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Datasets:
TabBioMed
/
avida_hil6_onehot

Tasks:
Tabular Classification
Modalities:
Tabular
Formats:
parquet
Size:
10K - 100K
Tags:
tabular
biomedical
benchmark
TabBioMed
proteomics
binary
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
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row_idx
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14
18
17
18
16
16
7
7
7
7
7
7
0
0
0
0
0
0
0
0
0
0
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0
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13
14
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4
3
8
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3
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10
10
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1
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3
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53
1
1
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10
1
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1
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6
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7
7
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7
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0
0
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1
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54
1
1
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6
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7
7
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0
0
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1
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1
1
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7
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0
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1
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7
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1
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7
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0
0
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1
1
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7
7
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0
0
0
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59
1
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7
7
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0
0
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1
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7
7
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0
0
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1
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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
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1
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18
17
18
16
16
7
7
7
7
7
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13
14
17
4
1
8
8
6
9
2
2
16
4
10
13
3
5
6
4
17
10
10
10
16
4
14
5
3
17
3
0
67
1
1
6
10
10
10
10
1
1
14
13
1
11
1
14
18
14
10
14
4
16
6
6
6
10
18
14
1
6
6
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10
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16
2
1
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12
4
8
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19
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1
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1
17
11
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6
3
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20
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9
3
16
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6
15
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17
8
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3
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1
9
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11
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10
14
11
12
12
10
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4
3
17
1
18
20
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2
15
1
1
6
15
15
5
3
20
19
6
14
6
17
14
18
17
18
16
16
7
7
7
7
7
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13
14
17
1
3
8
8
6
9
2
2
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4
10
13
3
5
6
4
17
10
10
10
16
4
14
5
3
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3
0
68
1
1
6
10
10
10
10
1
1
14
13
1
11
1
14
18
14
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14
4
16
6
6
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6
6
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10
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6
6
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1
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9
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3
12
1
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11
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10
9
10
4
3
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6
18
20
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2
12
6
3
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13
13
19
8
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16
9
15
8
8
20
19
6
14
6
17
14
18
17
18
16
16
7
7
7
7
7
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13
14
17
4
3
8
8
6
9
2
2
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4
10
13
3
5
6
1
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10
10
10
16
4
14
5
3
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3
0
69
1
1
6
10
10
10
10
1
1
14
13
1
11
1
14
18
14
10
14
4
16
6
6
6
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18
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17
6
6
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10
15
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1
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1
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1
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1
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6
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1
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9
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3
12
1
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9
13
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3
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1
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20
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1
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6
13
9
10
3
20
19
6
14
6
17
14
18
17
18
16
16
7
7
7
7
7
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13
14
17
4
3
8
8
6
9
2
2
1
4
10
13
3
5
6
4
17
10
10
10
16
4
14
5
3
17
3
0
70
1
1
6
10
10
10
10
1
1
14
13
1
11
1
14
18
14
10
14
4
16
6
6
6
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18
14
13
6
6
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10
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10
1
2
1
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6
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3
8
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8
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20
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14
1
13
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1
17
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16
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17
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15
3
16
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9
6
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3
17
1
4
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10
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10
9
13
4
3
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1
18
20
17
2
14
1
3
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20
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12
6
3
3
3
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5
14
5
5
6
5
19
6
14
6
17
14
18
17
16
16
7
7
7
7
7
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13
14
17
4
3
8
8
6
9
2
2
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4
10
13
3
5
6
4
17
10
10
10
1
4
14
5
3
17
3
0
71
1
1
6
10
10
10
10
1
1
14
13
1
11
1
14
18
14
10
14
4
16
6
6
6
10
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9
13
6
6
16
10
15
10
16
2
14
1
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1
11
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18
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1
13
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9
6
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3
8
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3
6
3
12
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20
20
1
3
16
18
9
6
15
5
17
8
16
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3
12
1
9
12
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3
10
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11
12
16
10
9
13
4
3
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6
18
20
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2
1
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3
15
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1
17
8
1
15
11
18
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15
1
3
5
6
16
19
6
14
6
17
18
17
18
16
16
7
7
7
7
7
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13
14
17
4
3
8
1
6
9
2
2
16
4
10
13
3
5
6
4
17
10
10
10
16
4
14
5
3
17
3
0
72
1
1
6
10
10
10
10
1
1
14
13
1
11
1
14
18
14
10
14
4
16
6
6
6
10
18
14
13
6
6
16
10
15
10
16
2
1
1
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20
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8
6
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5
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1
13
6
9
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4
1
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2
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1
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1
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1
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6
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8
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3
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1
9
12
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10
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11
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1
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1
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15
16
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6
16
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4
14
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2
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11
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16
14
20
3
20
19
6
14
6
17
14
18
17
18
16
16
7
7
7
7
7
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13
14
17
4
3
8
8
1
9
2
2
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4
10
13
3
5
6
4
17
10
10
10
16
4
14
5
3
17
3
0
73
1
1
6
10
10
10
10
1
1
14
13
1
11
1
14
18
14
10
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4
16
6
6
6
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14
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6
6
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10
15
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16
2
10
6
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10
1
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1
18
1
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5
15
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1
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6
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4
6
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16
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11
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3
4
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8
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1
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9
6
15
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8
16
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3
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1
9
12
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10
14
11
12
12
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9
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4
3
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1
18
20
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2
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1
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6
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7
13
16
16
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8
16
12
2
6
15
4
20
3
20
19
6
14
6
17
14
18
17
18
16
16
7
7
7
7
7
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13
14
17
4
3
1
8
6
9
2
2
16
4
10
13
3
5
6
4
17
10
10
10
16
4
14
5
3
17
3
0
74
1
1
6
10
10
10
10
1
1
14
13
1
11
1
14
18
14
10
14
4
16
6
6
6
10
1
14
13
6
6
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10
15
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16
2
1
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5
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5
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1
16
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6
19
20
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1
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4
2
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1
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8
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3
6
6
17
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1
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18
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6
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8
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3
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1
9
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18
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10
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11
12
16
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9
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4
3
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1
18
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2
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1
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6
17
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6
17
6
2
14
7
17
14
6
17
14
18
17
18
16
16
7
7
7
7
7
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13
14
17
4
3
8
8
6
9
2
2
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4
10
13
3
5
6
4
17
10
10
10
16
4
14
5
1
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3
0
75
1
1
6
10
10
10
10
1
1
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13
1
11
1
14
18
14
10
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4
16
6
6
6
10
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14
1
6
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6
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1
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1
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1
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9
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1
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3
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1
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1
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6
6
3
20
3
20
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6
14
6
17
14
18
17
18
16
16
7
7
7
7
7
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13
14
17
4
3
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8
6
9
2
2
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4
10
13
3
5
6
4
17
10
10
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16
4
1
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3
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3
0
76
1
1
6
10
10
10
10
1
1
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13
1
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1
14
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14
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4
16
6
6
6
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6
6
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1
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1
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1
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3
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1
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1
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1
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1
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1
1
20
18
3
9
6
17
14
18
17
18
16
16
7
7
7
7
7
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13
14
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4
3
8
8
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2
2
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4
10
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3
5
6
4
17
10
10
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16
4
14
1
3
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3
1
77
1
1
6
10
10
10
10
1
1
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1
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1
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18
14
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4
16
6
6
6
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1
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3
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2
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1
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1
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8
6
1
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6
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6
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14
18
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18
16
16
7
7
7
7
7
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0
0
0
0
0
0
0
0
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0
0
0
0
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0
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13
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4
3
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8
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2
2
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1
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3
5
6
4
17
10
10
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16
4
14
5
3
17
3
0
78
1
1
6
10
10
10
10
1
1
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1
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1
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14
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6
6
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1
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1
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14
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7
7
7
7
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0
0
0
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0
0
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13
14
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4
3
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6
1
2
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3
5
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10
10
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4
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3
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3
0
79
1
1
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10
10
10
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1
1
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1
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1
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4
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6
6
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1
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3
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7
7
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0
0
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13
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3
1
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2
2
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3
5
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10
10
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4
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3
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3
0
80
1
1
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10
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1
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1
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6
6
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9
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0
End of preview. Expand in Data Studio

avida_hil6_onehot

Part of TabBioMed (TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning), a benchmark of 94 curated biomedical tabular datasets. Code, preprocessing and documentation: https://github.com/AI-sandbox/TabBioMed.

Binary classification: predict VHH nanobody binding to one of 31 hIL6 antigen variants (wild type + 30 alanine mutants). Features: padded integer AA token IDs — VHH padded/truncated to the maximum training VHH length, Ag padded/truncated to the maximum training Ag length, then concatenated; vocab size 21 (0=PAD). All columns are categorical (cat_idx covers all).. Split: VHH-family-disjoint — VHH cores clustered with CD-HIT (identity 0.9, coverage 0.8), families assigned to train/val/test (about 70/15/15 of VHHs) by a label-balanced search, and validation/test VHHs with >= 0.9 identity to any training VHH removed; all 31 antigens occur in every split. Per-split sample caps apply.

Domain Task Samples Features Classes
Proteomics binary 29,995 193 2

Splits. Fixed train/val/test split (9999/9999/9997 rows).

Files

  • data/**.parquet: one row per sample. row_idx is the row index in the original TabBioMed array; f_<i>[_<name>] are the features (in order); target (or target_<j> for multi-target tasks) is the label. Categorical feature indices (0-based, among the features): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192]. Missing values are stored as nulls.
  • metadata.json: TabBioMed task metadata (task type, categorical indices, source, description).
from datasets import load_dataset
ds = load_dataset("TabBioMed/avida_hil6_onehot")

A Croissant (1.1) description with Responsible-AI fields is distributed with the TabBioMed release.

Source and license

Source: COGNANO AVIDa datasets (HuggingFace: cognano/AVIDa-hIL6, cognano/AVIDa-hTNFa, cognano/AVIDa-SARS-CoV-2). VHH (nanobody) sequences with binary binding labels against antigen variants.

This derived dataset is distributed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Please also cite and respect the terms of the original source.

Citation 

@inproceedings{tabbiomed2026,
  title     = {TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning},
  author    = {Mateo Bernad{\'o}, Pau and Polapragada, Saivenkata Nagavyjayanthi and Bonet, David and Arbiol Rakuljic, Pol and Mogas Pladevall, Laia and Comajoan Cara, Mar{\c{c}}al and Gonzalez Ferrer, Jesus and Abante, Jordi and Mas Montserrat, Daniel and Haussler, David and Ioannidis, Alexander G.},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
  year      = {2026}
}
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