# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("kwoncho/ko-sroberta-multitask-informative")
model = AutoModelForSequenceClassification.from_pretrained("kwoncho/ko-sroberta-multitask-informative")YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Fine-tuned KoR-SRoBERTa for Corporate News Relevance Classification
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Overview
This model is a fine-tuned version of the base model jhgan/ko-sroberta-multitask, adapted specifically for relevance classification of Korean corporate-related news articles. The goal of this model is to improve performance in distinguishing significant news within firm-specific news contexts, which often contain domain-specific financial language.
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
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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Model Details • Base model: jhgan/ko-sroberta-multitask • Architecture: RoBERTa (Korean SRoBERTa variant) • Task: Relevance classification (1/0) • Language: Korean • Domain: Corporate & financial news (Korean) • 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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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:
- Base Model
jhgan/ko-sroberta-multitask (Original authors as listed on the model card)
- 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
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kwoncho/ko-sroberta-multitask-informative")