IndicLaw-Class: Code-Mixed Legal Intent Classifier

IndicLaw-Class is a lightweight multilingual transformer-based classifier that identifies legal intent from code-mixed Indian queries (e.g., Kannada-English, Hinglish). It is fine-tuned on citizen-style queries for real-world legal triage applications.

---

Model Overview

• Architecture: distilbert-base-multilingual-cased
• Task: Multi-class text classification (6 legal categories)
• Input Style: Informal, code-mixed queries like:
  • divorce file maadbeku without husband consent
  • builder flat delay case haakbeku
  • rent refund maadbeku, owner refusing

---

Legal Categories

The model classifies input into one of the following categories:

Label	Description
Family Law	Divorce, custody, alimony, marriage
Property Law	Inheritance, land disputes, transfer
Criminal Law	FIRs, police misconduct, assault
Consumer Complaints	E-commerce, refund issues, builders
Rent & Tenancy	Eviction, deposit disputes, lease
Public Services	Certificates, ID updates, ration

---

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: More information needed Hours used: More information needed Cloud Provider: More information needed Compute Region: More information needed Carbon Emitted: More information needed

---

Citation

@misc{nishanth_prakash_2025,
    author       = { nishanth prakash },
    title        = { IndicLaw-Class (Revision 87ae96e) },
    year         = 2025,
    url          = { https://huggingface.co/nprak26/IndicLaw-Class },
    doi          = { 10.57967/hf/5964 },
    publisher    = { Hugging Face }
}

---

How to Get Started With the Model

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import torch

# Load model and tokenizer from your local folder
model_dir = "./indiclaw-classifier"

tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForSequenceClassification.from_pretrained(model_dir)

# Load label map (from labels.txt you saved earlier)
label_map = {}
with open(f"{model_dir}/labels.txt", "r") as f:
    for line in f:
        idx, label = line.strip().split("\t")
        label_map[int(idx)] = label

# Create pipeline
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)

# Test inputs
examples = [
    "wife divorce file maadbeku",
    "flat possession delay aadmele builder case file madbeku",
    "tenant evict maadbeku no notice"
]

# Run predictions
for text in examples:
    result = classifier(text)[0]
    label_str = result["label"]
    if "label" in label_str.lower():
      label_id = int(label_str.split("_")[-1])
    else:
      label_id = int(label_str)
    label_name = label_map[label_id]
    print(f"Input: {text}\nPredicted: {label_name} (confidence: {result['score']:.2f})\n")


---