README.md

metadata

license: cc-by-nc-sa-4.0
task_categories:
  - text-retrieval
language:
  - en
extra_gated_fields:
  Full Name: text
  Affiliation (Organization/University): text
  Designation/Status in Your Organization: text
  Country: country
  I want to use this dataset for (please provide the reason(s)): text
  IL-PCSR dataset is free for research use but NOT for commercial use; do you agree if you are provided with the IL-PCSR dataset, you will NOT use for any commercial purposes? Also do you agree that you will not be sharing this dataset further or uploading it anywhere else on the internet: checkbox
  DISCLAIMER The dataset is released for research purposes only and authors do not take any responsibility for any damage or loss arising due to usage of data or any system/model developed using the dataset: checkbox
tags:
  - legal
  - indian law
  - legal retrieval
  - statute retrieval
  - precedent retrieval
size_categories:
  - 1K<n<10K
pretty_name: IL-PCSR
config_names:
  - queries
  - statutes
  - precedents
dataset_info:
  - config_name: queries
    features:
      - name: id
        dtype: string
      - name: case_title
        dtype: string
      - name: date
        dtype: string
      - name: jurisdiction
        dtype: string
      - name: text
        list: string
      - name: rhetorical_roles
        list: string
      - name: relevant_statutes
        list: string
      - name: relevant_statute_ids
        list: string
      - name: relevant_precedents
        list: string
      - name: relevant_precedent_ids
        list: string
    splits:
      - name: train_queries
        num_examples: 5017
      - name: dev_queries
        num_examples: 627
      - name: test_queries
        num_examples: 627
  - config_name: statutes
    features:
      - name: id
        dtype: string
      - name: provision_name
        dtype: string
      - name: text
        list: string
    splits:
      - name: statute_candidates
        num_examples: 936
  - config_name: precedents
    features:
      - name: id
        dtype: string
      - name: case_title
        dtype: string
      - name: date
        dtype: string
      - name: jurisdiction
        dtype: string
      - name: text
        list: string
      - name: rhetorical_roles
        list: string
      - name: relevant_statutes
        list: string
      - name: relevant_statute_ids
        list: string
      - name: relevant_precedents
        list: string
      - name: relevant_precedent_ids
        list: string
    splits:
      - name: precedent_candidates
        num_examples: 3183
configs:
  - config_name: queries
    data_files:
      - split: train_queries
        path: train_queries*
      - split: dev_queries
        path: dev_queries*
      - split: test_queries
        path: test_queries*
    default: true
  - config_name: statutes
    data_files:
      - split: statute_candidates
        path: statute_candidates*
  - config_name: precedents
    data_files:
      - split: precedent_candidates
        path: precedent_candidates*

IL-PCSR (Indian Legal — Precedent & Statute Retrieval)

IL-PCSR: A dataset of Indian legal case queries annotated with relevant statutes and precedents, plus candidate pools of statutory provisions and precedent judgments for retrieval research.

---

Dataset Summary

IL-PCSR contains three configs:

• queries — case queries (judgments / case descriptions) annotated with relevant statutes and precedents.
• statutes — statutory provisions (candidate documents).
• precedents — prior judgments (candidate documents).

---

Languages

• English (Indian legal terminology and occasional code-switching).

---

License & Access

• License: CC-BY-NC-SA 4.0 (non-commercial, share-alike).
• This dataset is gated. To request access, users must complete the provided access form (the gate fields are defined in the front-matter). By requesting the dataset the user must explicitly agree to:
  • Use the dataset only for non-commercial research purposes, and
  • Not further redistribute or upload the dataset anywhere else.
• DISCLAIMER (must be accepted): the dataset is released for research purposes only; authors disclaim responsibility for any loss or damage from usage.

---

Dataset structure (detailed)

The YAML in the front-matter contains exact dataset_info and configs. Below is a readable description of the same schema.

Config: queries (default)

Splits

• train_queries: 5,017 examples
• dev_queries: 627 examples
• test_queries: 627 examples

Fields / schema

• id: string → unique id for the query.
• case_title: string → short title of the case.
• date: string → judgment date in ISO:YYYY-MM-DD format.
• jurisdiction: string → court or jurisdiction name.
• text: list[string] — tokenized/segmented text lines or paragraphs (stored as a list to preserve segments).
• rhetorical_roles: list[string] → role labels for each segment (aligned with text segments).
  • [Facts, Issue, Argument by Petitioner, Argument by Respondent, Statute Analysis, Precedent Analysis, Court Disclosure, Court Reasoning, NONE]
• relevant_statutes: list[string] → human-readable statute names referenced as relevant.
• relevant_statute_ids: list[string] → canonical IDs matching the statutes config id field.
• relevant_precedents: list[string] → human-readable precedent case titles.
• relevant_precedent_ids: list[string] → canonical IDs matching the precedents config id field.

Notes

• relevant_statute_ids and relevant_precedent_ids are the fields used to build ground-truth relevance sets (qrels) for retrieval evaluation.

---

Config: statutes

Splits

• statute_candidates: 936 examples

Fields

• id: string → unique provision id (matches relevant_statute_ids in queries).
• provision_name: string → statute/provision title.
• text: list[string] → provision text (segmented).

---

Config: precedents

Splits

• precedent_candidates: 3,183 examples

Fields / schema - Same as queries

Data provenance & preprocessing

• Sources: Publicly available Indian judgments and statutory texts (collected from public repositories and court websites).
• Curation steps (summary):
  • Document collection and deduplication.
  • Minimal normalization (unicode normalisation, whitespace clean-up).
  • Masking legal named entities and citations
  • Rhetorical segments (Facts, Issues, Reasoning, Judgment) provided according to IndianKanoon in rhetorical_roles.
  • Linking queries to statutes and precedents (producing the relevant_* fields).
• PII & legal caution: Users are responsible for additional PII removal or compliance checks; the dataset maintainers provide the data "as curated" and do not guarantee absence of sensitive information.

---

How to use

All of the examples assume you have access (i.e., gate accepted).

1) Load from Hugging Face Hub

from datasets import load_dataset

# Load queries config (default)
ds_queries = load_dataset("Exploration-Lab/IL-PCSR", name="queries")

# Access splits using the explicit split names
train_q = ds_queries["train_queries"]
dev_q   = ds_queries["dev_queries"]
test_q  = ds_queries["test_queries"]

# Example: inspect first query
print(train_q[0])

2) Load specific configs

# Statutes (candidate pool)
ds_statutes = load_dataset("Exploration-Lab/IL-PCSR", name="statutes")
statute_pool = ds_statutes["statute_candidates"]
print(statute_pool[0])

# Precedents (candidate pool)
ds_precedents = load_dataset("Exploration-Lab/IL-PCSR", name="precedents")
precedent_pool = ds_precedents["precedent_candidates"]
print(precedent_pool[0])

3) Local development (loading from a local repo folder)

# If you're testing locally (folder contains dataset script + README.md + data/)
ds_local_queries = load_dataset("</path/to/local/IL-PCSR>", name="queries")
print(ds_local_queries["train_queries"][0])

4) Building qrels (ground-truth) for retrieval evaluation

# Example: build a qrels-like dict mapping query id -> set(candidate_ids)
def build_qrels(queries_split):
    qrels = {}
    for ex in queries_split:
        qid = ex["id"]
        # combine statute ids and precedent ids if you want a single candidate set
        relevant = set(ex.get("relevant_statute_ids", []) + ex.get("relevant_precedent_ids", []))
        qrels[qid] = list(relevant)
    return qrels

qrels_train = build_qrels(train_q)
print("sample qrels:", list(qrels_train.items())[:2])

Citation

@inproceedings{il-pcsr2025,
  title = "IL-PCSR: Legal Corpus for Prior Case and Statute Retrieval",
  author = "Paul, Shounak and Ghumare, Dhananjay and Goyal, Pawan and Ghosh, Saptarshi and Modi, Ashutosh"
  booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
  month = nov,
  year = "2025",
  address = "Suzhou, China",
  publisher = "Association for Computational Linguistics",
  note = "To Appear"
}