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README.md

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+---
+language:
+  - ru
+  - alt
+  - av
+  - ba
+  - be
+  - cv
+  - dar
+  - dig
+  - kjh
+  - kk
+  - kv
+  - mhr
+  - os
+  - sah
+  - tt
+  - udm
+pretty_name: "SuperMinor — assistant instruction prompts in 15 minority languages, RU pivot"
+task_categories:
+  - translation
+  - text2text-generation
+multilinguality:
+  - translation
+size_categories:
+  - n<1K
+license: cc-by-4.0
+tags:
+  - parallel-corpus
+  - instruction-tuning
+  - low-resource
+  - minority-languages
+  - languages-of-russia
+configs:
+  - config_name: prompts
+    data_files: data/prompts.parquet
+    default: true
+---
+
+# SuperMinor — assistant-style instruction prompts in 15 minority languages (RU pivot)
+
+A fully parallel set of **124 assistant-style instruction prompts** (summarize, extract
+key points, quiz questions, style transfer, translation requests, etc.) translated from
+Russian into **15 low-resource languages**, most of them minority languages of Russia.
+Every prompt has a translation in every language — a perfect N-way parallel table.
+
+The prompts imitate how a user talks to a virtual assistant. Together with per-language
+answers they can be used to teach instruction-following models to converse in languages
+that today's assistants barely speak.
+
+## About the SuperMinor project
+
+SuperMinor is a project for the development of minor languages, aimed at collecting and
+translating data for training language models in instruction format. Virtual
+assistants mostly work well only with popular languages — and not
+even all of those. For small languages like Yakut or Chuvash the situation is far
+worse.
+
+For the project, a small portal was built where native speakers of minor languages
+could, without any extra hassle, translate and edit specially selected short texts in
+the form of instructions. The instructions are sets of phrases imitating a conversation
+with an assistant. By training on such texts, a model learns to communicate in a
+language new to it, which makes it possible to build all kinds of generative content
+and neural-network applications in Bashkir, Tatar, the Mari languages, the Ossetian
+dialects, and the other languages for which the data was collected.
+
+This dataset is the result of the project: native-speaker translations, validated with
+the **Claude Fable 5** model, which also produced the machine translations (`mt` rows)
+for the prompts no volunteer had covered.
+
+## Languages
+
+| code | language | human | mt |
+|---|---|---|---|
+| ru | Russian (pivot) | — | — |
+| alt | Southern Altai | 124 | 0 |
+| av | Avar | 2 | 122 |
+| ba | Bashkir | 124 | 0 |
+| be | Belarusian | 5 | 119 |
+| cv | Chuvash | 124 | 0 |
+| dar | Dargwa | 124 | 0 |
+| dig | Digor Ossetian | 0 | 124 |
+| kjh | Khakas | 25 | 99 |
+| kk | Kazakh | 124 | 0 |
+| kv | Komi-Zyrian | 124 | 0 |
+| mhr | Meadow Mari | 124 | 0 |
+| os | Ossetian (Iron) | 0 | 124 |
+| sah | Yakut | 124 | 0 |
+| tt | Tatar | 124 | 0 |
+| udm | Udmurt | 124 | 0 |
+
+Codes are ISO 639-1 where available, ISO 639-2/3 otherwise. `os` is Iron Ossetian, the
+literary standard; Digor Ossetian has no dedicated ISO code, so `dig` follows the
+convention of the companion [Little Prince multiparallel corpus of low resource languages of Russia](https://huggingface.co/datasets/averoo/low_resource_parallel_corpora).
+
+## Data
+
+`data/prompts.parquet` contains one flat row per prompt. Each language is a text column
+and carries two metadata columns prefixed with its code:
+
+```json
+{
+  "ru": "Выпиши десять ключевых слов, описывающих текст:",
+  "ba": "Тексты тасуирлаған ун төп һүҙҙе яҙып ал:",
+  "ba_source": "human",
+  "ba_edited": false
+}
+```
+
+- `<code>` — the prompt in that language;
+- `<code>_source` — `human` (translated by a native-speaker volunteer on the SuperMinor
+  portal) or `mt` (no volunteer translation existed; rendered by the validation model);
+- `<code>_edited` — `true` if the text was touched during the validation pass (typo /
+  consistency / mistranslation fixes for human rows; always `true` for `mt` rows).
+
+When pristine native-speaker text is required, filter on `<code>_source == "human"`.
+
+## Validation
+
+Every human translation was reviewed and every gap filled in a per-language verification
+pass (Claude Fable 5), using the project's Little Prince multiparallel corpus and its
+per-language dictionaries as orthographic and lexical reference. The pass enforced:
+faithfulness to the Russian prompt, one orthographic convention per language
+(single palochka form, Cyrillic-only letters where standard, one че/nasal letter for
+Khakas, degemination per native majority for Bashkir), an exact `XXX` placeholder,
+digit and trailing-colon parity with the pivot, and terminology consistency across each
+language's prompt set.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+prompts = load_dataset("averoo/sumi", "prompts")["train"]
+row = prompts[0]
+print(row["ru"], "->", row["sah"])
+```
+
+## Licensing and intended use
+
+Volunteer translations were contributed to the SuperMinor project for developing
+language technology for minority languages. Published for language preservation,
+education, and research; attribution appreciated.

data/prompts.parquet

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+oid sha256:e4fa78343769cc9f80e1be5f6d43c75c57d479ab82dbd5d873f4e207cbe6b916
+size 101731