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Clue250K

text stringlengths 228k 1.76M	size_bytes int64 228k 1.76M	sha256 stringlengths 64 64
"Reese is in the attic\nA body was found in the attic\nMina is in the conservatory\nPeyton is in the(...TRUNCATED)	1,576,622	6831ca92a4a6b853ab04e9683fd7ad93790a07036e51d217810332fe5eceb914
"Maria is in the ballroom\nA body was found in the pantry\nErin is in the observatory\nMina is in th(...TRUNCATED)	1,578,578	63751c986c945c0bcf9d2176b7c1303dd19104c164b9115b1b149a3afc577207
"Kendall is in the living room\nA body was found in the study\nRiley is in the study\nZane is in the(...TRUNCATED)	1,578,659	9eab449df56ce701611a2e9e8f262134bc614a4bb428366b37ae23d651bb6549
"Lena is in the lounge\nLogan is in the gallery\nTaylor is in the balcony\nEden is in the basement\n(...TRUNCATED)	1,577,651	8aa629fe56b09d8b8b292c51dd83c58788b7c2cd25a5e992cf7f1af607e43bac
"A body was found in the garden\nPaige is in the garden\nMaria is in the chapel\nLena is in the gall(...TRUNCATED)	1,577,465	af075e362c284c850d34bf6463c00bfb1ef06f6a8cbcb5751ffb7928fc74d488
"Kendall is in the chapel\nMicah is in the gallery\nA body was found in the dining room\nErin is in (...TRUNCATED)	1,577,499	6369549a3e0a370773282bd40b784c9e857098161242c87c98ca83dca31fca79
"Dana is in the study\nPeyton is in the lounge\nA body was found in the lounge\nLee is in the parlor(...TRUNCATED)	1,575,681	2854d0ed51d17b67ce6a2e1dab4e56d7e25819e80cac32a9be5c6234c0bef9bf
"Reese is in the living room\nA body was found in the ballroom\nSasha is in the pantry\nJules is in (...TRUNCATED)	1,578,635	cdbabf46c2f4f04386a840f4adea067b6bb577af559e4a8450539c6c50501424
"Morgan is in the parlor\nHarper is in the dining room\nAlex is in the garage\nA body was found in t(...TRUNCATED)	1,577,131	c7b585e056d8231d6270111dccd5a62d77ec81bba61bd6152afdab7e496da475
"A body was found in the chapel\nMara is in the kitchen\nRowan is in the observatory\nHarper is in t(...TRUNCATED)	1,575,520	ea45ba53c19a5f471a2e8fa4d5fe57754240fb2687b6f7104674c2ff92608e89

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Clue 250K Synthetic Corpus

This dataset contains synthetic short murder mysteries for training and testing the tiny Clue 250K language model. The examples use a fixed set of names, locations, weapons, and wound descriptions. Each mystery asks the model to infer the murderer, or answer Unknown when the clues do not identify exactly one person.

The companion code and trained demo model live in the GitHub repository for Clue 250K. The dataset is intended for educational language-model experiments, not as a benchmark for general reasoning.

Dataset Structure

Each .txt file contains many examples separated by a blank line. There is no JSON, CSV, or metadata sidecar required to use the corpus.

Example:

Josh is in the library
Basil is in the kitchen
A body was found in the library
Maria is in the living room
Therefore the murderer is: Josh FIN.

FIN. is literal text in the corpus. It is not a special token.

Size

The current upload contains:

Total examples: 3,164,051
Total text size: 524,288,508 bytes (500.0 MiB)
Total text shards: 318
Location examples: 1,196,152 in 120 files
Weapon examples: 1,091,322 in 110 files
Unsolvable examples: 876,577 in 88 files
Three-suspect examples: about 25%
Four-suspect examples: about 75%

Example Types

Location Mysteries

The body location identifies the murderer.

Josh is in the library
Basil is in the kitchen
Maria is in the living room
A body was found in the library
Therefore the murderer is: Josh FIN.

If no person is in the body location, or more than one person is in the body location, the correct answer is Unknown.

Weapon Mysteries

The wound identifies the matching weapon, and the weapon identifies the murderer.

Josh has a pipe
Basil has a knife
Maria has a rope
A body was found in the library with a knife wound
Therefore the murderer is: Basil FIN.

Some weapon examples include both room and weapon information:

Josh is in the library with a pipe

The compact Name has a weapon form is useful for very small models with short context windows.

If no weapon matches the wound, or more than one person has the matching weapon, the correct answer is Unknown.

Unsolvable Mysteries

Unsolvable examples are generated in both location and weapon styles. They include either no valid match or an ambiguous match.

Josh is in the library
Basil is in the library
A body was found in the library
Maria is in the kitchen
Therefore the murderer is: Unknown FIN.

Endings

Most examples use:

Therefore the murderer is: {answer} FIN.

The corpus also includes alternate ending styles so a model does not only learn one final-line prefix:

The murderer is {answer} FIN.
The murderer was {answer} FIN.
The Murderer is {answer} FIN.
So the murderer is {answer} FIN.
So the murderer was {answer} FIN.

Approximate ending mix:

Therefore the murderer is: - 70%
The murderer is - 10%
The murderer was - 8%
The Murderer is - 5%
So the murderer is - 4%
So the murderer was - 3%

Intended Use

This corpus was built to overtrain a very small decoder-only transformer:

Vocabulary size: 512
Context length: 96
Blocks: 3
Feature dimension: 72
Parameters in the trained reference model: 230,904

The reference training run used a byte-BPE tokenizer and produced:

Byte-BPE tokens per epoch: 240,244,946
Epochs: 4
Total token presentations: 960,979,784
Tokens per parameter per epoch: 1,040.45
Total token presentations per parameter: 4,161.82

Valid Names

Alex, Avery, Basil, Blair, Blake, Casey, Dana, Devin, Drew, Eden, Ellis,
Emery, Erin, Felix, Finn, Gail, Gray, Harper, Hazel, Ira, Jamie, Jordan,
Josh, Jules, Kai, Kendall, Lane, Laurel, Lee, Logan, Mara, Maria, Micah,
Morgan, Nico, Noel, Nora, Owen, Paige, Parker, Peyton, Quinn, Reese, Remy,
Riley, Robin, Rowan, Sage, Sam, Sasha, Sidney, Sky, Sloan, Talia, Taylor,
Theo, Vale, Wren, Zane, Mina, Ivy, Lena, Toby, June

Valid Locations

library, kitchen, living room, study, hallway, cellar, attic, garden, garage,
pantry, office, lounge, chapel, conservatory, ballroom, dining room, parlor,
nursery, balcony, courtyard, observatory, gallery, theater, basement

Valid Weapons

knife, pipe, rope, wrench, candle, poison, revolver, hammer, axe, poker,
dagger, chain, bat, statue, scissors, shovel, vial, club, cable, needle

Valid Wounds

knife     -> knife wound
pipe      -> pipe bruise
rope      -> rope mark
wrench    -> wrench bruise
candle    -> candle wax
poison    -> poison trace
revolver  -> bullet wound
hammer    -> hammer dent
axe       -> axe wound
poker     -> poker burn
dagger    -> dagger wound
chain     -> chain mark
bat       -> bat bruise
statue    -> statue blow
scissors  -> scissor cut
shovel    -> shovel mark
vial      -> glass cut
club      -> club bruise
cable     -> cable mark
needle    -> needle mark

Caveats

The data is highly templated.
Names, locations, weapons, and wounds are intentionally constrained.
The corpus intentionally contains many repeated structural patterns because it targets very small language models.

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