nassimjp/mix-riddles-reasoning-dataset

Mix Riddles Reasoning Dataset

A dataset for exploring riddles, puzzles, logical reasoning, and question-answering with language models.

Dataset repository: nassimjp/mix-riddles-reasoning-dataset

Overview

The Mix Riddles Reasoning Dataset is intended for working with riddle-based questions and reasoning tasks. It can be explored for language-model training, reasoning experiments, evaluation pipelines, and educational applications, subject to verification of the dataset's actual contents and licensing terms.

Riddles can help researchers investigate how models interpret questions, identify relevant clues, distinguish between plausible answers, and solve problems that require more than simple recall.

Dataset information

Property	Details
Dataset name	Mix Riddles Reasoning Dataset
Hugging Face repository	nassimjp/mix-riddles-reasoning-dataset
Data modality	Text, subject to repository verification
Main topic	Riddles and reasoning
Intended applications	Reasoning research, question-answering, and model training
Languages	To be verified from the dataset files
Number of examples	See the current dataset viewer
Data format	See the repository files
License	Must be confirmed from the dataset's actual license and source terms

Goals

The dataset may be useful for experiments involving:

• Riddle solving: Understanding clues and identifying possible solutions.
• Logical reasoning: Examining the relationship between a question's premises and its answer.
• Question-answering: Generating relevant answers to puzzle-style questions.
• Instruction tuning: Preparing compatible examples for supervised fine-tuning, after checking the actual schema.
• Evaluation: Testing model performance on selected riddle and reasoning tasks.
• Multilingual research: Investigating language coverage if multiple languages are present in the data.

These are potential applications, not claims that every category has been independently verified in the current release.

Dataset structure

The exact columns and their data types must be obtained from the current dataset files. Do not assume that every example contains a question, reasoning trace, and final answer.

Inspect the available columns with Python:

from datasets import load_dataset

dataset = load_dataset(
    "nassimjp/mix-riddles-reasoning-dataset"
)

print(dataset)
print(dataset.keys())

for split_name, split in dataset.items():
    print(f"\nSplit: {split_name}")
    print("Rows:", len(split))
    print("Columns:", split.column_names)

    if len(split) > 0:
        print("Example:", split[0])

The output identifies the available splits, actual column names, and an example record. Use those results when preparing training or evaluation pipelines.

Loading the dataset

Install the required library:

pip install -U datasets

Load the dataset:

from datasets import load_dataset

dataset = load_dataset(
    "nassimjp/mix-riddles-reasoning-dataset"
)

print(dataset)

Inspect a sample:

for split_name, split in dataset.items():
    if len(split):
        print(f"Split: {split_name}")
        print(split[0])
        break

Basic data inspection

The following example checks for empty values in string columns without assuming particular field names:

from datasets import load_dataset

dataset = load_dataset(
    "nassimjp/mix-riddles-reasoning-dataset"
)

for split_name, split in dataset.items():
    print(f"\n--- {split_name} ---")
    print("Examples:", len(split))
    print("Columns:", split.column_names)

    for column in split.column_names:
        if split.features[column].dtype == "string":
            empty_count = sum(
                1
                for value in split[column]
                if value is None or not value.strip()
            )
            print(f"{column}: {empty_count} empty values")

This is a basic inspection step, not a complete quality assessment.

Preparing data for model training

Before fine-tuning a model, inspect the dataset schema and map its actual fields to the input and target format expected by the training framework.

A generic example for a dataset with verified question and answer columns is:

from datasets import load_dataset

dataset = load_dataset(
    "nassimjp/mix-riddles-reasoning-dataset"
)

# Use this mapping only if these columns actually exist.
def format_example(example):
    return {
        "text": (
            f"Question: {example['question']}\n"
            f"Answer: {example['answer']}"
        )
    }

# Uncomment only after verifying the column names:
# formatted = dataset.map(format_example)

If the dataset uses different field names, adapt the mapping accordingly. If reasoning traces are available, inspect their quality and provenance before using them as training targets.

Quality assurance

For reliable training and evaluation, consider checking:

1. Answer correctness: Confirm that each answer actually solves the corresponding riddle.
2. Logical consistency: Identify contradictions, ambiguous premises, and unsupported conclusions.
3. Duplicate detection: Find repeated questions and near-duplicate examples.
4. Data cleanliness: Remove malformed records and unintended formatting artifacts.
5. Language quality: Check spelling, grammar, and language consistency.
6. Reasoning quality: Where explanations are present, verify that they support the proposed answers.
7. Evaluation leakage: Check for overlap with evaluation and benchmark examples.
8. Source and licensing: Verify the rights and restrictions applicable to the original material.

Automated checks can help prioritize review, but they do not establish that all answers are correct.

Limitations

• The number of examples, exact schema, language distribution, and source composition should be confirmed from the current repository.
• Riddles may be ambiguous or depend on cultural, linguistic, or contextual knowledge.
• A plausible answer is not necessarily a logically correct answer.
• Model-generated explanations, if present, may contain errors or unsupported reasoning.
• Training on a dataset does not guarantee improved reasoning on unfamiliar problems.
• The dataset should not be treated as a validated benchmark unless its evaluation methodology and answer correctness have been independently established.
• Users should inspect the source material and license before redistribution or commercial use.

License and usage

Important: The applicable license has not been independently verified for this README.

Before using or redistributing this dataset, check the repository's current license metadata, any license file, and the terms of the original source material.

Do not interpret this README as granting permission to redistribute third-party content or use it commercially. If the repository's metadata confirms a specific license, replace this section with the verified license name and a link to its terms.

Citation

If you use this dataset in research, cite the repository:

@dataset{nassimjp_mix_riddles_reasoning_dataset,
  author    = {nassimjp},
  title     = {Mix Riddles Reasoning Dataset},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/nassimjp/mix-riddles-reasoning-dataset}
}

This is a repository citation template. Add a verified publication year, version, DOI, or fuller author details if those become available.

Maintainer

Hugging Face account: @nassimjp

For questions, corrections, and contributions, use the repository's community discussion page.

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