Create README.md

README.md

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+---
+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.