# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("faycadnz/IMFBERT_binary")
model = AutoModelForSequenceClassification.from_pretrained("faycadnz/IMFBERT_binary")Quick Links
IMFBERT is built by fine-tuning the 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:
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}
}
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="faycadnz/IMFBERT_binary")