Pietro Lesci
pietrolesci
AI & ML interests
I like developing and applying causal methods to study the effect of training choices on models’ behaviour, including memorisation, shortcut learning, and tokenisation.
Organizations
models
27
pietrolesci/tokenisers
Updated
pietrolesci/tokenizers
Updated
pietrolesci/small_bpe128k
Updated
pietrolesci/small_langspec128k
Updated
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12
pietrolesci/small_unigramlm128k
Updated
pietrolesci/small_tokmix128k
Updated
pietrolesci/small_multigram128k
Updated
pietrolesci/me100M_finewebedu-20B_bpe32000minipile
Updated
pietrolesci/me100M-tied_finewebedu-20B_bpe32000minipile
Updated
pietrolesci/me850M_minipile_bpe32000minipile
Updated
datasets
56
pietrolesci/unimixlm
Viewer
•
Updated
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81.9M
•
488
pietrolesci/me-minipile-evals
Viewer
•
Updated
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1.22M
•
15
pietrolesci/pile-deduped
Viewer
•
Updated
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748M
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55
pietrolesci/pythia-deduped-memorisation-profiles
Viewer
•
Updated
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2.13M
•
27
pietrolesci/pile-validation
Viewer
•
Updated
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429k
•
43
pietrolesci/pile-deduped-subset
Viewer
•
Updated
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16.3k
•
70
pietrolesci/pythia-deduped-stats
Viewer
•
Updated
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16.3M
•
1.15k
pietrolesci/pythia-deduped-stats-raw
Viewer
•
Updated
•
14.9M
•
19.8k
pietrolesci/agnews
Viewer
•
Updated
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510k
•
59
pietrolesci/amazoncat-13k
Viewer
•
Updated
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5.99M
•
2.61k
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1