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README.md

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----
-license: mit
----
+---
+license: cc-by-4.0
+language:
+- en
+tags:
+- eu-law
+- legal
+- gdpr
+- ai-act
+- rag
+- retrieval
+- instruction-tuning
+task_categories:
+- text-generation
+- text-retrieval
+configs:
+- config_name: chunks
+  data_files: chunks/train.jsonl
+- config_name: finetuning
+  data_files: finetuning/train.jsonl
+---
+
+# EuropeGram: EU Legal Text -- RAG Chunks & Instruction Data
+
+Structured, chunked, and instruction-formatted text derived from official EU
+legislation, built for retrieval-augmented generation (RAG) and LoRA
+fine-tuning experiments comparing Base / RAG / Fine-tuned / Fine-tuned+RAG
+LLM strategies over EU documents. Produced by the EuropeGram project's
+extraction -> chunking -> fine-tuning-export pipeline.
+
+## Source documents
+
+| Document | CELEX ID | Source | Chunks | Instruction pairs |
+|---|---|---|---|---|
+| Regulation (EU) 2024/1689 (Artificial Intelligence Act) | `32024R1689` | [EUR-Lex](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689) | 316 | 126 |
+| Regulation (EU) 2016/679 (General Data Protection Regulation) | `32016R0679` | [EUR-Lex](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32016R0679) | 187 | 99 |
+
+**Attribution & authenticity notice.** The underlying legal texts are
+reproduced from the official PDF renditions published on EUR-Lex. Reuse of
+EU legislation is permitted free of charge under [Decision
+2011/833/EU](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32011D0833),
+provided the source is acknowledged and the meaning or message of the
+original is not distorted. **Only the versions published in the Official
+Journal of the European Union are authentic and legally binding** -- this
+dataset is a derived, machine-processed artifact for ML research and must
+not be treated as an authoritative legal source.
+
+## Configs
+
+### `chunks`
+
+One row per RAG chunk (structure-aware: packed by article/annex paragraph,
+capped at `max_chars` with `overlap_chars` overlap for long ones -- see
+`europegram.rag.chunker`). Fields:
+
+- `id`: chunk id, e.g. `article-4-chunk-0`
+- `text`: chunk text (first chunk of an article/annex is prefixed with its heading)
+- `document_id`, `celex_id`: which source document
+- `ref_type`: `"article"` or `"annex"`
+- `ref_id`: e.g. `"article-4"`, `"annex-viii"`
+- `title`: article/annex title
+- `chapter_number`, `chapter_title`: containing chapter, when applicable (nullable)
+- `page_start`, `page_end`: page span in the source PDF
+- `chunk_index`: position of this chunk within its article/annex (0-based)
+
+### `finetuning`
+
+One Alpaca-style instruction/output pair per article/annex, for LoRA
+fine-tuning (see `europegram.finetuning.dataset_builder` and
+`colab/finetune_lora.ipynb`). Fields:
+
+- `instruction`: natural-language question about one article/annex
+- `input`: always empty (kept for Alpaca-format compatibility)
+- `output`: the article/annex's full text -- the target completion
+- `document_id`, `celex_id`, `ref_id`: provenance, so any model output can be traced back to a specific legal reference
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+chunks = load_dataset("<your-username>/<dataset-name>", "chunks", split="train")
+finetuning = load_dataset("<your-username>/<dataset-name>", "finetuning", split="train")
+```