README.md

---
datasets:
- independently-platform/tasky
language:
- en
- it
base_model:
- google/functiongemma-270m-it
library_name: transformers
---

  # Tasky

  ## About the model
  This model is a fine-tuned **function-calling assistant** for a todo/task application. It maps user requests to one of four tools and produces valid tool
  arguments according to the schema in `AI-TRAINING-TOOLS.md`.

  - **Base model:** `google/functiongemma-270m-it`
  - **Primary languages:** English and Italian (with light spelling errors/typos to mimic real users)
  - **Task:** Structured tool selection + argument generation

  ## Intended Use
  Use this model to translate natural language task requests into tool calls for:
  - `create_tasks`
  - `search_tasks`
  - `update_tasks`
  - `delete_tasks`

  It is designed for **task/todo management** workflows and should be paired with strict validation of tool arguments before execution.

  ### Example
  **Input (user):**

  Aggiungi un task per pagare la bolletta della luce domani mattina


  **Expected output (model):**
  ```json
  {
    "tool_name": "create_tasks",
    "tool_arguments": "{\"tasks\":[{\"content\":\"pagare la bolletta della luce\",\"dueDate\":\"2026-01-13T09:00:00.000Z\"}]}"
  }

  ## Training Data

  Synthetic, bilingual tool-calling data built from the tool schema, including:

  - Multiple phrasings and paraphrases
  - Mixed English/Italian prompts
  - Light typos and user mistakes in user_content
  - Broad coverage of optional parameters

  Splits:

  - Train: 1,500 examples
  - Eval: 500 examples

  ## Training Procedure

  - Fine-tuning on synthetic tool-calling samples
  - Deduplicated examples
  - Balanced coverage of all tools and key parameters

  ## Evaluation

  Reported success rate: 99.5% on the 500‑example eval split vs 0% base model.
  Success was measured as exact match on the predicted tool name and the JSON arguments after normalization.

  ## Limitations

  - Trained for a specific tool schema; not a general-purpose assistant.
  - Outputs may include incorrect or incomplete tool arguments; validate before execution.
  - Language coverage is strongest in English and Italian.
  - Synthetic data may not capture all real-world user phrasing or ambiguity.