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README.md
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### Research Background
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As explained in our [EMNLP 2024 paper](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=RnPFjYcAAAAJ&citation_for_view=RnPFjYcAAAAJ:W7OEmFMy1HYC), this model was fine-tuned using contrastive learning on human-generated arguments for and against various claims. This technique enhances sentence transformers' ability to capture sentiment and stance in opinionated texts while retaining computational efficiency over classification-based approaches.
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## Model Use and Code Instructions
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### Research Background
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As explained in our [EMNLP 2024 paper](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=RnPFjYcAAAAJ&citation_for_view=RnPFjYcAAAAJ:W7OEmFMy1HYC), this model was fine-tuned using contrastive learning on human-generated arguments for and against various claims. This technique enhances sentence transformers' ability to capture sentiment and stance in opinionated texts while retaining computational efficiency over classification-based approaches.
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The code for training can be viewed from [this repository](https://github.com/vahidthegreat/StanceAware_SBERT/) at huggingface.
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## Model Use and Code Instructions
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