Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+# Qwen3-8B-Korean-Sentiment
+
+## Overview
+
+This repository contains a fine-tuned model for **Korean Sentiment Analysis (한국어 감정 분석)** using a **Large Language Model (LLM)**, specifically designed for **YouTube comments** in **Korean**. The model classifies sentiments into **Positive**, **Negative**, and **Neutral** categories, and is fine-tuned to detect not only direct emotions but also subtle features like **irony (반어법)** and **sarcasm (풍자)** common in Korean-language content.
+
+### Sentiment Classification:
+- **Positive (긍정)**
+- **Negative (부정)**
+- **Neutral (중립)**
+
+## Quickstart
+
+To quickly get started with the fine-tuned model, use the following Python code:
+
+```python
+from peft import AutoPeftModelForCausalLM
+from transformers import AutoTokenizer
+
+# Load the model and tokenizer
+model = AutoPeftModelForCausalLM.from_pretrained("suil0109/Qwen3-8B-Korean-Sentiment").to("cuda")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
+
+model.eval()
+
+# Sample comment
+comment = "이거 너무 좋아요!"
+
+# Format the prompt
+prompt = (
+    "다음은 유튜브 댓글입니다. 댓글의 감정을 분류해 주세요.\n\n"
+    f"댓글: {comment}\n\n"
+    "반드시 다음 중 하나만 출력하세요: 긍정 / 부정 / 중립"
+)
+
+# Tokenize the input
+inputs = tokenizer(prompt, return_tensors="pt")
+
+# Generate prediction
+outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=512)
+
+# Decode and print the output
+print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
+```
+
+## Train/Test Details
+
+- **Training Dataset**: Fine-tuned on **3,857** labeled YouTube comments for sentiment classification.
+- **Testing Dataset**: Evaluated on **1,130** labeled YouTube comments to assess the model's performance.
+
+## Results
+
+The fine-tuned model's performance on the sentiment classification task is summarized below:
+
+| Metric       | Positive (긍정)  | Neutral (중립)  | Negative (부정)  |
+|--------------|-----------------|----------------|-----------------|
+| **Precision**| 0.8981          | 0.3787         | 0.4971          |
+| **Recall**   | 0.7362          | 0.2880         | 0.7413          |
+| **F1-Score** | 0.8092          | 0.3272         | 0.5951          |
+| **Support**  | 527             | 309            | 344             |
+
+**Accuracy**: 62.03% (Based on 1180 samples)
+
+You can find detailed results [here](https://github.com/suil0109/LLM-SocialMedia/tree/main/huggingface).
+
+## Contact
+
+For any inquiries or feedback, feel free to contact the team:
+
+- **Email**: [email protected]
+
+**Team**:
+- Hanjun Jung
+- Jinsoo Kim
+- Junhyeok Choi
+- Suil Lee
+- Seongjae Kang