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146M
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3,385
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3,457
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3,097
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3,701
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[10.6262207031,10.626909256,10.6265420914,10.627040863,10.626865387,10.6258115768,10.6267356873,10.6(...TRUNCATED)
3,779
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3,103
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[10.626996994,10.6247320175,10.6265468597,10.6231985092,10.625169754,10.6231050491,10.6250228882,10.(...TRUNCATED)
4,085
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[18350,45827,35052,41903,11428,23788,2066,2303,28777,32683,6082,39314,40138,5475,32367,14912,39770,3(...TRUNCATED)
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3,509
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[23894,7169,10881,32829,28615,3459,17433,28906,6052,9290,15372,14820,29380,20998,25850,1630,8334,326(...TRUNCATED)
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3,785
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This dataset has been created as an artefact of the paper Causal Estimation of Memorisation Profiles (Lesci et al., 2024). More info about this dataset in the related collection Memorisation-Profiles.

Collection of data statistics computed using the intermediate checkpoints (step0, step1000, ..., step143k) of all Pythia deduped versions. This folder contains the model evaluations (or "stats") for each model size included in the study. This is the "raw" version where we have stats at the token level. We gathered these statistics "just in case" since the inference process was expensive. However, we provide the sequence-level statistics in the pietrolesci/pythia-deduped-stats dataset.

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