Create README.md

README.md

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+---
+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}
+}
+```