| --- |
| license: other |
| tags: |
| - science |
| - mathematics |
| - reasoning |
| - multiple-choice |
| - question-answering |
| task_categories: |
| - multiple-choice |
| - math |
| - question-answering |
| language: |
| - en |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # StreetMath Dataset |
|
|
| ## Dataset Summary |
|
|
| The **Street Math dataset** is a synthetic reasoning benchmark that evaluates a model’s ability to **approximate sums of decimal prices** in everyday shopping scenarios. |
| Each example presents a list of item prices, and the model must select the approximate total cost (before tax) from multiple-choice options. |
|
|
| The dataset is designed to test **numerical reasoning, estimation, and handling of decimal numbers**. |
| Language: **English**. |
| Domain: **mathematics applied to real-world shopping tasks**. |
|
|
| ## Languages |
|
|
| - **English (en)**: prompts and options are written in plain English, with U.S. dollar formatting for prices. |
|
|
| ## Data Instances |
|
|
| Example instance: |
|
|
| ```json |
| { |
| "id": "basket_sum_000243", |
| "topic": "basket_sum", |
| "subtopic": "decimal_prices", |
| "prompt": "You’re buying these items: $3.55, $15.42, $4.56, $12.63, $6.08. About how much will you pay (before tax)?", |
| "labels": ["A", "B", "C", "D"], |
| "correct_label": "A", |
| "choices": ["$43.00", "$14.11", "$42.24", "$182.80"], |
| "correct_option": 0, |
| "metadata": { |
| "exact_value": 42.24, |
| "good_value": 43.0, |
| "mild_value": 14.11, |
| "way_value": 182.8, |
| "prices": [3.55, 15.42, 4.56, 12.63, 6.08] |
| }, |
| "split": "test" |
| } |
| ``` |
|
|
| ## Intended Uses |
|
|
| The Basket Sum dataset is intended for: |
| - **Benchmarking language models** on basic numerical reasoning and arithmetic in natural language contexts. |
| - **Evaluating estimation skills**: testing whether models can provide approximate answers rather than exact calculations. |
| - **Educational and research purposes**: studying how models handle everyday math tasks such as adding decimal prices. |
|
|
| This dataset is **not** intended for: |
| - Financial or accounting applications. |
| - Real-world shopping or economic forecasting. |
| - Any critical decision-making where incorrect numerical outputs could cause harm. |
|
|
| ## Format |
|
|
| - **File type:** JSON Lines (`.jsonl`) |
| - **Each line:** one example as a JSON object |
| - **Compatible with:** Hugging Face `datasets` library (`load_dataset("json", data_files="...")`) |
|
|
|
|
| ## How to Get the Dataset |
|
|
| You can easily load this dataset from the Hugging Face Hub using the `datasets` library: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load the dataset |
| dataset = load_dataset("Chiung-Yi/StreetMath") |
| |
| # Access the test split |
| test_dataset = dataset["test"] |
| |
| # Example: print the first item |
| print(test_dataset[0]) |
| ``` |
|
|
|
|
| ## Limitations and Ethical Considerations |
|
|
| **Licensing**: The license is currently unspecified. For any public or commercial use, it is necessary to verify the terms with the author. |
|
|
|
|
| ## Dataset Curators |
|
|
| - Original dataset created by [Chiung-Yi](https://huggingface.co/Chiung-Yi) |
|
|
| ### Disclaimer |
| This dataset card was written by a community contributor to improve documentation. |
| If you are the original author or know additional details, feel free to submit a pull request or open an issue to update this card. |
|
|
