jed-lee/mirm_style_datasets

MIRM Style Datasets

Datasets prepared for a CS8803 MIRM team project at Georgia Tech. The project trains one style embedding model for many style tasks, so each dataset here is shaped for contrastive training.

Each dataset is its own config. Every config shares the same four core columns, so the training code can treat them the same way. Some configs add metadata columns after those. More configs will be added over the coming weeks.

Usage

from datasets import load_dataset
train_data = load_dataset("jed-lee/mirm_style_datasets", "3lf", split="train")
val_data = load_dataset("jed-lee/mirm_style_datasets", "3lf", split="validation")

# raid is about 5 million rows, so streaming avoids a full download
raid_train = load_dataset("jed-lee/mirm_style_datasets", "raid", split="train", streaming=True)

Configs

• 3lf: formality (informal vs. formal), with train and validation
• raid: which model family wrote a text (or human), with train and validation

Planned: argugpt, outfox, valla_ccat50, valla_blogs, wiki_auto and h4_stack_exchange.

Columns

These four are in every config and every split.

• text: one text, exactly as it appears in the source
• primary_id_int: the class to learn (its meaning depends on the config)
• secondary_category_int: shared content, such as a prompt or an aligned example. -1 means unknown and never counts as a shared group.
• source_record_id: traces the row back to the source

Integer ids are local to each config. In train they run from 0 with no gaps, which makes random access easy when building batches. Validation keeps the same labels. New groups in validation get ids after the training ones. The mappings for every id are in <config>/mappings.json.

3lf

The source pairs 1,500 example sentences with an informal and a formal rewrite of each. Each pair becomes two rows that share one secondary_category_int. That gives two texts with the same meaning in different registers, so a batch can learn formality without leaning on topic.

• primary_id_int: 0 = informal, 1 = formal
• secondary_category_int: the aligned pair
• source_record_id: 3lf-train-<position in train.json>-<informal|formal>

Split	Rows	Pairs	Informal	Formal
train	2,697	1,350	1,350	1,347
validation	300	150	150	150

No casual class. The 3LF paper describes three levels. The casual sentences come from GYAFC, which cannot be redistributed, so the public release leaves them out. They were not recreated here, so this config has two classes.

Preprocessing

The text is not changed. Spelling, capitalisation and punctuation stay as they were. Emojis and slang like "lol" stay too, because for style those details are the signal.

• Empty texts removed: 0
• Texts with both labels removed: 0
• Exact duplicates removed, first copy kept: 3
• Validation: 10% of pairs, seed 42, with each pair kept whole

Before saving, the script runs a set of checks. No text or pair may cross splits. Training ids must have no gaps, and every class needs at least two training texts. Every row must still match its source text and label. The script, mappings and counts are in the 3lf/ folder.

raid

RAID is a benchmark for detecting AI-generated text. It has human texts across eight domains, generations from 11 models written from the same prompts, and 11 adversarial attacks applied to both. Only the released training data is used. The official test split has no labels, and the extra split covers other domains.

• primary_id_int: the model family, see the table below
• secondary_category_int: the human source document (RAID source_id)
• source_record_id: RAID id

id	family	RAID models
0	human	human
1	cohere	cohere, cohere-chat
2	llama	llama-chat
3	mistral	mistral, mistral-chat
4	mpt	mpt, mpt-chat
5	openai	gpt2, gpt3, chatgpt, gpt4

Base and chat versions of a model share a family. For plain human vs AI, use primary_id_int == 0. The exact generator is still in the model column.

A group holds one human text, every generation made from its prompt, and every attacked copy of those. So texts in one group share their prompt and differ in which family wrote them. That makes them good hard negatives.

Five metadata columns are kept as they are in RAID. They are not labels. model is the generator (human for human text) and domain is the genre. decoding and repetition_penalty are empty for human text. attack is none for unattacked text.

Split	Rows	Source docs	Human	Cohere	Llama	Mistral	MPT	OpenAI
train	4,476,881	12,034	132,090	516,323	525,765	1,029,026	995,812	1,277,865
validation	498,606	1,337	14,645	57,516	58,556	114,664	110,938	142,287

Still uneven, but much less. Human is the smallest family at about 1 to 10 against OpenAI. As a human vs AI split it would be 1 to 34, which is why the label is the family.

Preprocessing

The text is not changed, including attacked text with homoglyphs, misspellings or zero-width characters. That is what the attacks look like.

• Empty or invisible texts removed: 473 (some rows were only zero-width spaces)
• Identical texts from two different families removed: 425
• Exact duplicates removed: 639,435. Most are attacks that changed nothing, such as the number attack on a text with no digits. The unattacked copy is kept.
• Kept on purpose: 3,431 texts under 50 visible characters. These include short model refusals like "Sorry, but I can't assist with that."
• Validation: 10% of source documents, seed 42. A whole group moves together, so every attacked copy stays with its original. Documents that share an identical text (301 of them) also go to the same split, so no text or its attacked copies can sit on both sides.

Before saving, the script checks that every attacked row points at an original with the same source_id. It also checks that no text, kept or removed, touches both splits and that training ids have no gaps. A separate check compared 4,000 random rows against the raw file for text, label and metadata.

Dataset Description

3lf. Hyojeong Yu, Hyukhun Koh, Minsung Kim and Kyomin Jung. Casual as an Anchor: Resolving Supervision Misalignment in Formality Transfer Dataset. arXiv:2605.29365, 2026.

Data obtained from stellahj/3lf, which is built from GYAFC examples. Thank you to the 3LF authors for making their rewrites public.

raid. Liam Dugan, Alyssa Hwang, Filip Trhlík, Andrew Zhu, Josh Magnus Ludan, Hainiu Xu, Daphne Ippolito and Chris Callison-Burch. RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors. ACL 2024, pages 12463 to 12492.

Data obtained from liamdugan/raid.

Licenses

• 3lf: the source's terms, see stellahj/3lf
• raid: MIT, see liamdugan/raid

Citation

Please cite the sources for the configs you use: the 3LF paper and GYAFC for 3lf, and RAID for raid.

@misc{yu2026casualanchorresolvingsupervision,
  title         = {Casual as an Anchor: Resolving Supervision Misalignment in Formality Transfer Dataset},
  author        = {Hyojeong Yu and Hyukhun Koh and Minsung Kim and Kyomin Jung},
  year          = {2026},
  eprint        = {2605.29365},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2605.29365}
}

@inproceedings{rao-tetreault-2018-dear,
  title     = {Dear Sir or Madam, May {I} Introduce the {GYAFC} Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer},
  author    = {Rao, Sudha and Tetreault, Joel},
  booktitle = {Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  year      = {2018},
  pages     = {129--140},
  url       = {https://aclanthology.org/N18-1012}
}

@inproceedings{dugan-etal-2024-raid,
  title     = {{RAID}: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors},
  author    = {Dugan, Liam and Hwang, Alyssa and Trhl{\'\i}k, Filip and Zhu, Andrew and Ludan, Josh Magnus and Xu, Hainiu and Ippolito, Daphne and Callison-Burch, Chris},
  booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year      = {2024},
  pages     = {12463--12492},
  url       = {https://aclanthology.org/2024.acl-long.674}
}