| --- |
| 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<n<10M |
| configs: |
| - config_name: all |
| default: true |
| data_files: |
| - split: train |
| path: data/*/train-*.parquet |
| - config_name: 'bg' |
| data_files: |
| - split: train |
| path: data/bg/train-*.parquet |
| - config_name: 'bs' |
| data_files: |
| - split: train |
| path: data/bs/train-*.parquet |
| - config_name: 'ca' |
| data_files: |
| - split: train |
| path: data/ca/train-*.parquet |
| - config_name: 'cs' |
| data_files: |
| - split: train |
| path: data/cs/train-*.parquet |
| - config_name: 'da' |
| data_files: |
| - split: train |
| path: data/da/train-*.parquet |
| - config_name: 'de' |
| data_files: |
| - split: train |
| path: data/de/train-*.parquet |
| - config_name: 'el' |
| data_files: |
| - split: train |
| path: data/el/train-*.parquet |
| - config_name: 'es' |
| data_files: |
| - split: train |
| path: data/es/train-*.parquet |
| - config_name: 'et' |
| data_files: |
| - split: train |
| path: data/et/train-*.parquet |
| - config_name: 'eu' |
| data_files: |
| - split: train |
| path: data/eu/train-*.parquet |
| - config_name: 'fi' |
| data_files: |
| - split: train |
| path: data/fi/train-*.parquet |
| - config_name: 'fr' |
| data_files: |
| - split: train |
| path: data/fr/train-*.parquet |
| - config_name: 'ga' |
| data_files: |
| - split: train |
| path: data/ga/train-*.parquet |
| - config_name: 'gl' |
| data_files: |
| - split: train |
| path: data/gl/train-*.parquet |
| - config_name: 'hr' |
| data_files: |
| - split: train |
| path: data/hr/train-*.parquet |
| - config_name: 'hu' |
| data_files: |
| - split: train |
| path: data/hu/train-*.parquet |
| - config_name: 'is' |
| data_files: |
| - split: train |
| path: data/is/train-*.parquet |
| - config_name: 'it' |
| data_files: |
| - split: train |
| path: data/it/train-*.parquet |
| - config_name: 'ka' |
| data_files: |
| - split: train |
| path: data/ka/train-*.parquet |
| - config_name: 'lt' |
| data_files: |
| - split: train |
| path: data/lt/train-*.parquet |
| - config_name: 'lv' |
| data_files: |
| - split: train |
| path: data/lv/train-*.parquet |
| - config_name: 'mk' |
| data_files: |
| - split: train |
| path: data/mk/train-*.parquet |
| - config_name: 'mt' |
| data_files: |
| - split: train |
| path: data/mt/train-*.parquet |
| - config_name: 'nl' |
| data_files: |
| - split: train |
| path: data/nl/train-*.parquet |
| - config_name: 'no' |
| data_files: |
| - split: train |
| path: data/no/train-*.parquet |
| - config_name: 'pl' |
| data_files: |
| - split: train |
| path: data/pl/train-*.parquet |
| - config_name: 'pt' |
| data_files: |
| - split: train |
| path: data/pt/train-*.parquet |
| - config_name: 'ro' |
| data_files: |
| - split: train |
| path: data/ro/train-*.parquet |
| - config_name: 'sk' |
| data_files: |
| - split: train |
| path: data/sk/train-*.parquet |
| - config_name: 'sl' |
| data_files: |
| - split: train |
| path: data/sl/train-*.parquet |
| - config_name: 'sq' |
| data_files: |
| - split: train |
| path: data/sq/train-*.parquet |
| - config_name: 'sr' |
| data_files: |
| - split: train |
| path: data/sr/train-*.parquet |
| - config_name: 'sv' |
| data_files: |
| - split: train |
| path: data/sv/train-*.parquet |
| - config_name: 'tr' |
| data_files: |
| - split: train |
| path: data/tr/train-*.parquet |
| - config_name: 'uk' |
| data_files: |
| - split: train |
| path: data/uk/train-*.parquet |
| --- |
| |
| # EuroMultiSynth-Instruct: synthetic instruction-tuning data in 35 European languages |
|
|
| 4,160,598 single-turn instruction/response pairs in 35 European (and |
| neighbouring) languages, written natively in each language (not translated) by |
| an LLM, scored by an LLM judge, quality-filtered and de-duplicated within each |
| language. Built with [`synthgen`](https://github.com/OpenEuroLLM/synthgen) for the OpenEuroLLM project. |
|
|
| ## Quick start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("openeurollm/EuroMultiSynth-Instruct", "de", split="train") # one language |
| ds = load_dataset("openeurollm/EuroMultiSynth-Instruct", split="train") # all 35 languages |
| print(ds[0]["messages"]) |
| ``` |
|
|
| ## Fields |
|
|
| | field | type | description | |
| |---|---|---| |
| | `id` | string | unique row id, `<lang>-<number>` | |
| | `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 |
|
|
