README.md

---
license: mit
tags:
- code
- Text
- Science
- Math
- Logic
---

# Multi-Task Dataset

## Description

A large-scale multi-task dataset designed for training and evaluating AI models across **reasoning, mathematics, code, research, verification, data analysis, and general problem solving**.

## Content

* **100,000,001** examples
* **20+ task families**
* English + French
* Train / Validation / Test splits
* Structured reasoning and verification signals
* Multiple difficulty levels
* OOD and generalization-oriented examples

## Dataset Structure

| Split      | Percentage |        Examples |
| ---------- | ---------: | --------------: |
| Train      | 97.999999% |      98,000,000 |
| Validation |  0.999999% |       1,000,000 |
| Test       |  1.000000% |       1,000,001 |
| **Total**  |   **100%** | **100,000,001** |

## Task Categories

| Category                 | Share |   Examples |
| ------------------------ | ----: | ---------: |
| Mathematics              |   10% | 10,000,000 |
| Logic                    |    8% |  8,000,000 |
| Programming              |   10% | 10,000,000 |
| Data Analysis            |    8% |  8,000,000 |
| Reasoning                |   10% | 10,000,000 |
| Research                 |    6% |  6,000,000 |
| Fact Checking            |    5% |  5,000,000 |
| Self-Correction          |    6% |  6,000,000 |
| Instruction Following    |    6% |  6,000,000 |
| Planning                 |    5% |  5,000,000 |
| Constraint Reasoning     |    4% |  4,000,000 |
| Counterexample Reasoning |    4% |  4,000,000 |
| Adversarial Reasoning    |    4% |  4,000,000 |
| Calibration              |    3% |  3,000,000 |
| Ambiguity Handling       |    3% |  3,000,000 |
| Error Analysis           |    3% |  3,000,000 |
| Generalization           |    3% |  3,000,000 |
| Prompt Review            |    2% |  2,000,000 |
| Consistency Checking     |    2% |  2,000,000 |
| Evidence Checking        |    1% |  1,000,000 |

## Main Capabilities

The dataset is designed to improve:

* Mathematical reasoning
* Logical reasoning
* Code generation
* Code understanding
* Data analysis
* Research methodology
* Error detection
* Error correction
* Self-checking
* Instruction following
* Constraint satisfaction
* Counterexample detection
* Ambiguity resolution
* Prompt consistency
* Confidence estimation
* Uncertainty handling
* Generalization
* Verification

## Difficulty

Examples are distributed across multiple difficulty levels:

* `easy`
* `medium`
* `hard`
* `very_hard`
* `extreme`

## Verification Signals

Examples can contain structured fields for:

* `math_check`
* `logic_check`
* `code_check`
* `data_analysis_check`
* `constraint_check`
* `consistency_check`
* `counterexample_check`
* `evidence_check`
* `source_check`
* `error_detection`
* `error_repair`
* `prompt_review`
* `prompt_alignment`
* `confidence`
* `uncertainty`
* `answerability`

## Usage

Install the required library:

```bash
pip install -U datasets
```

Load the dataset:

```python
from datasets import load_dataset

dataset = load_dataset(
    "Lelonthecodeur/multi-task-dataset",
    streaming=True
)

train = dataset["train"]

for example in train:
    print(example)
    break
```

Load a specific split:

```python
from datasets import load_dataset

train = load_dataset(
    "Lelonthecodeur/multi-task-dataset",
    split="train",
    streaming=True
)
```

Streaming is recommended for the full dataset because of its size.

## Hugging Face CLI

Login:

```bash
hf auth login
```

Clone:

```bash
git lfs install
git clone https://huggingface.co/datasets/Lelonthecodeur/multi-task-dataset
```

Push an update:

```bash
cd multi-task-dataset
git add .
git commit -m "Update dataset"
git push
```

## Python Upload

```python
from huggingface_hub import HfApi

api = HfApi(token="YOUR_HF_TOKEN")

api.upload_folder(
    folder_path="/kaggle/working/multi-task-dataset",
    repo_id="Lelonthecodeur/multi-task-dataset",
    repo_type="dataset",
    commit_message="Update dataset",
)
```

## Data Format

The dataset is stored in **Parquet** format.

Main fields include:

```text
id
task_family
task_type
domain
difficulty
language
instruction
context
response
analysis_plan
verification
prompt_review
prompt_alignment
constraint_check
consistency_check
math_check
logic_check
counterexample_check
data_analysis_check
code_check
evidence_check
source_check
hallucination_control
error_detection
error_repair
answerability
confidence
uncertainty
reasoning_depth
minimal_sufficient_reasoning
unnecessary_reasoning
stop_condition
surface_variation
numeric_variation
structure_variation
ood_style
quality_score
generator_version
```

## Future Updates

### V2 — Robustness

Planned improvements:

* Harder reasoning tasks
* Adversarial examples
* Hard negatives
* Better deduplication
* Near-duplicate detection
* Leakage detection
* Stronger OOD splits
* Better generalization testing

### V3 — Science & Research

Planned additions:

* Scientific reasoning
* Scientific knowledge
* Research methodology
* Experimental design
* Hypothesis evaluation
* Scientific data analysis
* Evidence comparison
* Source comparison
* Uncertainty analysis

### V4 — Mega Deep

Planned addition of approximately **10M highly difficult examples**.

The objective is to target specific weaknesses found during model evaluation instead of simply increasing prompt complexity.

```text
Model
  ↓
Benchmark
  ↓
Failure Detection
  ↓
Weak Skill Detection
  ↓
Targeted Hard Examples
  ↓
Verification
  ↓
Deduplication
  ↓
OOD / Adversarial Tests
  ↓
Training
  ↓
New Benchmark
```

### V5 — Science × Knowledge × Logic × Experience

Future expansion combining:

* Science
* Knowledge
* Complex logic
* Experience-based problem solving
* Cross-domain reasoning
* Multi-step verification
* Novel situations
* Adaptive evaluation

## Font

For standard text:

```python
import matplotlib.pyplot as plt

plt.rcParams["font.family"] = "DejaVu Sans"
```

For multilingual text:

```python
import matplotlib.pyplot as plt

plt.rcParams["font.family"] = ["Noto Sans", "Noto Sans CJK JP"]
```

## License

MIT License

Copyright (c) 2026 Lelonthecodeur

Permission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated files, to use, copy, modify, merge, publish, distribute, sublicense, and sell copies of the dataset, subject to the conditions of the MIT License.

## Version

**Current version:** `v1.1`

**Total examples:** `100,000,001`

**Format:** Parquet

**Status:** Active development

## Citation

```bibtex
@dataset{multi_task_dataset,
  title        = {multi-task-dataset},
  author       = {Lelonthecodeur},
  year         = {2026},
  publisher    = {Hugging Face},
  version      = {1.1},
  note         = {100,000,001 examples}
}
```