| --- |
| pretty_name: LEXPT Law SFT (CAP subset) |
| dataset_name: lexpt-law-sft |
| tags: |
| - legal |
| - law |
| - caselaw |
| - sft |
| - lora |
| - chatml |
| - instruction-tuning |
| task_categories: |
| - text-generation |
| - question-answering |
| - summarization |
| language: |
| - en |
| license: cc-by-4.0 |
| size_categories: |
| - 10K<n<100K |
| source_datasets: |
| - common-pile/Caselaw_Access_Project |
| datasets: |
| - common-pile/caselaw_access_project |
| base_model: |
| - openai/gpt-oss-20b |
| pipeline_tag: text-generation |
| --- |
| |
| # LEXPT Law SFT (CAP subset) |
|
|
| ## Dataset Summary |
| **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: |
|
|
| - Case skeleton extraction (posture, issues, holdings, standards, disposition) |
| - Variance vs. constructive amendment analysis |
| - Preservation/waiver and prejudice analysis |
| - Habeas procedural-default framing (cause–prejudice; innocence gateway) |
| - Evidence topics (authentication, 801(d)(2)(E), Rule 403, juror aids) |
| - IRAC drafting and advocacy point-headings (petitioner/state) |
| - Bluebook formatting exercises |
|
|
| 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**. |
|
|
| --- |
|
|
| ## Intended Use |
| - Fine-tuning or LoRA-adapting general LLMs for **opinion-grounded legal reasoning**. |
| - Evaluation/benchmarking of structured appellate/habeas analysis on held-out opinions. |
| - Not for production of legal advice; this is a research/engineering dataset to improve structured legal outputs. |
|
|
| --- |
|
|
| ## Use Cases (15 task templates) |
|
|
| 1. **Core extraction (case skeleton)** |
| Extract (1) procedural posture, (2) issues, (3) holdings (one line each), (4) standards of review, (5) disposition from a provided opinion excerpt. |
|
|
| 2. **Variance vs. constructive amendment** |
| Define both doctrines, then classify the opinion’s problem (proof–pleading discrepancy vs. alteration of elements) and justify using the court’s analysis. |
|
|
| 3. **Preservation / waiver** |
| 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. |
|
|
| 4. **Prejudice analysis (variance)** |
| 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)** |
| 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** |
| 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** |
| 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** |
| 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** |
| 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** |
| 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. |
| |
| 11. **Hypothetical preservation** |
| Re-analyze outcome/posture assuming defense counsel objected when variant proof emerged and sought a continuance; discuss how that affects prejudice and review. |
| |
| 12. **Notice pleading in informations** |
| 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. |
| |
| 13. **Jury-instruction angle** |
| 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. |
| |
| 14. **Bluebook formatting** |
| 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. |
| |
| 15. **One-page IRAC** |
| Produce an IRAC with exact headers—**Issue**, **Rule**, **Application**, **Conclusion**—summarizing the variance/notice dispute and the court’s reasoning. |
| |
| --- |
|
|
| ## Data Structure |
|
|
| ### Record Schema |
| | Field | Type | Description | |
| |----------------|--------|---------------------------------------------------------------------------------------------------| |
| | `id` | str | Unique identifier (e.g., `ridgeway_habeas_0001`). | |
| | `case_name` | str | Case caption (e.g., “Ridgeway v. Hutto”). | |
| | `court` | str | Court (e.g., “8th Cir.”). | |
| | `year` | int | Decision year. | |
| | `jurisdiction` | str | “federal” or “state”. | |
| | `prompt_type` | str | One of the 15 task categories (see **Use Cases**). | |
| | `opinion_text` | str | Public-domain opinion excerpt used as context. | |
| | `messages` | list | ChatML-style messages: `[{"role": "system"|"user"|"assistant", "content": "..."}]`. | |
| | `source_ref` | str | Short provenance note (e.g., “CAP; citation: 474 F.2d 22 (8th Cir. 1973)”). | |
|
|
| ### Example Record |
| ```json |
| { |
| "id": "ridgeway_habeas_0001", |
| "case_name": "Ridgeway v. Hutto", |
| "court": "8th Cir.", |
| "year": 1973, |
| "jurisdiction": "federal", |
| "prompt_type": "core_extraction", |
| "opinion_text": "…public-domain opinion excerpt…", |
| "messages": [ |
| { |
| "role": "system", |
| "content": "You are a legal analysis assistant. Return ONLY the final answer. No prefaces or meta-commentary." |
| }, |
| { |
| "role": "user", |
| "content": "From the opinion text, list: (1) procedural posture, (2) issues, (3) holdings, (4) standards of review, (5) disposition.\n\nOPINION TEXT:\n…" |
| }, |
| { |
| "role": "assistant", |
| "content": "1) …\n2) …\n3) …\n4) …\n5) …" |
| } |
| ], |
| "source_ref": "CAP; citation: 474 F.2d 22 (8th Cir. 1973)" |
| } |
| ``` |
|
|
| ### Splits |
| - `train`: update after upload |
| - `validation`: update after upload |
| - `test` (optional): update after upload |
|
|
| > **Split policy:** Do **not** split tasks for the **same case** across train/val/test to avoid leakage. |
|
|
| --- |
|
|
| ## How to Use |
|
|
| ### Load with 🤗 Datasets |
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("sik247/lexpt-law-sft") # replace with your repo id |
| print(ds) |
| print(ds["train"][0]) |
| ``` |
|
|
| ### Use with Chat Templates (Transformers) |
| ```python |
| from transformers import AutoTokenizer |
| tok = AutoTokenizer.from_pretrained("unsloth/gpt-oss-20b") # or your base |
| |
| sample = ds["train"][0]["messages"] |
| prompt = tok.apply_chat_template(sample, add_generation_prompt=True, tokenize=False) |
| ``` |
|
|
| --- |
|
|
| ## Curation & Construction |
| - **Sources:** public-domain opinions (e.g., CAP). |
| - **Selection:** appellate/habeas cases and issues suited for structured outputs (lists, checklists, IRAC). |
| - **Annotation:** prompts and answers authored by legal-knowledgeable contributors; emphasis on **final-answer-only** style. |
| - **Preprocessing:** remove site boilerplate; normalize whitespace/quotes; ensure consistent role formatting; de-duplicate near-identical snippets. |
|
|
| --- |
|
|
| ## Quality Control |
| - Spot checks for: (i) factual alignment with the opinion excerpt, (ii) formatting adherence (lists/IRAC), (iii) concise, jurisdiction-aware language. |
| - Where uncertainty exists, assistant outputs avoid invented facts/citations and prefer “Insufficient information.” |
|
|
| --- |
|
|
| ## Ethical Considerations & Limitations |
| - **Not legal advice.** This dataset trains formatting and structure for legal analysis; always verify with primary sources. |
| - **Coverage:** U.S. appellate caselaw; not exhaustive across jurisdictions or dates. |
| - **Model risk:** Misstatements of doctrine or miscitation can occur; downstream users should validate. |
| - **Bias:** Judicial texts may reflect historical or jurisdictional bias; outputs may inherit such patterns. |
|
|
| --- |
|
|
| ## Licensing |
| - **Opinion texts:** Public domain (as supplied by CAP and similar sources). |
| - **Prompts & annotations:** © 2025 sik247, released under **CC-BY-4.0**. |
| - When redistributing, include attribution: *“sik247 / LEXPT Law SFT (CAP subset)”*. |
|
|
| --- |
|
|
| ## Citation |
| If you use this dataset, please cite: |
| ``` |
| sik247. LEXPT Law SFT (CAP subset). 2025. Hugging Face Dataset. |
| ``` |
| And acknowledge the public-domain opinion sources (e.g., CAP) per their attribution guidance. |
|
|
| --- |
|
|
| ## Maintainer |
| - **Author/Maintainer:** `sik247` |
| - Issues/requests: open a Discussion on the dataset page. |
|
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| --- |
|
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| ## Changelog |
| - **v1.0** — Initial release with CAP-based opinion excerpts, 15 task templates, and ChatML records. Update counts and add additional jurisdictions in subsequent versions. |
