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README.md

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+Fine-tuned KoR-SRoBERTa for Corporate News Relevance Classification
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+⸻
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+Overview
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+This model is a fine-tuned version of the base model jhgan/ko-sroberta-multitask, adapted specifically for sentiment classification of Korean corporate-related news articles. The goal of this model is to improve performance in distinguishing positive, neutral, and negative sentiments within firm-specific news contexts, which often contain domain-specific financial language.
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+This work is based on the methodology and dataset presented in the following academic paper:
+
+Hyun Ji-won, Lee Jun-il, and Cho Hyun-kwon (2022).
+“A Study on Sentiment Classification of Corporate-related News Articles Using KoBERT.”
+Accounting Research 47(4), 33–54.
+https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002873618
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+This model card includes proper attribution to the original authors of the base model as required under the CC-BY-SA-4.0 license.
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+⸻
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+Model Details
+	•	Base model: jhgan/ko-sroberta-multitask
+	•	Architecture: RoBERTa (Korean SRoBERTa variant)
+	•	Task: Sentiment classification (Positive / Neutral / Negative)
+	•	Language: Korean
+	•	Domain: Corporate & financial news
+	•	Fine-tuning: Conducted on a curated dataset of company-related news headlines and lead sentences derived from the methodology of the 2022 study.
+
+⸻
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+Intended Use
+
+This model is intended for:
+	•	Identifying relevance of firm-specific and finance-related Korean news
+	•	Downstream applications requiring corporate textual analytsis
+
+Not intended for:
+	•	General-purpose Korean analysis without domain adaptation
+	•	High-stakes financial decision-making without human oversight
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+
+⸻
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+Licensing
+
+This model is published under CC-BY-SA-4.0, inherited from the base model:
+	•	Original base model: jhgan/ko-sroberta-multitask
+	•	Original license: CC-BY-SA-4.0
+
+⸻
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+Attribution
+
+If you use this model, please cite both:
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+1. Base Model
+
+jhgan/ko-sroberta-multitask
+(Original authors as listed on the model card)
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+2. Fine-tuned Model / Academic Basis
+
+Hyun Ji-won, Lee Jun-il, and Cho Hyun-kwon (2022).
+A Study on Sentiment Classification of Corporate-related News Articles Using KoBERT.
+Accounting Research 47(4), 33–54.
+https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002873618
+