Datasetpublic

Qrzysztof/functiongemma-prepaid-cards-tool-calling

FunctionGemma Prepaid-Cards Tool-Calling Dataset Synthetic training data for fine-tuning google/functiongemma-270m-it so it can recognize chat intents for buying prepaid cards, checking balances, and viewing transaction history, and emit the correct tool call. Tools Tool Purpose purchase_card(amount, card_type, email?, currency?) Buy a Digital Prepaid Visa or Virtual Prepaid Mastercard get_card_balance(card_number) Check the balance of a card… See the full description on the dataset page: https://huggingface.co/datasets/Qrzysztof/functiongemma-prepaid-cards-tool-calling.

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face

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Dataset Card

FunctionGemma Prepaid-Cards Tool-Calling Dataset

Synthetic training data for fine-tuning google/functiongemma-270m-it so it can recognize chat intents for buying prepaid cards, checking balances, and viewing transaction history, and emit the correct tool call.

Tools

Tool	Purpose
`purchase_card(amount, card_type, email?, currency?)`	Buy a Digital Prepaid Visa or Virtual Prepaid Mastercard
`get_card_balance(card_number)`	Check the balance of a card
`get_transaction_history(card_number, limit?)`	List a card's transactions

card_type values: digital_prepaid_visa | virtual_prepaid_mastercard.

Format

Each row follows the exact structure TRL's SFTTrainer expects for FunctionGemma:

—messages — conversation in the OpenAI-style format (developer / user / assistant with tool_calls / tool responses)
—tools — JSON schemas generated with transformers.utils.get_json_schema
—lang — ISO 639-1 code of the user messages
—kind — sample category (see below)
—template_id — deterministic id used for the train/test split
—split — train or test (deterministic)

Sample kinds

—purchase — single-turn card purchase requests
—balance — single-turn balance requests (full, masked ****1234, 1234...9876, ending in 1234 card numbers)
—tx — single-turn transaction-history requests
—chain_split — multi-turn: card number given in message 1, request in message 2
—chain_clarify — multi-turn: request without card number → assistant asks → user provides → tool call
—chain_purchase_clarify — multi-turn purchase: amount/type collected across turns
—chain_retry — tool called with missing arg → error response → clarification → retry
—chain_full / chain_full_balance / chain_full_tx — full function-calling loop: call → response → assistant text
—chain_two / chain_purchase_balance — long multi-tool conversations
—greet / negative — small talk that must not trigger a tool call

Languages

User messages are hand-translated in 84 languages (list in the language tag above). English has the largest template set (60+ purchase phrasings, 45 balance, 45 transaction).

Train/test split

—5 languages are fully held out of training: ja, ko, ar, sw, ur
—~12% of English templates are held out
—Split is deterministic per template_id

Build

bash

python3 build_dataset.py --push

Training

See the companion training script in this repository (train.py) which follows the official FunctionGemma fine-tuning guide.