eyepyon
/

constitution-gemma2-test

@@ -1,199 +1,151 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+license: apache-2.0
+base_model: google/gemma-2-2b
+tags:
+- fine-tuned
+- legal
+- constitution
+- japanese
+- education
+- law
 library_name: transformers
+pipeline_tag: text-generation
+language:
+- ja
 ---
+# eyepyon/constitution-gemma2-test - 憲法教育支援AI
+このモデルは、`google/gemma-2-2b` をベースに憲法教育データでファインチューニングされた専門モデルです。
+## 🏛️ モデル概要
+- **専門分野**: 憲法学・法学教育
+- **ベースモデル**: google/gemma-2-2b
+- **ファインチューニング手法**: LoRA (Low-Rank Adaptation)
+- **対応言語**: 日本語
+- **主要タスク**: 憲法問題の解答・見解分析
+## 🎓 教育用途
+このモデルは以下の教育シーンで活用できます：
+- **法学部学生の学習支援**
+- **司法試験対策**
+- **憲法判例の理解促進**
+- **見解の批判関係の分析**
+## 🚀 使用方法
+### 基本的な使用例
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+# ベースモデルとトークナイザーを読み込み
+base_model = AutoModelForCausalLM.from_pretrained(
+    "google/gemma-2-2b",
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b")
+# LoRAアダプターを適用
+model = PeftModel.from_pretrained(base_model, "eyepyon/constitution-gemma2-test")
+# 憲法問題の解答
+def solve_constitution_problem(context, question):
+    input_text = f"### 法律分野: 憲法\n### コンテキスト:\n{context}\n\n### 問題:\n{question}\n\n### 解答:\n"
+    inputs = tokenizer(input_text, return_tensors="pt")
+    if torch.cuda.is_available():
+        inputs = {k: v.to(model.device) for k, v in inputs.items()}
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_length=1024,
+            do_sample=True,
+            temperature=0.3,  # 法律問題では低めの温度
+            top_p=0.8,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    if "### 解答:" in response:
+        response = response.split("### 解答:")[-1].strip()
+    return response
+# 使用例
+context = "憲法に関する法律問題について、見解の関係性を分析し、批判の有無を判断してください。"
+question = "人権の享有主体に関する次の記述について、ｂの見解がａの見解の批判となっているか分析してください..."
+answer = solve_constitution_problem(context, question)
+print(answer)
+```
+## 📊 入力フォーマット
+```
+### 法律分野: 憲法
+### コンテキスト:
+[法的背景や問題設定]
+### 問題:
+[具体的な憲法問題]
+### 解答:
+[期待される解答や分析]
+```
+## ⚙️ トレーニング設定
+- **ファインチューニング手法**: LoRA
+- **LoRAランク**: 16 (法律ドメイン最適化)
+- **LoRA Alpha**: 32
+- **学習率**: 1e-4 (専門分野用に調整)
+- **最大シーケンス長**: 1024トークン
+- **バッチサイズ**: 1 × 8 (gradient accumulation)
+## 🎯 モデルの特長
+### 法学専門性
+- 憲法の基本概念の理解
+- 判例分析能力
+- 見解の論理的関係性の把握
+### 教育支援機能
+- 段階的な説明
+- 批判的思考の促進
+- 法的推論の支援
+## ⚠️ 使用上の注意
+1. **教育目的限定**: このモデルは教育支援を目的として作成されています
+2. **法的助言の禁止**: 実際の法的助言や判断には使用しないでください
+3. **継続的学習**: 法律は変化するため、最新の情報は別途確認が必要です
+4. **批判的検証**: 生成される内容は必ず専門知識と照らし合わせて検証してください
+## 📚 データセット情報
+- **データ源**: 憲法教育用問題集
+- **問題形式**: 見解の批判関係分析
+- **データ量**: 約60問
+- **品質**: 教育専門家による監修済み
+## 📄 ライセンス
+Apache 2.0 License
+## 🤝 謝辞
+- ベースモデル: google/gemma-2-2b
+- 教育データ提供者の皆様
+- 法学教育関係者の皆様
+---
+**最終更新**: 2025年06月04日
+このモデルは教育研究目的での使用を想定しています。実際の法的判断には使用せず、必ず専門家の助言を求めてください。