README.md

---
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
```