---
license: apache-2.0
language:
- en
size_categories:
- n<1K
task_categories:
- question-answering
- multiple-choice
configs:
- config_name: benchmark
  data_files:
  - split: test
    path: dataset.json
tags:
- geospatial
annotations_creators:
- expert-generated
paperswithcode_id: mapeval-textual
---


# MapEval-Textual

[MapEval](https://arxiv.org/abs/2501.00316)-Textual is created using [MapQaTor](https://arxiv.org/abs/2412.21015).

## Usage

```python
from datasets import load_dataset

# Load dataset
ds = load_dataset("MapEval/MapEval-Textual", name="benchmark")

# Generate better prompts
for item in ds["test"]:
    # Start with a clear task description
    prompt = (
        "You are a highly intelligent assistant. "
        "Based on the given context, answer the multiple-choice question by selecting the correct option.\n\n"
        "Context:\n" + item["context"] + "\n\n"
        "Question:\n" + item["question"] + "\n\n"
        "Options:\n"
    )
    
    # List the options more clearly
    for i, option in enumerate(item["options"], start=1):
        prompt += f"{i}. {option}\n"
    
    # Add a concluding sentence to encourage selection of the answer
    prompt += "\nSelect the best option by choosing its number."

    # Use the prompt as needed
    print(prompt)  # Replace with your processing logic
```

## Citation

If you use this dataset, please cite the original paper:

```
@article{dihan2024mapeval,
  title={MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models},
  author={Dihan, Mahir Labib and Hassan, Md Tanvir and Parvez, Md Tanvir and Hasan, Md Hasebul and Alam, Md Almash and Cheema, Muhammad Aamir and Ali, Mohammed Eunus and Parvez, Md Rizwan},
  journal={arXiv preprint arXiv:2501.00316},
  year={2024}
}
```