annotations_creators: []
language:
- en
language_creators: []
license: []
multilinguality:
- monolingual
pretty_name: adaption-english-multilingual-translation-pairs
size_categories:
- 1K<n<10K
source_datasets: []
tags:
- adaption
- instruction-tuning
- language
task_categories: []
task_ids: []
This dataset is a remastered version prepared using Adaption's Adaptive Data platform.
adaption-english-multilingual-translation-pairs
This dataset contains prompt-completion pairs formatted for sentence-level machine translation instruction tuning and evaluation. Prompts follow a standardized template requesting translation from English into Spanish, French, Hindi, Swahili, or Tamil. Each sample provides a source English sentence across various domains alongside its corresponding target language translation.
Dataset size
There are 1,000 data points in this dataset. This is an instruction tuning dataset.
Quality of Remastered Dataset
The final quality is B, with a relative quality improvement of 4.3%.
Domain
- Language (100%)
Language
- English (100%)
Tone
- Professional (38%)
- Clear (32%)
- Informative (20%)
Evaluation Results
Quality Gains:
Grade Improvement:
Percentile Chart:
Structure
Every prompt follows one template:
Translate from <source language> to <target language>: <sentence>
99.8% of rows parse against that template. The completion is the translation alone, on a single line.
Language distribution
Parsed from the generated prompts themselves rather than taken from metadata:
| Direction | Rows |
|---|---|
| English → Hindi | 243 |
| English → Spanish | 229 |
| English → Tamil | 214 |
| English → French | 211 |
| English → Swahili | 98 |
| Other (German, Gujarati, Odia) | 5 |
995 of 1,000 rows are in one of the five requested directions. Note that Swahili is roughly half as well represented as the other four, so the set is usable but not balanced — sample accordingly if balance matters to you.
What was verified, and what was not
Verified. Prompt template conformance, the declared language direction of every row, and — for the non-Latin targets — that the completion is actually written in the expected script rather than in English or transliteration.
Not verified: whether the translations are correct. No fluent speaker reviewed this data, and no automatic translation-quality metric was run. A row can be in the right language, in the right script, and still be a poor or wrong translation. Nothing here evidences otherwise.
That distinction matters most for Swahili and Tamil, where a downstream user is least likely to catch a bad translation by eye, and which are also the two directions where a synthetic generator is most likely to struggle. Treat this dataset as structurally sound and semantically unaudited.
Suggested use
Reasonable: instruction-format training where the shape of the task matters, prompt-template experiments, multilingual formatting work, or as a seed set to be quality-filtered with a translation metric or human review.
Not recommended: any use where translation accuracy is load-bearing, without running your own quality pass first.
Limitations
- Translation quality unaudited (see above).
- English → X only. No reverse directions.
- Unbalanced across the five target languages.
- Source sentences were requested at 8–25 words across conversational, news, technical and idiomatic registers; register balance was not verified.
- 5 rows fall outside the requested language set.
- No per-row language column is included in the platform export; the direction must be parsed from the prompt string.
Provenance
- Generated with Adaption's Invent a Dataset feature, September 2026.
- Platform dataset ID:
2fd05389-fa7a-4893-9d14-0ef618dae773 - Rows are unmodified platform output.
- Requested specification: sentence-level translation pairs, prompts of the form
Translate from <source language> to <target language>: <sentence>, with the language pair drawn from English to Spanish, French, Hindi, Swahili or Tamil.
Citation
@misc{anbalagan2026translationpairs,
title = {adaption-english-multilingual-translation-pairs},
author = {Anbalagan, Vinod},
year = {2026},
note = {Generated with Adaption Adaptive Data; structure and language direction verified, translation quality unaudited},
url = {https://huggingface.co/datasets/vinod-anbalagan/adaption-english-multilingual-translation-pairs}
}