Datasets:
MinaMila
/
GermanCredit_train_CDF_lr_33

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status of existing checking account_0 to less than 200 DM
int64
0
1
status of existing checking account_200 DM or more
int64
0
1
status of existing checking account_less than 0 DM
int64
0
1
status of existing checking account_no checking account
int64
0
1
purpose of credit_for business
int64
0
1
purpose of credit_for education
int64
0
1
purpose of credit_for other reasons
int64
0
1
purpose of credit_for repairs
int64
0
1
purpose of credit_for retraining
int64
0
1
purpose of credit_to buy a new car
int64
0
1
purpose of credit_to buy a radio or television
int64
0
1
purpose of credit_to buy a used car
int64
0
1
purpose of credit_to buy domestic appliances
int64
0
1
purpose of credit_to buy furniture or equipment
int64
0
1
sex_male
int64
0
1
housing_owned
int64
0
1
housing_provided for free
int64
0
1
housing_rented
int64
0
1
job status_manager, self-employed, or highly qualified
int64
0
1
job status_skilled worker or official
int64
0
1
job status_unemployed or unskilled (non-resident)
int64
0
1
job status_unskilled worker (resident)
int64
0
1
marital status_divorced / separated
int64
0
1
marital status_divorced / separated / married
int64
0
1
marital status_married / widowed
int64
0
1
marital status_single
int64
0
1
duration in months
int64
4
60
credit amount
int64
338
15.9k
age
int64
20
75
credit worthiness
int64
0
1
0
0
0
1
1
0
0
0
0
0
0
0
0
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1
0
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1
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0
0
36
9,572
28
0
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1
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1
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1
0
12
1,680
35
1
0
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1
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1
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0
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1
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1
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1
0
0
1
0
0
0
36
2,302
31
0
0
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1
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1
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1
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0
15
3,186
20
1
0
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1
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0
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1
0
0
0
0
1
1
0
0
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1
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0
0
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1
10
1,038
49
1
0
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1
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1
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0
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1
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0
1
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0
0
0
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1
24
2,397
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0
0
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1
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24
1,851
33
1
0
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1
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1
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12
3,386
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0
1
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1
1
1
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1
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1
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24
4,736
25
0
0
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1
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1
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1
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0
1
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1
48
10,127
44
0
0
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1
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0
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0
0
0
0
1
0
1
1
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0
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1
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1
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0
15
874
24
1
1
0
0
0
0
1
0
0
0
0
0
0
0
0
1
1
0
0
1
0
0
0
0
1
0
0
9
1,501
34
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
1
0
0
1
1
0
0
0
1
0
0
0
6
3,384
44
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
1
24
2,538
47
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
1
12
4,843
43
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
1
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1
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1
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24
7,758
29
1
0
0
1
0
0
0
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1
0
0
0
0
0
1
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0
0
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0
1
0
0
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1
18
4,380
35
1
1
0
0
0
1
0
0
0
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0
0
0
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1
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1
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1
18
2,427
42
1
1
0
0
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0
0
0
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1
0
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0
0
1
0
0
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1
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1
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48
8,487
24
1
0
0
1
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0
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1
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0
0
0
1
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1
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1
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24
1,024
48
0
0
1
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1
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1
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1
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0
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12
939
28
0
1
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1
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1
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1
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1
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30
2,991
25
1
1
0
0
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1
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0
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1
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1
0
1
0
0
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1
36
1,953
61
0
0
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1
0
1
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0
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1
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1
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1
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1
45
11,816
29
0
0
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1
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1
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18
1,808
22
0
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1
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1
12
3,386
35
0
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1
1
1
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1
24
2,483
22
1
0
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1
0
0
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1
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1
1
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0
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1
36
9,629
24
0
1
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1
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0
1
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1
30
1,919
30
0
0
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1
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1
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0
1
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1
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1
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1
36
10,477
42
1
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1
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1
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1
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1
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24
2,910
34
1
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1
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1
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1
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1
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12
2,073
28
1
1
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1
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2,150
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0
1
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1,501
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2,538
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1
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1,967
20
1
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3,527
45
1
1
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1
1
1
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1,577
20
1
1
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1
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21
3,976
35
1
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1
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36
9,271
24
0
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10
2,848
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1
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2,538
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2,022
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1
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14,896
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1,413
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2,397
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2,116
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5,003
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1,343
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1
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1,842
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7,685
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2,538
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2,613
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1
1
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2,671
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4,057
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2,319
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2,150
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2,603
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1
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30
1,867
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1
0
1
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2,327
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18
1,864
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1
9
2,753
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1,275
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939
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36
2,384
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9
1,154
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1
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12
701
40
1
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18
2,864
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18
1,940
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24
1,747
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1
1
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60
14,782
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15
4,623
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30
3,496
34
1
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42
4,796
56
1
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1
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1
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18
4,594
32
1
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24
2,978
32
1
1
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24
1,216
38
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2,397
35
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15
2,327
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12
766
66
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448
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6
1,554
24
1
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
1
0
0
15
392
23
1
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
1
0
0
1
0
0
12
926
38
1
0
0
0
1
0
0
0
0
0
0
1
0
0
0
1
0
0
1
0
1
0
0
0
1
0
0
18
433
22
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
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0
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0
0
6
448
23
0
1
0
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0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
1
0
0
0
0
0
1
12
1,331
22
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
1
0
0
1
0
0
0
1
0
0
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18
3,780
35
1
0
1
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
1
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12
939
28
0
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1
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0
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1
0
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0
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1
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1
6
3,676
37
1
1
0
0
0
1
0
0
0
0
0
0
0
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0
0
0
1
0
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0
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1
48
6,681
38
1
1
0
0
0
0
0
0
0
0
0
1
0
0
0
1
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1
0
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1
0
0
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1
0
0
36
2,671
50
0
1
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0
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0
0
0
0
0
1
0
0
0
0
0
0
1
0
1
0
0
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1
0
24
3,092
22
0
0
0
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1
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0
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1
0
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12
930
65
1
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1
36
5,842
35
1
1
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1
1
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6
753
64
1
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21
1,188
39
0
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1
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1
60
6,288
42
0
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1
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1
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1
18
2,659
28
1
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1
1
1
0
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24
4,351
48
1