---
language:
- ru
- en
license: cc0-1.0
task_categories:
- text-generation
- conversational
tags:
- synthetic
- instruction-tuning
- bilingual
- russian
- english
- coding
- greetings
pretty_name: RU-EN Humanlike Synthetic Instructions
---

# RU-EN Humanlike Synthetic Instructions

Synthetic Russian + English prompt → answer dataset for SFT/chat fine-tuning experiments.

Current local build: **20,000 examples**.

## What's new in v2

Added another 10k examples and expanded the generator with:

- greetings and warm opening conversations;
- more small talk and human-like supportive replies;
- more coding categories;
- debugging prompts;
- code review prompts;
- tests and refactoring prompts;
- API design;
- data analysis;
- translation;
- summarization;
- comparison;
- polite emails;
- roleplay mentor dialogs;
- habit-building advice.

## Files

- `data/train.jsonl`
- `data/validation.jsonl`
- `data/test.jsonl`
- `data/dataset_info.json`
- `generate_dataset.py` — reproducible generator; can create thousands or millions of rows.

## Schema

Each row contains:

```json
{
  "id": "stable id",
  "lang": "ru|en",
  "category": "greeting|explain|planning|empathy|coding|coding_debug|...",
  "prompt": "user prompt",
  "response": "assistant response",
  "messages": [
    {"role": "user", "content": "..."},
    {"role": "assistant", "content": "..."}
  ],
  "source": "synthetic_template_v2",
  "license": "CC0-1.0",
  "quality_note": "Template-generated; review/filter before serious training."
}
```

## Generate more rows

```bash
python generate_dataset.py --n 20000 --out data --seed 43
python generate_dataset.py --n 1000000 --out data_million --seed 43
```

## Load with Hugging Face datasets

```python
from datasets import load_dataset

dataset = load_dataset(
    "json",
    data_files={
        "train": "data/train.jsonl",
        "validation": "data/validation.jsonl",
        "test": "data/test.jsonl",
    }
)
```

## Intended use

Good for prototyping a Hugging Face dataset and testing SFT pipelines. For a production-quality model, mix this with reviewed high-quality examples, deduplicate, add preference data, and run safety/quality filters.

## Design goals

- Russian + English coverage.
- Warm, natural, emotionally aware replies.
- Chat-friendly `messages` column.
- Expanded coding/task categories.
- Safer refusals for harmful requests.

## Limitations

This is synthetic template-generated data. It can be repetitive at very large scale. Use it as a scaffold/base generator, not as the only source for a serious model.