dvr76/india-synthetic-property-maintenance-tickets

Indian Synthetic Property Maintenance Ticket Triage

A dataset of property maintenance tickets in English and Hinglish (Hindi-English code-mixed), annotated with structured triage labels for supervised fine-tuning of LLMs.

Dataset Description

This dataset contains tenant-submitted maintenance tickets from residential properties. Each ticket is labeled with structured fields that a triage system needs to extract: issue classification, urgency level, vendor routing, and entry requirements.

The dataset was created to fine-tune LLMs (specifically Qwen3-0.6B via QLoRA) for automated maintenance ticket triage in property management systems.

Languages

• English
• Hinglish (Hindi-English code-mixed, written in Latin script)

Source

Tickets were manually authored and annotated to cover a realistic distribution of maintenance scenarios including plumbing, electrical, structural, appliance, and pest-related issues.

Dataset Structure

Data Fields

Field	Type	Description
ticket_text	string	Raw tenant-submitted maintenance ticket text. This is the model input.
is_maintenance_request	bool	Whether the ticket describes an actual maintenance issue (true) or is an unrelated query like rent or amenities (false).
issues	list of objects	One or more issues extracted from the ticket. Each contains the four fields below.
issues[].category	string	Top-level issue type. One of: plumbing, electrical, structural, appliance, pest_control, general.
issues[].sub_category	string	Specific issue. Examples: leaking_tap, pipe_leak, burning_smell_switchboard, dampness_mold, water_seepage.
issues[].location	string	Where in the unit. Examples: kitchen, bathroom, bedroom, hall, balcony.
issues[].urgency	string	One of: LOW, MEDIUM, HIGH, CRITICAL.
vendor_type	string	Professional to dispatch. One of: licensed_plumber, electrician, civil_contractor, general_handyman, pest_control_service, appliance_technician.
entry_required	bool	Whether physical entry into the tenant's unit is needed for inspection or repair.

Data Splits

Split	Purpose	Description
train	Model training	Used for supervised fine-tuning.
validation	Hyperparameter tuning	Evaluated every N steps during training to detect overfitting.
test	Final evaluation	Held out entirely. Used only after training is complete.

Example

{
  "ticket_text": "kitchen wall near the window has some greenish patches, started small but slowly spreading over last few weeks",
  "is_maintenance_request": true,
  "issues": [
    {
      "category": "structural",
      "sub_category": "dampness_mold",
      "location": "kitchen",
      "urgency": "MEDIUM"
    }
  ],
  "vendor_type": "civil_contractor",
  "entry_required": true
}

Usage

from datasets import load_dataset

dataset = load_dataset("dvr76/india-synthetic-property-maintenance-tickets")

# Access splits
train = dataset["train"]
val = dataset["validation"]
test = dataset["test"]

# Print first example
print(train[0])

Intended Use

• Fine-tuning LLMs for structured information extraction from maintenance tickets.
• Building automated triage and dispatch systems for property management.
• Benchmarking structured extraction models on informal, multilingual text.

Limitations

• The dataset is relatively small. Models fine-tuned on it may not generalize to maintenance domains outside residential property (e.g., industrial, commercial).
• Hinglish examples use Latin script only. Devanagari Hindi is not represented.
• Urgency labels are subjective and based on the annotator's judgment. Different property managers may assign different urgency levels to the same issue.
• The dataset does not cover every possible maintenance category. Edge cases like fire damage, flooding, or structural collapse are underrepresented.

Fine-Tuned Model

A Qwen3-0.6B model fine-tuned on this dataset is available at: dvr76/ticket-triage-qwen3

License

MIT