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pretty_name: LEXPT Law SFT (CAP subset) |
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dataset_name: lexpt-law-sft |
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tags: |
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- legal |
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- law |
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- caselaw |
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- sft |
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- lora |
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- chatml |
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- instruction-tuning |
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task_categories: |
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- text-generation |
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- question-answering |
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- summarization |
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language: |
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- en |
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license: cc-by-4.0 |
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size_categories: |
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- 10K<n<100K |
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source_datasets: |
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- common-pile/Caselaw_Access_Project |
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datasets: |
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- common-pile/caselaw_access_project |
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base_model: |
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- openai/gpt-oss-20b |
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pipeline_tag: text-generation |
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--- |
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# LEXPT Law SFT (CAP subset) |
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## Dataset Summary |
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**LEXPT Law SFT** is a supervised fine-tuning corpus for **U.S. case-law analysis**. It provides **chat-style instruction/response** records derived from **public-domain judicial opinions** (e.g., the Caselaw Access Project, “CAP”) and lawyer-authored prompts targeting appellate/habeas skills: |
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- Case skeleton extraction (posture, issues, holdings, standards, disposition) |
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- Variance vs. constructive amendment analysis |
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- Preservation/waiver and prejudice analysis |
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- Habeas procedural-default framing (cause–prejudice; innocence gateway) |
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- Evidence topics (authentication, 801(d)(2)(E), Rule 403, juror aids) |
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- IRAC drafting and advocacy point-headings (petitioner/state) |
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- Bluebook formatting exercises |
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The data are curated for **base+LoRA** legal assistants and are compatible with `tokenizer.apply_chat_template(...)` (ChatML-style roles). All **opinion texts** are public-domain; **prompts/annotations** are newly authored and released under **CC-BY-4.0**. |
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--- |
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## Intended Use |
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- Fine-tuning or LoRA-adapting general LLMs for **opinion-grounded legal reasoning**. |
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- Evaluation/benchmarking of structured appellate/habeas analysis on held-out opinions. |
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- Not for production of legal advice; this is a research/engineering dataset to improve structured legal outputs. |
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--- |
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## Use Cases (15 task templates) |
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1. **Core extraction (case skeleton)** |
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Extract (1) procedural posture, (2) issues, (3) holdings (one line each), (4) standards of review, (5) disposition from a provided opinion excerpt. |
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2. **Variance vs. constructive amendment** |
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Define both doctrines, then classify the opinion’s problem (proof–pleading discrepancy vs. alteration of elements) and justify using the court’s analysis. |
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3. **Preservation / waiver** |
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Identify the exact trial steps necessary to preserve a fatal-variance claim (contemporaneous objection, motion grounds specificity, request for continuance) and assess whether they occurred. |
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4. **Prejudice analysis (variance)** |
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Evaluate whether variant proof (e.g., gun vs. knife) misled the defense, caused surprise, or impaired preparation; point to record facts showing (no) prejudice. |
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5. **Habeas framing (procedural default)** |
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Explain how a state-trial variance claim is reviewed on federal habeas when no contemporaneous objection was made; outline cause-and-prejudice / actual-innocence gateways if prompted. |
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6. **Standard of review** |
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State which standard(s) the court applied (de novo, abuse of discretion, harmless error) and why; explain how lack of preservation narrowed the scope. |
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7. **Argument for petitioner/appellant** |
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Draft 4–8 concise advocacy points that a means discrepancy (e.g., knife → gun) violated Sixth-Amendment notice and was not harmless. |
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8. **Argument for the state/appellee** |
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Draft 4–8 concise counterpoints on waiver (failure to object), lack of prejudice/surprise, alignment with defense theory, and adequacy of notice. |
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9. **Record checklist** |
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Bullet list of record items to pull for briefing (charging instrument; key witness testimony; objections or lack thereof; motions and grounds; any continuance requested; state appeal; federal habeas pleadings). |
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10. **Remedies** |
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State the proper remedies if a preserved fatal variance is found on direct appeal vs. habeas (reversal, new trial, or other relief), and when harmless error applies. |
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11. **Hypothetical preservation** |
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Re-analyze outcome/posture assuming defense counsel objected when variant proof emerged and sought a continuance; discuss how that affects prejudice and review. |
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12. **Notice pleading in informations** |
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Explain required factual specificity to satisfy notice; apply to “assault with intent to kill” and assess whether the instrument’s means (knife vs. gun) is material. |
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13. **Jury-instruction angle** |
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Propose a limiting/clarifying instruction to mitigate variance prejudice (e.g., confining the theory to the charged means) and analyze whether refusal would be reversible error. |
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14. **Bluebook formatting** |
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Provide full and short-form citations for the controlling decision(s) and the referenced state case; compose a citation string suitable for a brief’s argument section. |
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15. **One-page IRAC** |
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Produce an IRAC with exact headers—**Issue**, **Rule**, **Application**, **Conclusion**—summarizing the variance/notice dispute and the court’s reasoning. |
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--- |
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## Data Structure |
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### Record Schema |
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| Field | Type | Description | |
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|----------------|--------|---------------------------------------------------------------------------------------------------| |
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| `id` | str | Unique identifier (e.g., `ridgeway_habeas_0001`). | |
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| `case_name` | str | Case caption (e.g., “Ridgeway v. Hutto”). | |
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| `court` | str | Court (e.g., “8th Cir.”). | |
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| `year` | int | Decision year. | |
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| `jurisdiction` | str | “federal” or “state”. | |
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| `prompt_type` | str | One of the 15 task categories (see **Use Cases**). | |
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| `opinion_text` | str | Public-domain opinion excerpt used as context. | |
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| `messages` | list | ChatML-style messages: `[{"role": "system"|"user"|"assistant", "content": "..."}]`. | |
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| `source_ref` | str | Short provenance note (e.g., “CAP; citation: 474 F.2d 22 (8th Cir. 1973)”). | |
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### Example Record |
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```json |
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{ |
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"id": "ridgeway_habeas_0001", |
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"case_name": "Ridgeway v. Hutto", |
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"court": "8th Cir.", |
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"year": 1973, |
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"jurisdiction": "federal", |
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"prompt_type": "core_extraction", |
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"opinion_text": "…public-domain opinion excerpt…", |
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"messages": [ |
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{ |
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"role": "system", |
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"content": "You are a legal analysis assistant. Return ONLY the final answer. No prefaces or meta-commentary." |
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}, |
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{ |
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"role": "user", |
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"content": "From the opinion text, list: (1) procedural posture, (2) issues, (3) holdings, (4) standards of review, (5) disposition.\n\nOPINION TEXT:\n…" |
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}, |
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{ |
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"role": "assistant", |
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"content": "1) …\n2) …\n3) …\n4) …\n5) …" |
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} |
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], |
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"source_ref": "CAP; citation: 474 F.2d 22 (8th Cir. 1973)" |
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} |
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``` |
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### Splits |
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- `train`: update after upload |
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- `validation`: update after upload |
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- `test` (optional): update after upload |
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> **Split policy:** Do **not** split tasks for the **same case** across train/val/test to avoid leakage. |
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--- |
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## How to Use |
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### Load with 🤗 Datasets |
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```python |
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from datasets import load_dataset |
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ds = load_dataset("sik247/lexpt-law-sft") # replace with your repo id |
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print(ds) |
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print(ds["train"][0]) |
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``` |
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### Use with Chat Templates (Transformers) |
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```python |
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from transformers import AutoTokenizer |
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tok = AutoTokenizer.from_pretrained("unsloth/gpt-oss-20b") # or your base |
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sample = ds["train"][0]["messages"] |
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prompt = tok.apply_chat_template(sample, add_generation_prompt=True, tokenize=False) |
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``` |
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--- |
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## Curation & Construction |
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- **Sources:** public-domain opinions (e.g., CAP). |
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- **Selection:** appellate/habeas cases and issues suited for structured outputs (lists, checklists, IRAC). |
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- **Annotation:** prompts and answers authored by legal-knowledgeable contributors; emphasis on **final-answer-only** style. |
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- **Preprocessing:** remove site boilerplate; normalize whitespace/quotes; ensure consistent role formatting; de-duplicate near-identical snippets. |
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--- |
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## Quality Control |
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- Spot checks for: (i) factual alignment with the opinion excerpt, (ii) formatting adherence (lists/IRAC), (iii) concise, jurisdiction-aware language. |
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- Where uncertainty exists, assistant outputs avoid invented facts/citations and prefer “Insufficient information.” |
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--- |
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## Ethical Considerations & Limitations |
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- **Not legal advice.** This dataset trains formatting and structure for legal analysis; always verify with primary sources. |
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- **Coverage:** U.S. appellate caselaw; not exhaustive across jurisdictions or dates. |
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- **Model risk:** Misstatements of doctrine or miscitation can occur; downstream users should validate. |
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- **Bias:** Judicial texts may reflect historical or jurisdictional bias; outputs may inherit such patterns. |
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--- |
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## Licensing |
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- **Opinion texts:** Public domain (as supplied by CAP and similar sources). |
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- **Prompts & annotations:** © 2025 sik247, released under **CC-BY-4.0**. |
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- When redistributing, include attribution: *“sik247 / LEXPT Law SFT (CAP subset)”*. |
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--- |
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## Citation |
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If you use this dataset, please cite: |
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``` |
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sik247. LEXPT Law SFT (CAP subset). 2025. Hugging Face Dataset. |
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``` |
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And acknowledge the public-domain opinion sources (e.g., CAP) per their attribution guidance. |
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--- |
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## Maintainer |
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- **Author/Maintainer:** `sik247` |
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- Issues/requests: open a Discussion on the dataset page. |
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--- |
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## Changelog |
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- **v1.0** — Initial release with CAP-based opinion excerpts, 15 task templates, and ChatML records. Update counts and add additional jurisdictions in subsequent versions. |
