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license: mit |
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# 🚗 BEst DrivEr’s License Performer (BEEP) Dataset |
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**BEEP** is a challenge benchmark designed to evaluate large language models (LLMs) through a simulation of the Italian driver’s license exam. This dataset focuses on understanding traffic laws and reasoning through driving situations, replicating the complexity of the Italian licensing process. |
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## 📁 Dataset Structure |
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| Column | Data Type | Description | |
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| ---------------------- | ------------- | --------------------------------------------------------------------------- | |
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| `Categorisation Structure` | [String] | Hierarchical categorisation of major, minor, and subcategories for each question | |
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| `Question Text` | [String] | The actual content of the question | |
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| `True Answer` | [Boolean] | True or false answer | |
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| `Figure` | [String] | Reference to an accompanying figure, if present | |
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> **Note**: Questions are organised into a classification system that reflects the complexity of road rules and signage. |
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## 📊 Summary Statistics |
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- **Total Questions**: 2920 |
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- **Last Updated**: 01/07/2020 |
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## 🔍 Key Features |
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- **Source**: The dataset is derived from the publicly accessible official document "Listato A e B", provided by the Italian Ministry of Infrastructure and Transport. It includes all questions related to driver’s license categories A and B. |
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- **Hierarchical Structure**: Questions are classified into major categories, such as "Road Signage", and further subdivided into minor and subcategories for precise categorisation. |
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- **Question Format**: The dataset primarily consists of true/false questions aimed at evaluating knowledge of traffic laws, signage, and driving behavior. |
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- **Exclusions**: For the **CALAMITA** challenge, questions containing images are excluded, focusing solely on text-based questions. |
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## 🛠️ Using the Dataset |
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### Loading Example |
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You can load this dataset in Python using `pandas`: |
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```python |
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import pandas as pd |
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# Load the dataset |
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df = pd.read_csv('beep_data.csv') |
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# Display the first few rows of the dataset |
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print(df.head()) |
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