metadata
license: cc-by-4.0
language:
- en
tags:
- legal
- india
- nlp
- knowledge-graph
- matrimonial
- 498a
- judicial
size_categories:
- 1K<n<10K
task_categories:
- text-classification
- token-classification
- summarization
configs:
- config_name: sc
data_files: sc_enriched.csv
- config_name: hc_karnataka
data_files: hc_karnataka.csv
- config_name: combined
data_files: hc_matrimonial.csv
IMLJD — Indian Matrimonial Litigation Judgment Dataset
Computational dataset of 4897 Indian court judgments on matrimonial disputes (IPC 498A, DV Act, CrPC 482 quashing petitions), built from AWS Open Data judicial archives.
Dataset Description
Code and knowledge graph: https://gitlab.com/joyboseroy/imljd
| Sub-corpus | Cases | Court | Period | Precision |
|---|---|---|---|---|
| SC matrimonial | 1,474 | Supreme Court of India | 2000–2024 | Medium (broad filter) |
| Karnataka HC | 2,139 | Karnataka High Court | 2018–2024 | High (482 confirmed) |
| Total | 3,613 | |||
| Combined HC | 3,423 | Karnataka + Delhi + others | 2018–2024 | Mixed |
| Grand Total | 4,897 |
Key Statistics
| Metric | Value |
|---|---|
| Total cases | 3,613 |
| SC quash success rate | 57.6% |
| HC (Karnataka) quash success rate | 39.7% |
| Cases with CrPC 482 | 2,179 |
| Cases with IPC 498A | 192 |
| KG nodes | 1,520 |
| KG edges | 13,364 |
Columns
SC sub-corpus (sc_enriched.csv)
| Column | Description |
|---|---|
| case_id | Stable identifier |
| title | Case title |
| petitioner / respondent | Party names |
| year | Year (2000–2024) |
| case_type | quash / appeal / maintenance / bail / other |
| outcome | quashed / allowed / dismissed / settled / disposed / partly_allowed |
| statutes | Pipe-delimited statute list |
| disposal_nature | Raw disposal string |
| mediation_mentioned | bool |
| settlement_mentioned | bool |
| relatives_accused | bool |
| judicial_criticism_misuse | bool |
| arnesh_kumar_cited | bool |
| rajesh_sharma_cited | bool |
HC Karnataka sub-corpus (hc_karnataka.csv)
| Column | Description |
|---|---|
| title | Case title (CRL.P/NNNNN/YYYY format) |
| judge | Presiding judge |
| decision_date | Date of judgment |
| disposal_nature | ALLOWED / DISMISSED / DISPOSED / Partly Allowed |
| outcome | quashed / dismissed / disposed / partly_allowed |
| _year | Year (2018–2024) |
| _bench | Bench (karhcdharwad / karhckalaburagi / karnataka_bng_old) |
| statutes | CrPC 482 (all cases) |
| case_type | quash (all cases) |
Data Sources
Both sub-corpora built from AWS Open Data (no credentials needed):
s3://indian-supreme-court-judgments/s3://indian-high-court-judgments/
Usage
from datasets import load_dataset
# Supreme Court cases
sc = load_dataset("joyboseroy/imljd", "sc")
# Karnataka HC quash petitions
hc = load_dataset("joyboseroy/imljd", "hc_karnataka")
# Basic analysis
import pandas as pd
df = pd.DataFrame(sc["train"])
quash = df[df["case_type"] == "quash"]
print(f"SC quash success rate: {(quash['outcome']=='quashed').mean()*100:.1f}%")
Knowledge Graph
A NetworkX/GEXF knowledge graph is included in the repository:
- Nodes: Case, Statute, Court, Outcome, Precedent, Year
- Edges: INVOKES, HEARD_BY, RESULTS_IN, CITES, DECIDED_IN
Open data/kg/imljd_graph.gexf in Gephi for visualisation.
Ethical Considerations
- Public court judgments only
- Names present as in original public records
- Recommended: anonymisation pass before NLP model training
- Not suitable for "false case detection" — ground truth doesn't exist cleanly
- Framing: procedural fairness research, not case outcome prediction
Citation
@dataset{boseroy2026imljd,
title = {IMLJD: Indian Matrimonial Litigation Judgment Dataset},
author = {Bose, Joy},
year = {2026},
url = {https://huggingface.co/datasets/joyboseroy/imljd},
note = {3,613 cases, Supreme Court 2000-2024 and Karnataka HC 2018-2024}
}
Related work:
@article{boseroy2026falkor,
title = {FalkorDB-IRAC: Graph-Grounded Legal Reasoning},
author = {Bose, Joy},
year = {2026},
url = {https://arxiv.org/abs/2605.14665}
}