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