bfc47d89dc
Model: hachi-intelligence/HACHI-Summary-Ja-sarashina2.2-0.5b-instruct-v0.1 Source: Original Platform
169 lines
8.1 KiB
Markdown
169 lines
8.1 KiB
Markdown
---
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license: apache-2.0
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base_model:
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- sbintuitions/sarashina2.2-0.5b-instruct-v0.1
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library_name: transformers
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task_categories:
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- text-generation
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language:
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- ja
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tags:
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- HACHI-Intelligence
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- extractive-summarization
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- SLM
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datasets:
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- hachi-intelligence/JapaneseSummarization-FW2EduJa-Distill
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model_type: causal-lm
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pipeline_tag: text-generation
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finetuned_from: sbintuitions/sarashina2.2-0.5b-instruct-v0.1
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---
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# HACHI-Summary-Ja-sarashina2.2-0.5b-instruct-v0.1
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For a comprehensive deep dive into the model's architecture, training methodology, and performance benchmarks, please follow the pawprints to our [technical report (available in Japanese)](https://zenn.dev/hachi_intelli/articles/f72205c178f133). 🐕🐾
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## Overview
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**HACHI-Summary-Ja-sarashina2.2-0.5b-instruct-v0.1** is a commercially viable Japanese Small Language Model (SLM) optimized for high-performance **extractive summarization**. It is specifically designed for professional sectors where factual integrity is non-negotiable, such as **legal affairs, research, healthcare, and finance**.
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Built upon SB Intuitions' **sarashina2.2-0.5b-instruct-v0.1**, this model utilizes a knowledge distillation approach to inherit advanced processing capabilities from frontier LLMs while maintaining a lightweight footprint.
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### Key Features and Use Cases
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* **High-Fidelity Extractive Summarization**: Expertly preserves proper nouns, numerical data, units, chronological order, and causal relationships within reports and articles.
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* **Optimized for Edge Deployment**: With only 0.5B parameters, it enables rapid inference and integration into resource-constrained environments or edge devices.
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* **Commercial Readiness**: Released under the Apache-2.0 license, providing a transparent and reliable AI foundation for commercial applications, modifications, and redistribution.
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## Evaluation
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### Benchmarking Methodology
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Traditional benchmarks like **XLSUM-ja** primarily measure "abstractive summarization" (paraphrasing based on context). For models like HACHI-Summary-Ja, which prioritize the precise transcription of original data points, standard abstractive scores may not fully reflect their utility.
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To address this, we developed **HES-Ja (HACHI-Extractive-Summarization-Ja)**. We extracted 100 test cases from XLSUM and created a new evaluation set focused on the exact preservation of proper nouns, numerical values (including character-specific notations), and logical flow.
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> [!NOTE]
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> Detailed articles and the full HES-Ja dataset are scheduled for public release in the near future.
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### Benchmark Results
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#### XLSUM-ja Evaluation (Abstractive Metrics)
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Performance in general summarization tasks. The model shows significant strength when specific character constraints are applied.
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| Model | BLEU | ROUGE-2 | ROUGE-L |
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| :--- | :---: | :---: | :---: |
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| Qwen3-0.6B | 0.0147 | 0.0419 | 0.0837 |
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| gemma-3-270m-it | 0.0328 | **0.0788** | 0.1549 |
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| granite-4.0-350m | 0.0343 | 0.0728 | 0.1575 |
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| sarashina2.2-0.5b-instruct-v0.1 (Base) | 0.0263 | 0.0731 | 0.1317 |
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| HACHI-Summary-Ja | 0.0276 | 0.0747 | 0.1363 |
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| **HACHI-Summary-Ja (approx. 100 chars)** | **0.0412** | 0.0748 | **0.2055** |
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#### HES-Ja Evaluation (Extractive Metrics)
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This benchmark measures the accuracy of information transcription. **HACHI-Summary-Ja outperforms all other tested SLMs across every metric.**
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| Model | BLEU | ROUGE-2 | ROUGE-L |
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| :--- | :---: | :---: | :---: |
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| Qwen3-0.6B | 0.0963 | 0.1496 | 0.1820 |
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| gemma-3-270m-it | 0.1960 | 0.3029 | 0.3403 |
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| granite-4.0-350m | 0.1003 | 0.1963 | 0.2330 |
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| sarashina2.2-0.5b-instruct-v0.1 (Base) | 0.2457 | 0.3394 | 0.3635 |
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| **HACHI-Summary-Ja** | **0.2757** | **0.3644** | **0.4044** |
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## Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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# ===== settings =====
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# "","三行","100", "300", "500"から要約条件を設定
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SUMMARY_MODE = ""
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# 要約したいテキスト
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TEXT = """忠犬ハチ公
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犬種は秋田犬（あきたいぬ）で、性別はオス。名前はハチ。ハチ公の愛称でも知られる。
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ハチが飼い主を待ち続けた渋谷駅の出入り口の前には、ハチの銅像が設置されており、この「忠犬ハチ公像」は、渋谷のシンボルとして、観光名所としても有名である。
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ハチは、飼い主が死去した後も駅前で帰りを待ち続けた「忠犬」として知られる。東京・渋谷をはじめ、ゆかりの地には像が置かれている。
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特に、渋谷駅前のハチ公銅像は、いつしか待ち合わせの目印として使われるようになり、その銅像周囲は待ち合わせ場所としては「ハチ公前」などと呼ばれ、広く親しまれている。
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ハチの飼い主は、東京府豊多摩郡渋谷町大向（現・東京都渋谷区松濤一丁目）に住んでいた、東京帝国大学の教授・上野英三郎であった。
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彼は、大変な愛犬家であり、ハチの前にもたくさんの犬を飼っていた。出かける時には、渋谷駅までハチを伴うことも多かった。
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しかしながら、ハチを飼い始めた翌年にあたる1925年（大正14年）5月21日に上野は急死した。
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上野の死後も、駅前で亡くなった飼い主の帰りを毎日待ち続けたハチの姿は、新聞記事に掲載され、人々に感銘を与えたことから「忠犬ハチ公」と呼ばれるようになった。
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"""
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# 出典:忠犬ハチ公(https://ja.wikipedia.org/wiki/%E5%BF%A0%E7%8A%AC%E3%83%8F%E3%83%81%E5%85%AC)
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# ===== main =====
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SUMMARY_PROMPT_MAP = {
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"100": "原文を100字程度で簡潔に要約してください。",
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"300": "原文を300字程度で簡潔に要約してください。",
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"500": "原文を500字程度で簡潔に要約してください。",
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"三行": "原文を三行で簡潔に要約してください。",
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}
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SYSTEM_PROMPT = SUMMARY_PROMPT_MAP.get(
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SUMMARY_MODE,
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"原文を簡潔に要約してください。"
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)
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model_name = "hachi-intelligence/HACHI-Summary-Ja-sarashina2.2-0.5b-instruct-v0.1"
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tokenizer = AutoTokenizer.from_pretrained(
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model_name,
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)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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chat_pipeline = pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer
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)
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": TEXT}
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]
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outputs = chat_pipeline(
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messages,
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do_sample=False,
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# temperature=0.1,
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repetition_penalty=1.00,
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max_new_tokens=512,
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num_return_sequences=1,
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)
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generated_messages = outputs[0]["generated_text"]
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summary = generated_messages[-1]["content"]
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# ===== result =====
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print("\n===== SYSTEM PROMPT =====")
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print(SYSTEM_PROMPT)
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print("===== SOURCE TEXT =====")
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print(TEXT,f"({len(TEXT)}文字)")
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print("\n===== SUMMARY =====")
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print(summary,f"({len(summary)}文字)")
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# ===== output =====
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# 秋田犬（あきたいぬ）のオス・ハチは、飼い主が死去した後も渋谷駅の出入り口で帰りを待ち続けた「忠犬ハチ公」として知られる。東京・渋谷のハチ公銅像は渋谷のシンボルとして観光名所となり、待ち合わせの目印として「ハチ公前」と呼ばれる。飼い主は東京府豊多摩郡渋谷町大向（現・東京都渋谷区松濤一丁目）の東京帝国大学教授・上野英三郎で、ハチの前に多数の犬を飼っていた。1925年（大正14年）5月21日に上野が死去した後も、ハチは渋谷駅で 飼い主の帰りを待ち続け、新聞記事で「忠犬ハチ公」と称された。 (242文字)
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```
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## Acknowledgements
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* [SB Intuitions (sarashina2.2-0.5b-instruct-v0.1)](https://huggingface.co/sbintuitions/sarashina2.2-0.5b-instruct-v0.1)
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* [hotchpotch (fineweb-2-edu-japanese)](https://huggingface.co/datasets/hotchpotch/fineweb-2-edu-japanese)
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* [Open-R1](https://github.com/huggingface/open-r1)
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## License
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This model is licensed under the [Apache-2.0 License](https://www.google.com/search?q=LICENSE).
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