--- license: apache-2.0 language: - 'bg' - 'bs' - 'ca' - 'cs' - 'da' - 'de' - 'el' - 'es' - 'et' - 'eu' - 'fi' - 'fr' - 'ga' - 'gl' - 'hr' - 'hu' - 'is' - 'it' - 'ka' - 'lt' - 'lv' - 'mk' - 'mt' - 'nl' - 'no' - 'pl' - 'pt' - 'ro' - 'sk' - 'sl' - 'sq' - 'sr' - 'sv' - 'tr' - 'uk' task_categories: - text-generation tags: - synthetic - instruction-tuning - sft - multilingual - llm-as-a-judge size_categories: - 1M-` | | `language` | string | ISO 639-1 code | | `messages` | list of `{role, content}` | `[user prompt, assistant response]` | | `domain` | string | task category the prompt was generated for (18 values) | | `persona` | string or null | user persona the prompt was written as (null for 87% of rows) | | `judge_score` | int8 | LLM-judge quality score, 0-10 (only 7-10 kept) | ## How it was made 1. **Generation.** `google/gemma-4-31b-it` (served with vLLM) wrote each instruction *and* its response in the target language in one call, conditioned on a task domain and, for some prompts, a user persona. 7,040,000 unique samples were generated (160k or 320k per language). 2. **Judging.** `Qwen/Qwen3.6-27B` scored the samples 0-10 (temperature 0, thinking disabled). 2,204 samples (0.03%) were never judged and are not included. 3. **Quality filter.** Kept samples with score ≥ 7: 5,688,783 rows (80.9% of scored samples). 4. **Exact de-duplication** on (language, whitespace/case-normalised prompt): −93,387 rows. 5. **Near-duplicate removal, within each language only.** Prompts were embedded and a prompt was dropped if its cosine similarity to an earlier kept prompt in the *same* language was at or above the threshold (greedy, first occurrence kept). 31 languages used `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2` at **0.97**; Maltese, Irish, Basque and Icelandic, which that model does not cover, used `sentence-transformers/LaBSE` at **0.96** (calibrated on German and Croatian to remove the same share as MiniLM at 0.97). Thresholds were chosen from sweeps with example pairs per similarity band: at these values, removed pairs are rewordings of the same request. −1,434,798 rows. Final: **4,160,598 rows**. ## Languages | code | language | rows | near-dup model / threshold | |---|---|---|---| | `tr` | Turkish | 236,352 | MiniLM ≥ 0.97 | | `de` | German | 230,459 | MiniLM ≥ 0.97 | | `bg` | Bulgarian | 212,388 | MiniLM ≥ 0.97 | | `bs` | Bosnian | 207,040 | MiniLM ≥ 0.97 | | `gl` | Galician | 193,629 | MiniLM ≥ 0.97 | | `hr` | Croatian | 190,139 | MiniLM ≥ 0.97 | | `da` | Danish | 185,955 | MiniLM ≥ 0.97 | | `ca` | Catalan | 166,246 | MiniLM ≥ 0.97 | | `fi` | Finnish | 132,641 | MiniLM ≥ 0.97 | | `pl` | Polish | 126,767 | MiniLM ≥ 0.97 | | `it` | Italian | 124,420 | MiniLM ≥ 0.97 | | `ro` | Romanian | 119,021 | MiniLM ≥ 0.97 | | `no` | Norwegian | 115,400 | MiniLM ≥ 0.97 | | `mk` | Macedonian | 110,730 | MiniLM ≥ 0.97 | | `sr` | Serbian | 110,270 | MiniLM ≥ 0.97 | | `uk` | Ukrainian | 109,240 | MiniLM ≥ 0.97 | | `sq` | Albanian | 107,505 | MiniLM ≥ 0.97 | | `sk` | Slovak | 106,467 | MiniLM ≥ 0.97 | | `sv` | Swedish | 104,862 | MiniLM ≥ 0.97 | | `pt` | Portuguese | 104,727 | MiniLM ≥ 0.97 | | `fr` | French | 103,013 | MiniLM ≥ 0.97 | | `is` | Icelandic | 102,362 | LaBSE ≥ 0.96 | | `nl` | Dutch | 102,213 | MiniLM ≥ 0.97 | | `cs` | Czech | 100,099 | MiniLM ≥ 0.97 | | `es` | Spanish | 99,709 | MiniLM ≥ 0.97 | | `el` | Greek | 99,005 | MiniLM ≥ 0.97 | | `ka` | Georgian | 90,665 | MiniLM ≥ 0.97 | | `sl` | Slovenian | 85,119 | MiniLM ≥ 0.97 | | `lt` | Lithuanian | 71,445 | MiniLM ≥ 0.97 | | `hu` | Hungarian | 68,390 | MiniLM ≥ 0.97 | | `lv` | Latvian | 62,438 | MiniLM ≥ 0.97 | | `mt` | Maltese | 52,674 | LaBSE ≥ 0.96 | | `et` | Estonian | 52,551 | MiniLM ≥ 0.97 | | `eu` | Basque | 50,716 | LaBSE ≥ 0.96 | | `ga` | Irish | 25,941 | LaBSE ≥ 0.96 | ## Domains | domain | rows | share | |---|---|---| | general question answering | 855,672 | 20.6% | | coding | 585,753 | 14.1% | | brainstorming & idea generation | 555,206 | 13.3% | | reports, summaries & documentation | 287,952 | 6.9% | | roleplay & character play | 223,106 | 5.4% | | translation | 195,296 | 4.7% | | math (word problems & explanations) | 179,613 | 4.3% | | short stories & fiction | 165,221 | 4.0% | | social media & blog posts | 136,750 | 3.3% | | data analysis (tables, lists, datasets) | 133,886 | 3.2% | | summarization | 124,134 | 3.0% | | meetings, planning & project coordination | 108,337 | 2.6% | | workplace email & communication | 104,977 | 2.5% | | humor, jokes & satire | 104,715 | 2.5% | | poetry & song lyrics | 104,456 | 2.5% | | customer & client communication | 99,965 | 2.4% | | job applications & CVs | 99,951 | 2.4% | | personal essays & reflections | 95,608 | 2.3% | ## Judge scores | score | rows | share | |---|---|---| | 7 | 283,975 | 6.8% | | 8 | 168,940 | 4.1% | | 9 | 476,087 | 11.4% | | 10 | 3,231,596 | 77.7% | Personas (8): office worker (69,697), teenager (69,427), retiree (69,304), small-business owner (68,846), recent immigrant (68,527), parent (68,376), university student (67,856), tourist (63,608). ## Limitations - **Fully synthetic.** Both the prompts and the responses are model-generated; facts, figures and local details (laws, prices, places) can be wrong or out of date. - **LLM-judged.** Quality filtering relies on a single LLM judge, whose reliability likely varies by language and is probably weaker for lower-resource languages. - **Uneven size and quality across languages.** Languages with lower judge pass rates are smaller (e.g. Irish: 320,000 generated, 25,941 kept). A few spot-checked Basque and Irish samples contained malformed words, so expect more noise in the lower-resource languages. - **Residual redundancy.** The near-duplicate thresholds are deliberately conservative, so many prompts still share a template or scenario with different specifics. - **Single-turn only.** ## License Released under the **Apache License 2.0**. This choice follows from the models used to build the dataset, all of which are Apache-2.0: | role | model | license | |---|---|---| | generation (wrote every prompt and response) | [`google/gemma-4-31B-it`](https://huggingface.co/google/gemma-4-31B-it) | [Apache-2.0](https://ai.google.dev/gemma/docs/gemma_4_license) | | quality judge (scores only, no text in the dataset) | [`Qwen/Qwen3.6-27B`](https://huggingface.co/Qwen/Qwen3.6-27B) | [Apache-2.0](https://huggingface.co/Qwen/Qwen3.6-27B/blob/main/LICENSE) | | near-duplicate filtering | [`sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2), [`sentence-transformers/LaBSE`](https://huggingface.co/sentence-transformers/LaBSE) | Apache-2.0 | All text in this dataset is model output, attributed to `google/gemma-4-31B-it`. Please credit both the dataset and the generating model when you use it, and follow Google's [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy) in spirit when building on Gemma-generated data. ## Citation TODO