evaluate 0 0 0

kurakurai/IFEval-FR

published by @kurakurai

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
922
Last 30 days
12
Likes
-
Size
93 KB
Created on HF
2026-06-16
Age
4 months ago

Description

IFEval-FR

French split of the M-IFEval instruction-following benchmark, in the same format as google/IFEval. This version was reviewed and cleaned with Claude Opus 4.8 (fixed prompt/verifier mismatches, off-by-one constraints, an untranslated prompt, and grammar/formatting).

from datasets import load_dataset

ds = load_dataset("kurakurai/IFEval-FR")

Fields

key: unique id

prompt: the instruction (French)

instruction_id_list: verifiable instructions (fr: prefixed)… See the full description on the dataset page: https://huggingface.co/datasets/kurakurai/IFEval-FR.

Tags

task_categories:text-generationlanguage:frlicense:apache-2.0size_categories:n<1Kformat:jsonmodality:textlibrary:datasetslibrary:pandaslibrary:polarslibrary:mlcroissantarxiv:2502.04688region:us

README current version from Hugging Face


license: apache-2.0
task_categories:

  • text-generation
    language:
  • fr
    pretty_name: IFEval-FR

IFEval-FR

French split of the M-IFEval instruction-following benchmark, in the same format as google/IFEval. This version was reviewed and cleaned with Claude Opus 4.8 (fixed prompt/verifier mismatches, off-by-one constraints, an untranslated prompt, and grammar/formatting).

from datasets import load_dataset
ds = load_dataset("kurakurai/IFEval-FR")

Fields

  • key: unique id
  • prompt: the instruction (French)
  • instruction_id_list: verifiable instructions (fr: prefixed)
  • kwargs: arguments for each instruction

235 prompts, single train split. Apache-2.0.

Citation

@article{Dussolle2025MIFEval,
  title={M-IFEval: Multilingual Instruction-Following Evaluation},
  author={Dussolle, Antoine and Cardena D\'iaz, Andrea and Sato, Shota and Devine, Peter},
  year={2025},
  eprint={2502.04688},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2502.04688}
}

README history 4 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2026-06-16Clean 17 prompts (grammar/spacing/register/\n) + shorten README, cite M-IFEva...d5b0c221.1 KB
    Loading...
  2. 2026-06-16Fix 6 scoring-affecting prompts: end_phrase mismatch (145), off-by-one moins ...aeca5293.6 KB
    Loading...
  3. 2026-06-16Add French IFEval (M-IFEval) in google/IFEval formata18a5fe3.6 KB
    Loading...
  4. 2026-06-16initial commit8ddf3de0 B
    Loading...

Used by abliterated models none yet

No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.

Community discussions 1 thread · 1 open · 1 comments

Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.

  1. 2026-06-17[bot] Conversion to Parquetopen1 💬#1
    Loading...

Related datasets same stage · evaluate

Read further

Metadata

License
apache-2.0

Repository

Download the dataset

0

Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python)
from datasets import load_dataset
ds = load_dataset("kurakurai/IFEval-FR")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="kurakurai/IFEval-FR", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/kurakurai/IFEval-FR
Catalog is the map. Apps are the tools.

Run models on your own machine, not in the cloud.

Every model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.

Open in Abliteration