebert/sentiment-roberta-large-english](https://huggingface.co/siebert/sentiment-roberta-large-english) |
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model with IMF (International Monetary Fund) |
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Executive Board meeting minutes (around 150,000 sentences). |
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This model is suitable for English. Labels in this model are: |
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- 1 : Positive |
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- 0 : Negative |
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# Example Usage |
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``` |
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from transformers import pipeline |
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sentiment_classification = pipeline(task = 'sentiment-analysis', model = 'faycadnz/IMFBERT_binary') |
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sentiment_classification('They remain vulnerable to external shocks.') |
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``` |
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# Citation |
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If you find this repository useful in your research, please cite the following paper: |
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<ins>APA format</ins>: |
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> 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. |
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<ins>Bibtex format</ins>: |
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``` |
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@inproceedings{deniz2022understanding, |
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title={Understanding IMF Decision-Making with Sentiment Analysis}, |
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author={Deniz, Ay{\c{c}}a and Angin, Merih and Angin, Pelin}, |
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booktitle={2022 30th Signal Processing and Communications Applications Conference (SIU)}, |
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pages={1--4}, |
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year={2022}, |
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organization={IEEE} |
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} |
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``` |
