Create README.md

README.md

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+---
+language:
+  - en
+tags:
+  - IMF
+  - sentiment
+  - BERT
+widget:
+  - text: The implementation of coherent policies has decisively transformed the performance of the Turkish 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}
+}
+```