Datasetpublic

svr99991/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… See the full description on the dataset page: https://huggingface.co/datasets/svr99991/india-synthetic-property-maintenance-tickets.

sourceHugging Facemitupdated 6mo agoView on Hugging Face

0likes33downloads

Dataset Card

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

json

{
  "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

python

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