Create README.md

README.md

@@ -0,0 +1,120 @@
+---
+license: mit
+language:
+- ko
+- en
+- zh
+base_model:
+- Qwen/Qwen3-1.7B
+pipeline_tag: summarization
+tags:
+- qwen3
+- korean
+- summary
+- summarization
+- ko
+---
+
+# qwen3-1.7B-ko-summary-finetuned-06-12
+
+A fine-tuned Qwen3-1.7B model specialized for abstractive summarization of Korean documents, particularly academic papers. This model was trained on high-quality Korean paper summarization data and enhanced with emotional multi-turn conversation data to expand vocabulary and improve generation quality.
+
+## Model Description
+
+- **Architecture**: Qwen3-1.7B
+- **Fine-tuning Task**: Abstractive summarization
+- **Training Data**: Korean academic paper summaries (e.g., KoreaScience dataset) + Emotional multi-turn conversation data
+
+## Key Improvements
+
+1. **Resolved Token Repetition Issue**: Fixed meaningless token repetition problems from the previous colli98/qwen3-1.7B-ko-summary-finetuned model
+2. **Structured Summary Format**: Improved unstructured summary format issues for better coherence
+3. **Enhanced Vocabulary**: Added emotional multi-turn conversation training data to expand vocabulary range beyond academic papers
+
+## Intended Use
+
+- Summarizing long Korean documents—especially research papers—into clear, concise overviews.
+- Integrating into research tools, educational platforms, or automated document-processing pipelines.
+
+## Performance Evaluation
+
+### ROUGE Score Comparison
+
+| Metric                   | Previous Model | Current Model | Improvement |
+| ------------------------ | -------------- | ------------- | ----------- |
+| **ROUGE-1 Precision**    | 0.357          | 0.388         | **+8.7%**   |
+| **ROUGE-1 Recall**       | 0.189          | 0.174         | -7.9%       |
+| **ROUGE-1 F-measure**    | 0.247          | 0.241         | -2.4%       |
+| **ROUGE-2 Precision**    | 0.109          | 0.169         | **+55.0%**  |
+| **ROUGE-2 Recall**       | 0.058          | 0.076         | **+31.1%**  |
+| **ROUGE-2 F-measure**    | 0.075          | 0.104         | **+38.7%**  |
+| **ROUGE-L Precision**    | 0.269          | 0.328         | **+21.9%**  |
+| **ROUGE-L Recall**       | 0.142          | 0.147         | **+3.5%**   |
+| **ROUGE-L F-measure**    | 0.186          | 0.203         | **+9.1%**   |
+| **ROUGE-Lsum Precision** | 0.316          | 0.319         | **+0.9%**   |
+| **ROUGE-Lsum Recall**    | 0.168          | 0.171         | **+1.8%**   |
+| **ROUGE-Lsum F-measure** | 0.219          | 0.223         | **+1.8%**   |
+
+### Performance Analysis
+
+**Positive Improvements:**
+
+- **Overall Precision Enhancement**: Improved precision across all metrics, indicating higher quality generated content
+- **Significant ROUGE-2 Improvement**: Major improvement in bigram-level metrics, suggesting more natural and coherent sentence structure generation
+
+**Trade-offs:**
+
+- **Partial Recall Decrease**: Slight decrease in recall, particularly in ROUGE-1, suggesting potential missed content from reference texts
+- **Room for Further Improvement**: All metrics remain below 0.4, indicating need for additional performance enhancements
+
+**Conclusion**: Fine-tuning improved **generation quality (precision)** while showing slight trade-offs in **completeness (recall)**. The significant ROUGE-2 improvement represents meaningful progress in model performance.
+
+![ROUGE Score Comparison](rouge_comparison_chart.png)
+
+## Limitations & Risks
+
+- May produce inaccuracies or hallucinated content.
+- Not intended for generating verbatim legal/medical texts or for extractive quotation.
+- Users should verify critical facts against original sources.
+
+## Installation
+
+```bash
+pip install transformers safetensors
+```
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("your-username/qwen3-1.7B-ko-summary-finetuned-06-12")
+model = AutoModelForSeq2SeqLM.from_pretrained("your-username/qwen3-1.7B-ko-summary-finetuned-06-12")
+
+text = "여기에 긴 한국어 논문 텍스트를 입력하세요..."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="longest")
+summary_ids = model.generate(
+    **inputs,
+    max_length=150,
+    num_beams=4,
+    early_stopping=True
+)
+summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+print(summary)
+```
+
+## Files in This Repository
+
+```bash
+.
+├── config.json
+├── generation_config.json
+├── model.safetensors
+├── model.safetensors.index.json
+├── tokenizer.json
+├── tokenizer_config.json
+├── special_tokens_map.json
+├── vocab.json
+├── merges.txt
+└── added_tokens.json
+```