README.md

metadata

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:

{
  "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

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

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.