Datasets:

African-Languages-Lab
/

proxy-mt-translations

pretty_name: Proxy-MT Translations
license: other
task_categories:
  - translation
language:
  - af
  - am
  - ar
  - bm
  - bn
  - bg
  - ca
  - ceb
  - cs
  - cy
  - da
  - de
  - el
  - et
  - eu
  - ee
  - fi
  - fr
  - gu
  - ha
  - he
  - hi
  - hr
  - hu
  - hy
  - ig
  - id
  - is
  - it
  - ja
  - jv
  - kn
  - ka
  - kk
  - km
  - rw
  - ky
  - ko
  - lo
  - ln
  - lg
  - luo
  - lv
  - ml
  - mr
  - mk
  - mt
  - mi
  - my
  - nl
  - 'no'
  - ny
  - or
  - pa
  - fa
  - pl
  - pt
  - ro
  - ru
  - sn
  - si
  - sk
  - sl
  - sd
  - so
  - st
  - es
  - sr
  - ss
  - su
  - sv
  - sw
  - ta
  - te
  - tg
  - tl
  - th
  - ti
  - tn
  - ts
  - tr
  - tw
  - uk
  - ur
  - uz
  - vi
  - war
  - wo
  - xh
  - yo
  - zh
  - zu
tags:
  - machine-translation
  - african-languages
  - flores-200
  - ntrex
  - wmt24
  - low-resource
size_categories:
  - 10M<n<100M
extra_gated_prompt: >-
  You agree to use these model-generated translations for research purposes and
  to cite the source. Access is granted on request and reviewed manually.
extra_gated_fields:
  Name: text
  Affiliation: text
  Intended use: text
  I agree to use this dataset for non-commercial research only: checkbox

Proxy-MT Translations

English→X machine translations generated with vLLM across 49 open-weight LLMs on three evaluation benchmarks. This dataset holds the raw model outputs (one CSV per model × dataset × target language); metric scores (BLEU / chrF / COMET / MetricX) are computed separately.

Layout

flores-200/<model>/eng-<lang>.csv   # 119 target languages
ntrex/<model>/eng-<lang>.csv        #  87 target languages
wmt24/<model>/eng-<lang>.csv        #  51 target languages

Each CSV has three columns:

column	description
`source`	English source sentence
`target`	reference translation from the benchmark
`translation`	model-generated translation

Source benchmarks

FLORES-200 (facebook/flores, devtest split)
NTREX (mteb/NTREX, test split)
WMT24++ (google/wmt24pp, train split)

Models (49)

aya-expanse-8b, aya-expanse-32b, tiny-aya-base, c4ai-command-r-08-2024, DeepSeek-R1-0528-Qwen3-8B, DeepSeek-R1-Distill-Llama-8B, DeepSeek-R1-Distill-Qwen-1.5B, DeepSeek-R1-Distill-Qwen-7B, DeepSeek-R1-Distill-Qwen-14B, DeepSeek-R1-Distill-Qwen-32B, Falcon3-3B-Instruct, Falcon3-7B-Instruct, Falcon3-10B-Instruct, gemma-3-1b-it, gemma-3-4b-it, gemma-3-12b-it, gemma-3-27b-it, gemma-4-E2B-it, gemma-4-E4B-it, gemma-4-26B-A4B-it, gemma-4-31B-it, glm-4-9b-chat, granite-4.1-3b, granite-4.1-8b, granite-4.1-30b, internlm2_5-7b-chat, internlm2_5-20b-chat, Llama-3.1-8B-Instruct, Llama-3.2-1B-Instruct, Llama-3.2-3B-Instruct, Ministral-3-3B-Instruct-2512, Ministral-3-8B-Instruct-2512, Ministral-3-14B-Instruct-2512, Mistral-7B-Instruct-v0.3, Mistral-Nemo-Instruct-2407, Mixtral-8x7B-Instruct-v0.1, Olmo-3-7B-Instruct, Olmo-3-1125-32B, Olmo-3.1-32B-Instruct, phi-4, Qwen3-1.7B, Qwen3-4B, Qwen3-8B, Qwen3-14B, Qwen3-30B-A3B, Qwen3.6-27B, Qwen3.6-35B-A3B, Yi-1.5-9B-Chat, Yi-1.5-34B-Chat.

Access

This is a gated dataset — access is reviewed and approved manually. Fill in the request form to be granted access.

Note: set the license field above to the license you intend to release under.