MedSpellCount-QA

Dataset Summary

MedSpellCount-QA is a lightweight dataset for orthographic counting framed as question-answering over medical terms.
Each input is a short natural-language question like:

“How many r are in warfarine?”

The output is the correct count as an integer (e.g., 1). This format is convenient for GRPO (Group Relative Policy Optimization) or other RL-style post-training, where a simple correctness reward compares multiple candidates per prompt.

Why a distinct dataset? Counting letters in real medical vocabulary is a simple, objective task that stresses spelling attention and string reasoning without requiring external knowledge.

Use Cases

• GRPO training: generate K candidates per prompt and reward exact correctness.
• Instruction/QA fine-tuning for robustness to orthographic queries.
• Eval of character-level attention and tokenization effects on medical terms.

Languages

• English prompts; terms are predominantly medical.

Dataset Structure

Data Fields

• input (string, required): the question, e.g.,
  How many 'r' in 'warfarine'?
• output (integer, required): the correct count as text, e.g., 1.

Data Instances

{
  "input": "How many 'r' in 'warfarine'?",
  "output": 1
}

```python
from datasets import load_dataset

ds = load_dataset("mkurman/MedSpellCount-QA", split='train')

print(ds)
print(ds[0])