qwen3-1.7B-ko-summary-finetuned
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 to generate concise, coherent abstracts from long-form Korean texts.
Model Description
- Architecture: Qwen3-1.7B
- Fine-tuning Task: Abstractive summarization
- Training Data: Korean academic paper summaries (e.g., KoreaScience dataset)
Intended Use
- Summarizing long Korean documentsโespecially research papersโinto clear, concise overviews.
- Integrating into research tools, educational platforms, or automated document-processing pipelines.
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
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("your-username/qwen3-1.7B-ko-summary-finetuned")
model = AutoModelForSeq2SeqLM.from_pretrained("your-username/qwen3-1.7B-ko-summary-finetuned")
text = "์ฌ๊ธฐ์ ๊ธด ํ๊ตญ์ด ๋
ผ๋ฌธ ํ
์คํธ๋ฅผ ์
๋ ฅํ์ธ์..."
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
.
โโโ config.json
โโโ generation_config.json
โโโ model.safetensors
โโโ model.safetensors.index.json
โโโ tokenizer.json
โโโ tokenizer_config.json
โโโ special_tokens_map.json
โโโ vocab.json
โโโ merges.txt
โโโ added_tokens.json
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