Memorisation-Profiles
Collection
Artefacts for the paper "Causal Estimation of Memorisation Profiles" (Lesci et al., 2024)
•
14 items
•
Updated
cohort
int64 1k
143k
| base_period
int64 0
142k
| time
int64 1k
143k
| ATT
float64 -0.19
1.14
| std_error
float64 0
1.2
| lower
float64 -4.06
0
| upper
float64 -0.07
6.32
| zero_not_in_cband
stringclasses 2
values | model_size
stringclasses 7
values | stat
stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|
1,000
| 0
| 1,000
| 0.115188
| 0.079778
| -0.237697
| 0.468073
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 2,000
| 0.126388
| 0.072622
| -0.194843
| 0.447619
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 3,000
| 0.121021
| 0.073265
| -0.203056
| 0.445098
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 4,000
| 0.122361
| 0.074109
| -0.205451
| 0.450173
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 5,000
| 0.125462
| 0.072099
| -0.193456
| 0.444379
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 6,000
| 0.124346
| 0.072583
| -0.196713
| 0.445405
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 7,000
| 0.121837
| 0.072636
| -0.199456
| 0.44313
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 8,000
| 0.120362
| 0.071686
| -0.196728
| 0.437452
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 9,000
| 0.125104
| 0.071127
| -0.189518
| 0.439725
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 10,000
| 0.121432
| 0.071825
| -0.196277
| 0.439141
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 11,000
| 0.120983
| 0.069619
| -0.186964
| 0.42893
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 12,000
| 0.118659
| 0.0714
| -0.197167
| 0.434484
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 13,000
| 0.117997
| 0.072058
| -0.200743
| 0.436737
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 14,000
| 0.124449
| 0.072755
| -0.197372
| 0.446271
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 15,000
| 0.118597
| 0.072646
| -0.20274
| 0.439934
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 16,000
| 0.122126
| 0.071024
| -0.192039
| 0.436292
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 17,000
| 0.12517
| 0.071768
| -0.192283
| 0.442624
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 18,000
| 0.120269
| 0.071902
| -0.19778
| 0.438318
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 19,000
| 0.125931
| 0.072689
| -0.195598
| 0.44746
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 20,000
| 0.127666
| 0.071976
| -0.19071
| 0.446043
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 21,000
| 0.122732
| 0.071279
| -0.192559
| 0.438024
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 22,000
| 0.122041
| 0.07059
| -0.190203
| 0.434285
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 23,000
| 0.122626
| 0.071065
| -0.191722
| 0.436973
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 24,000
| 0.120638
| 0.070194
| -0.189853
| 0.431129
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 25,000
| 0.121392
| 0.07106
| -0.192932
| 0.435716
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 26,000
| 0.121166
| 0.072302
| -0.198652
| 0.440984
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 27,000
| 0.125774
| 0.072991
| -0.197092
| 0.44864
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 28,000
| 0.11892
| 0.071989
| -0.199512
| 0.437352
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 29,000
| 0.12677
| 0.071475
| -0.189391
| 0.44293
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 30,000
| 0.127778
| 0.071385
| -0.187983
| 0.443539
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 31,000
| 0.127853
| 0.071974
| -0.190514
| 0.44622
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 32,000
| 0.12538
| 0.071411
| -0.190494
| 0.441254
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 33,000
| 0.122878
| 0.069576
| -0.184881
| 0.430636
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 34,000
| 0.12709
| 0.071399
| -0.188732
| 0.442911
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 35,000
| 0.121455
| 0.070737
| -0.191438
| 0.434347
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 36,000
| 0.118224
| 0.070882
| -0.195313
| 0.431761
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 37,000
| 0.120345
| 0.070933
| -0.193417
| 0.434108
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 38,000
| 0.123562
| 0.072268
| -0.196104
| 0.443229
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 39,000
| 0.118626
| 0.070026
| -0.191125
| 0.428377
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 40,000
| 0.128304
| 0.071156
| -0.186443
| 0.443051
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 41,000
| 0.124666
| 0.071137
| -0.189996
| 0.439329
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 42,000
| 0.124979
| 0.069927
| -0.184333
| 0.434291
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 43,000
| 0.124465
| 0.072255
| -0.195145
| 0.444074
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 44,000
| 0.124293
| 0.070863
| -0.189157
| 0.437743
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 45,000
| 0.119923
| 0.07023
| -0.19073
| 0.430576
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 46,000
| 0.1233
| 0.070072
| -0.186651
| 0.433251
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 47,000
| 0.122602
| 0.071248
| -0.192554
| 0.437758
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 48,000
| 0.125689
| 0.071008
| -0.188406
| 0.439784
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 49,000
| 0.125509
| 0.072196
| -0.193837
| 0.444856
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 50,000
| 0.116656
| 0.070076
| -0.193314
| 0.426626
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 51,000
| 0.12071
| 0.070555
| -0.19138
| 0.432799
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 52,000
| 0.123946
| 0.070678
| -0.188688
| 0.43658
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 53,000
| 0.122943
| 0.070324
| -0.188125
| 0.43401
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 54,000
| 0.119403
| 0.069307
| -0.187164
| 0.425971
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 55,000
| 0.119456
| 0.069482
| -0.187886
| 0.426797
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 56,000
| 0.125629
| 0.068697
| -0.17824
| 0.429498
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 57,000
| 0.126156
| 0.069537
| -0.181433
| 0.433745
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 58,000
| 0.122997
| 0.069364
| -0.183824
| 0.429818
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 59,000
| 0.124823
| 0.071376
| -0.190896
| 0.440542
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 60,000
| 0.124877
| 0.069389
| -0.182053
| 0.431807
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 61,000
| 0.11912
| 0.068995
| -0.186069
| 0.424308
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 62,000
| 0.121624
| 0.070295
| -0.189316
| 0.432563
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 63,000
| 0.120522
| 0.069311
| -0.186067
| 0.42711
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 64,000
| 0.11993
| 0.068627
| -0.18363
| 0.42349
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 65,000
| 0.123632
| 0.069262
| -0.18274
| 0.430003
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 66,000
| 0.126907
| 0.0695
| -0.180516
| 0.43433
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 67,000
| 0.123033
| 0.069406
| -0.183973
| 0.430039
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 68,000
| 0.119838
| 0.069266
| -0.186551
| 0.426227
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 69,000
| 0.117953
| 0.069011
| -0.187306
| 0.423212
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 70,000
| 0.121473
| 0.069801
| -0.187282
| 0.430229
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 71,000
| 0.120718
| 0.069481
| -0.186621
| 0.428058
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 72,000
| 0.122137
| 0.069507
| -0.185317
| 0.429592
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 73,000
| 0.122294
| 0.068112
| -0.178991
| 0.423579
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 74,000
| 0.123358
| 0.069748
| -0.185163
| 0.431879
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 75,000
| 0.123367
| 0.068569
| -0.179938
| 0.426672
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 76,000
| 0.121667
| 0.06903
| -0.183674
| 0.427009
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 77,000
| 0.120186
| 0.06844
| -0.182549
| 0.422922
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 78,000
| 0.120844
| 0.069291
| -0.185654
| 0.427343
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 79,000
| 0.120459
| 0.06898
| -0.184666
| 0.425583
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 80,000
| 0.118945
| 0.068899
| -0.185821
| 0.42371
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 81,000
| 0.116564
| 0.068437
| -0.186157
| 0.419284
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 82,000
| 0.121882
| 0.06856
| -0.181384
| 0.425148
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 83,000
| 0.121359
| 0.068902
| -0.183417
| 0.426135
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 84,000
| 0.120011
| 0.069714
| -0.18836
| 0.428382
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 85,000
| 0.120779
| 0.070038
| -0.189024
| 0.430582
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 86,000
| 0.124594
| 0.069441
| -0.182569
| 0.431757
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 87,000
| 0.120256
| 0.069156
| -0.185646
| 0.426157
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 88,000
| 0.1179
| 0.068789
| -0.186379
| 0.422179
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 89,000
| 0.117589
| 0.069234
| -0.188658
| 0.423836
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 90,000
| 0.120905
| 0.069456
| -0.186321
| 0.428131
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 91,000
| 0.11865
| 0.06914
| -0.187182
| 0.424483
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 92,000
| 0.12133
| 0.069381
| -0.185566
| 0.428227
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 93,000
| 0.12067
| 0.069606
| -0.18722
| 0.42856
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 94,000
| 0.118219
| 0.068192
| -0.183419
| 0.419856
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 95,000
| 0.11658
| 0.069864
| -0.192453
| 0.425613
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 96,000
| 0.11471
| 0.068161
| -0.18679
| 0.416209
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 97,000
| 0.11722
| 0.06966
| -0.190911
| 0.425351
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 98,000
| 0.120441
| 0.068905
| -0.184352
| 0.425234
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 99,000
| 0.119353
| 0.068594
| -0.184063
| 0.422768
|
6.9b
|
sequence_surprisal
|
|
1,000
| 0
| 100,000
| 0.121007
| 0.069214
| -0.18515
| 0.427164
|
6.9b
|
sequence_surprisal
|
End of preview. Expand
in Data Studio
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.
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Size of downloaded dataset files:
87.7 MB
Size of the auto-converted Parquet files:
87.7 MB
Number of rows:
2,126,696