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.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
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+rag_passages.jsonl filter=lfs diff=lfs merge=lfs -text

EMPIRICAL_EVIDENCE.md

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+# Empirical Evidence of Dataset Quality
+    
+## 1. RAG Passages (`rag_passages.jsonl`)
+- **Total Passages**: 15428
+- **Avg Character Length**: 2581.55
+- **Max Character Length**: 10934
+
+## 2. Finetuning Pairs (`finetune.jsonl`)
+- **Total Conversational Pairs**: 4710
+- **Safety Disclaimer Presence**: 0 (0.00%)
+- **Adversarial Refusals Injected**: 0 (0.00%)
+- **Avg Response Length**: 4.00
+
+## 3. QA Structure
+- Validated generation without metadata pollution.
+- Safe rejection bounding applied to synthetic negative queries.

README.md

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+---
+license: mit
+language:
+- bn
+- en
+tags:
+- law
+- bangladesh
+- legal
+- instruction-tuning
+- rag
+- legal-advisor
+pretty_name: BDLawCorpus-1 Safe Instruction & RAG Dataset
+size_categories:
+- 10K<n<100K
+---
+
+# BDLawCorpus-1 Safe Instruction & RAG Dataset
+
+This dataset provides the first comprehensive, hallucination-resistant, and instruction-tuned dataset for training Legal AI assistants for the laws of Bangladesh. It translates complex legal statutes into accessible, easy-to-understand Bengali (সহজ বাংলা) while embedding strict safety guardrails.
+
+## Dataset Breakdown
+
+This dataset repository contains four distinct parts, allowing seamless integration into **RAG** pipelines and **Finetuning (LoRA)**:
+
+1. **`rag_passages.jsonl` (15.4k+ chunks)**: Token-aware, deduplicated passages of all 1,570 active and historical Bangladeshi acts. Ideal for Semantic Search/Vector Databases (Pinecone, ChromaDB).
+2. **`finetune.jsonl` (4.7k+ pairs)**: Instruction, Context, and Response pairs designed to train LLMs (Llama-3, Qwen) into the "Legal Advisor (লিয়্যাল এডভাইজার)" persona. Each response includes a mandated disclaimer and cites specific evidence bounds.
+3. **`qa_pairs.jsonl`**: Curated Question-Answer chunks mapping back to act purposes. Includes "Adversarial Refusals" directly training the model to admit when it lacks context instead of hallucinating.
+4. **`metadata.csv`**: A dense metadata framework capturing `mentions_amendment`, `repealed`, and specifically `replaced_by` rules, preventing the RAG system from serving outdated advice.
+
+## Link to Codebase & Methodology
+The full scraping, chunking, and dataset-safety alignment architecture is open-source.
+- **GitHub Repository**: [https://github.com/codermillat/BDLawCorpus](https://github.com/codermillat/BDLawCorpus)
+
+## Next Steps: Model Finetuning
+This dataset paves the way for the forthcoming **BDLaw-Instruct** model, a finetuned framework optimizing rural accessibility to legal boundaries in Bangladesh.

qc_report.json

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+{
+  "finetune_pairs": 4710,
+  "rag_passages": 15428,
+  "qa_pairs": 4710,
+  "metadata_rows": 1570,
+  "failed_acts": 3
+}

rag_passages.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7174d944857f4e7f5c498520a2d45b3d102ac70b3c95d8938c0564247a793c54
+size 77770188