Update README.md

README.md

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 <!-- Provide a quick summary of what the model is/does. -->
 2024.4.4 Update
 
+This model is a sentiment analysis model designed to determine the positive/neutral/negative sentiment of sentences included in corporate-related news.
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+It can be used as a Korean-based sentiment analysis model for the finance/management/accounting fields.
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+Example>
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+"Samsung's debt is increasing." --> Neutral. The mere increase in debt is not necessarily negative.
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+"Due to the failure of management strategy, Samsung's debt is increasing." --> Negative. Debt increase due to failure is considered negative.
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+Hyun Ji-won, Lee Jun-il, and Cho Hyun-kwon. "A Study on Sentiment Classification of Corporate-related News Articles Using KoBERT." Accounting Research 47.4 (2022): 33-54.
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+We have further developed the model proposed in the above paper and made it available through Huggingface. If you use it for research purposes, please cite the above paper.
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+This model was fine-tuned using https://huggingface.co/jhgan/ko-sroberta-multitask.
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+For the usage code, refer to the link below:
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+Google Colab: https://colab.research.google.com/drive/1ORzKUr94cPyc5jaRCAngbclm4Qb4DtdG
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+The current evaluation results of the model are as follows:
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+{'eval_loss': 0.7330707907676697, 'eval_f1': 0.8689251403360293, 'eval_runtime': 0.464, 'eval_samples_per_second': 2047.32, 'eval_steps_per_second': 17.241, 'epoch': 33.33}
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+While the accuracy has increased compared to the paper's 85.7%, the improvement is not significant.
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+2024.4.4 Update
+
 이 모형은 기업관련 뉴스에 포함된 문장의 긍정/중립/부정을 판단하기 위한 감성분석 모형입니다.
 
 한국어 기반 금융/경영/회계 분야 감성분석 모형으로 사용하시면 됩니다. 
@@ -49,189 +77,4 @@ https://colab.research.google.com/drive/1ORzKUr94cPyc5jaRCAngbclm4Qb4DtdG
 
 ### Model Description
 
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-### Model Sources [optional]
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-## Uses
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-### Direct Use
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-### Training Procedure 
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-## Citation [optional]
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+<!-- Provide a longer summary of what this model is. -->