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colli-ai/
qwen3-1.7B-ko-summary-finetuned-06-12

Name: qwen3-1.7B-ko-summary-finetuned-06-12
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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

Resolved Token Repetition Issue: Fixed meaningless token repetition problems from the previous colli98/qwen3-1.7B-ko-summary-finetuned model
Structured Summary Format: Improved unstructured summary format issues for better coherence
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.

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

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

colli-ai/qwen3-1.7B-ko-summary-finetuned-06-12