datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
vn-spell-correction-eval-real
vn-spell-correction-eval-real
Out-of-distribution evaluation corpus for Vietnamese spell-correction
models — 150 hand-curated (noisy, clean) pairs sampled from real
VN error sources, not generated by nom.text.noise.
This is the test set we use to verify a spell-correction model
generalises beyond its own synthetic training distribution. A model
that scores 95 % on nom-vn's synthetic eval grid and 60 % on this
set is overfit to the noise generator.
Splits
Config… See the full description on the dataset page: https://huggingface.co/datasets/nrl-ai/vn-spell-correction-eval-real.3gpp-5g-nr-qa
3GPP 5G NR Q&A Dataset
A question-answering dataset derived from 3GPP 38-series technical specifications covering 5G New Radio (NR). Contains ~30,000 Q&A pairs suitable for fine-tuning LLMs on telecommunications domain knowledge.
Dataset Description
Overview
This dataset contains 29,918 instruction-following Q&A pairs generated from 3GPP 38-series specifications. The questions cover technical aspects of 5G New Radio including physical layer procedures, RRC… See the full description on the dataset page: https://huggingface.co/datasets/raoulbia/3gpp-5g-nr-qa.vn-diacritic-eval
nrl-ai/vn-diacritic-eval — 4-register Vietnamese diacritic-restoration eval
A reproducible evaluation set covering four registers of Vietnamese
text. Used by the nom-vn project
to compare diacritic-restoration models against the public
Toshiiiii1/Vietnamese_diacritics_restoration_5th
SOTA on a register-balanced grid.
Multi-corpus measurement is the rule — single-corpus quality
numbers hide register-shift weakness. This dataset is the
multi-register grid we maintain.… See the full description on the dataset page: https://huggingface.co/datasets/nrl-ai/vn-diacritic-eval.vn-spell-correction-eval
nrl-ai/vn-spell-correction-eval
Vietnamese spell-correction evaluation grid: 4 source registers × 2
noise levels = 8 splits, 2,098 (noisy, clean) sentence pairs total.
Each pair is {"input": "<noisy>", "target": "<clean>"}. Both sides are
NFC-normalized. The clean target is the same sentence used as the
target in nrl-ai/vn-diacritic-eval —
spell correction is a strict superset of diacritic restoration, so we
reuse the same registers-balanced corpus.
Splits
Two noise… See the full description on the dataset page: https://huggingface.co/datasets/nrl-ai/vn-spell-correction-eval.vn-spell-correction-train
nrl-ai/vn-spell-correction-train
459,478 (noisy, clean) Vietnamese training pairs for fine-tuning a
seq2seq spell-correction model. Each row:
{"input": "<noisy>", "target": "<clean>"}
Both fields are NFC-normalized.
How it was built
Clean side: same 500K register-balanced mix as
nrl-ai/vn-diacritic-train —
350K Vietnamese Wikipedia (CC-BY-SA-4.0,
hirine/wikipedia-vietnamese-1M296K-dataset) + 150K NFC-fixed
Vietnamese news (CC-BY-4.0, tmnam20/Vietnamese-News-dedup).… See the full description on the dataset page: https://huggingface.co/datasets/nrl-ai/vn-spell-correction-train.proximal-bug-injection-sft
Proximal Bug-Injection SFT
Supervised fine-tuning data for teaching a model to inject realistic, hard-to-find bugs into otherwise-correct code. The goal is to build coding RL environments where the introduced bugs resemble real human mistakes rather than trivial off-by-one or sign-flip errors.
The data is mined from real GitHub bug fixes: for each fix we take the after (correct) file as input and train the model to produce the before (buggy) version — i.e. the inverse of a… See the full description on the dataset page: https://huggingface.co/datasets/nreHieW/proximal-bug-injection-sft.vn-diacritic-train
nrl-ai/vn-diacritic-train — Vietnamese diacritic-restoration training data
Two register-distinct training corpora used to fine-tune Vietnamese
diacritic-restoration models in the
nom-vn project. Each row is a
JSONL record:
{"input": "diacritic-stripped text", "target": "correctly diacriticized original"}
Inputs are produced by
nom.text.strip_diacritics
on the target. Both fields are NFC-normalized.
Configs
wiki_500k
500K (input, target) pairs from… See the full description on the dataset page: https://huggingface.co/datasets/nrl-ai/vn-diacritic-train.Machine-Generated-Reviews-0.1
Machine Generated Reviews
This dataset contains the machine generated peer reviews used in the study of machine generated text (MGT) output syntactic homogenization in "Emphasizing the Commendable": A Study of Homogenized Transitive Verb Constructions in Machine Generated Peer Reviews.
The corresponding academic research papers and official reviews are available on OpenReview.
The machine generated peer reviews are produced by three LLMs with a diverse background.
The prompts and… See the full description on the dataset page: https://huggingface.co/datasets/NRC-CNRC/Machine-Generated-Reviews-0.1.