Instructions to use faycadnz/IMFBERT_binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use faycadnz/IMFBERT_binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="faycadnz/IMFBERT_binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("faycadnz/IMFBERT_binary") model = AutoModelForSequenceClassification.from_pretrained("faycadnz/IMFBERT_binary") - Notebooks
- Google Colab
- Kaggle
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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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> 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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```
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@inproceedings{deniz2022understanding,
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# Citation
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If you find this repository useful in your research, please cite [the following paper](https://ieeexplore.ieee.org/abstract/document/9864926):
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APA format:
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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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Bibtex format:
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```
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@inproceedings{deniz2022understanding,
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