---
language:
- en

license: mit

tags:
- uncensored
- uncensored-llm
- jailbreak
- anti-censorship
- alignment
- dealigned
- instruction-following
- instruction-tuning
- supervised-fine-tuning
- sft
- conversational
- chatbot
- roleplay
- assistant
- synthetic-data
- open-domain
- english-only
- deduplicated
- cleaned-dataset
- llm-training
- finetuning
- question-answering
- text-generation

task_categories:
- text-generation
- question-answering

size_categories:
- 100K<n<1M

pretty_name: High Quality Uncensored Instruction Dataset
---
# Dataset Creation Process

This dataset was not scraped from a single source.

Instead, it was built through a large multi-stage curation and cleaning pipeline involving many open instruction datasets available on Hugging Face.

The entire dataset was normalized into a unified:

```json
{
  "input": "...",
  "output": "..."
}
```

format.

---

# Data Collection

A large number of public instruction datasets were downloaded from Hugging Face.

These datasets included:
- Instruction-following datasets
- Chat datasets
- QA datasets
- Roleplay datasets
- Coding datasets
- General assistant conversations
- Uncensored datasets
- Open conversational corpora

Many datasets originally existed in different formats such as:
- Alpaca format
- ShareGPT format
- ChatML format
- Message-based structures
- Instruction/input/output triples
- Raw JSONL
- Arrow datasets

All datasets were converted into a single standardized schema.

---

# Dataset Processing Pipeline

## 1. Format Normalization

All datasets were normalized into a common structure:

```json
{
  "input": "...",
  "output": "..."
}
```

This involved:
- Removing system prompts
- Removing metadata
- Flattening multi-message chat structures
- Extracting user-assistant pairs
- Converting multiple schemas into one consistent format

---

## 2. OCR and Junk Cleaning

Basic OCR and text corruption cleaning was performed.

This included removal of:
- Broken Unicode characters
- OCR artifacts
- Repeated symbol junk
- Corrupted whitespace
- Encoding issues
- Extremely malformed rows
- Random garbage strings

The cleaning pipeline was intentionally conservative to avoid damaging useful training data.

---

## 3. English-Only Filtering

The dataset was aggressively filtered to retain only English samples.

This involved:
- Unicode script detection
- Language detection
- Removal of multilingual rows
- Removal of non-English scripts

Languages/scripts filtered included:
- Chinese
- Japanese
- Korean
- Hindi
- Arabic
- Russian/Cyrillic
- Bengali
- Tamil
- Telugu
- Gujarati
- Punjabi
- Hebrew
- Greek
- Thai
- Burmese
- Tibetan
- and many others

Only high-confidence English samples were retained.

---

## 4. Exact Deduplication

Exact duplicate rows were removed.

This step eliminated:
- Repeated instruction-response pairs
- Re-uploaded datasets
- Duplicate synthetic generations
- Mirrored samples

This significantly reduced redundancy and improved information density.

---

## 5. Question Deduplication

Many open instruction datasets contain repeated or near-identical questions with different responses.

The dataset was deduplicated at the question level using normalized input matching.

Examples:

```text
What is AI?
what is ai
What is AI!!!
```

These were treated as identical questions.

---

## 6. Best Response Selection

When multiple responses existed for the same question, the pipeline retained the highest-quality candidate.

Selection criteria included:
- Response length
- Information density
- Completeness
- Reduced redundancy
- Better formatting

The longest and most informative response was generally retained.

---

## 7. Low-Quality Filtering

Additional filtering was performed to remove:
- Extremely short responses
- Empty outputs
- Corrupted samples
- Low-information generations
- Formatting failures
- Broken rows

This improved overall dataset quality and reduced noisy supervision.

---

# Final Dataset Statistics

| Statistic | Value |
|---|---|
| Final Rows | ~721,000 |
| Language | English |
| Format | JSONL |
| Schema | input/output |
| Type | Instruction-following |
| Primary Use | SFT / Instruction Tuning |

---

# Example Format

```json
{
  "input": "Explain what a black hole is.",
  "output": "A black hole is a region of spacetime where gravity is so strong that nothing, including light, can escape from it..."
}
```

---

# Intended Use

This dataset is intended for:
- Supervised fine-tuning
- Instruction tuning
- Chat model training
- Alignment experimentation
- Uncensored assistant research
- Synthetic data experiments
- Small model capability improvement

Potential applications:
- LLaMA fine-tuning
- Mistral fine-tuning
- Qwen fine-tuning
- Gemma fine-tuning
- Distillation
- Reasoning experiments
- Alignment studies

---

# Recommended Training Usage

Recommended for:
- QLoRA
- Full fine-tuning
- Continued pretraining
- Distillation
- SFT
- Alignment tuning

Works particularly well for:
- Smaller models
- Instruction-following recovery
- Reducing over-refusal behavior
- Increasing response completeness

---

# File Structure

```text
train.jsonl
```

Each line is a standalone JSON object.

---

# Acknowledgements

Thanks to the open-source AI community and all original dataset contributors on Hugging Face whose datasets made this aggregation possible.

---