| --- |
| license: cc0-1.0 |
| task_categories: |
| - text-classification |
| - question-answering |
| language: |
| - en |
| tags: |
| - logistics |
| - supply-chain |
| - benchmark |
| - transportation |
| --- |
| |
| # LogistikaBench Dataset Structure 🚚 |
|
|
| **LogistikaBench** is a specialized evaluation benchmark designed to test the domain knowledge and reasoning capabilities of AI models in logistics, transport, and supply chain management. |
| The dataset contains 1,446 multiple-choice questions across supply chain domains: |
| - Material Handling |
| - Transport |
| - Warehousing |
| - Supply Chain Management |
| - Procurement |
|
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| More than one right answer can be available. For instance, [0, 2] in the answer column means the first and the third answers are correct. |
|
|
| No questions or answers in this dataset were generated using AI (such as large language models). |
| The questions are mostly based on the following open-access textbooks and other free sources: |
| 1. Caplice, C., & Ponce, E. (2023). MITx MicroMasters Program in SCM Key Concepts. MIT Center for Transportation & Logistics. |
| 2. Ivanov, D., Tsipoulanidis, A., & Schönberger, J. (2017). Global supply chain and operations management. A decision-oriented introduction to the creation of value. ( http://global-supply-chain-management.de/Slides) |
| 3. Koningsveld, M., Verheij, H. J., Taneja, P., & de Vriend, H. J. (2021). Ports and Waterways: Navigating the changing world. |
| 4. Carpenter, M. A., & Dunung, S. P. (2012). Challenges and opportunities in international business. Creative Commons by-nc-sa, https://2012books.lardbucket.org/ |
| 5. EU/UN/ITF/OECD. (2019). Glossary for Transport Statistics. |
|
|
| ## Limitations & Potential Errors |
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|
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| All test items are derived strictly from curated, authentic source materials used in actual university-level examinations and assessments. |
| Every question reflects pedagogical evaluations designed for real academic environments. |
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| While rigorous curation was applied, human and structural limitations are inevitable: |
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| Answers: Human error during transcription or answer key compilation means inaccuracies are possible and even likely. |
| Subject Categorization: Due to the human error or multidisciplinary nature of the domain with overlapping category. |
| |
| Call for Community Improvement |
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|
| To maintain dataset quality, we encourage the community, educators, and researchers to help identify and correct these flaws. |
| You can contribute improvements in the following standard ways: |
|
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| GitHub Pull Requests: Submit direct corrections for answer keys or categorization via official repository. |
| |
| Dataset Discussions / Issues: Report ambiguous questions, potential answer errors, or classification misalignments on the Kaggle or Hugging Face community tabs. |
| |
|
|
| ## 🚀 Quick Start: Run the Benchmark in Google Colab |
|
|
| Evaluate an open-source model on **LogistikaBench** for free using a Google Colab T4 GPU |
|
|
| 1. Click the button below to open a fresh notebook: |
| [](https://colab.research.google.com) |
| 2. Copy the evaluation script evaluate.py` file. |
| 3. Paste it into your Colab cell, change the `model_id` to model you want to test, and hit **Play**! |
| |
| ## 🤝 How to Submit Scores (Leaderboard Contributions) |
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| Add scores for proprietary models (e.g., GPT, Claude, Grok, Gemini) or your own fine-tuned models - contribute to the leaderboard by submitting a Pull Request: |
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| 1. **Run the evaluation script** on your end using your own API keys. |
| 2. **Edit the `README.md` file** directly on this dataset repository. |
| 3. **Add your model details** to the Leaderboard markdown table in alphabetical or ranked order: |
| * **Model Name** | **Provider** | **Strict Match Accuracy (%)** | **Contributor / Handle** |
| 4. **Open a Pull Request**. Once reviewed and merged, your score will appear live on the official leaderboard |
| --- |
