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