Datasets:

Languages:
English
ArXiv:
Libraries:
Datasets
License:
class_id
stringlengths
7
10
display_name
stringlengths
3
18
ngram
stringlengths
3
18
label
sequence
object_group
class label
3 classes
text
stringlengths
15
56
template_group
class label
2 classes
template_idx
int32
0
19
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
All rulers are [MASK].
1text-masked
0
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
Commonly rulers are [MASK].
1text-masked
1
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
Everyone knows that most rulers are [MASK].
1text-masked
2
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
Everyone knows that rulers are [MASK].
1text-masked
3
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
It is known that most rulers are [MASK].
1text-masked
4
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
It is known that rulers are [MASK].
1text-masked
5
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
It's known that most rulers are [MASK].
1text-masked
6
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
It's known that rulers are [MASK].
1text-masked
7
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
Most rulers are [MASK].
1text-masked
8
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
This ruler is [MASK].
1text-masked
9
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
a jpeg corrupted photo of a ruler.
0clip-imagenet
10
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
a photo of a large ruler.
0clip-imagenet
11
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
a photo of hard to see rulers.
0clip-imagenet
12
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
a photo of the hard to see ruler.
0clip-imagenet
13
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
a pixelated photo of a ruler.
0clip-imagenet
14
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
a pixelated photo of rulers.
0clip-imagenet
15
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
itap of many rulers.
0clip-imagenet
16
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
itap of my ruler.
0clip-imagenet
17
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
itap of some rulers.
0clip-imagenet
18
/m/0hdln
Ruler
ruler
[ 0.0181818176060915, 0.036363635212183, 0.30772727727890015, 0.0181818176060915, 0.036363635212183, 0.08636363595724106, 0.036363635212183, 0.036363635212183, 0.036363635212183, 0.08636363595724106, 0.30136364698410034 ]
2Any
the cartoon ruler.
0clip-imagenet
19
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
All toothbrushes are [MASK].
1text-masked
0
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
Commonly toothbrushes are [MASK].
1text-masked
1
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
Everyone knows that most toothbrushes are [MASK].
1text-masked
2
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
Everyone knows that toothbrushes are [MASK].
1text-masked
3
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
It is known that most toothbrushes are [MASK].
1text-masked
4
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
It is known that toothbrushes are [MASK].
1text-masked
5
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
It's known that most toothbrushes are [MASK].
1text-masked
6
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
It's known that toothbrushes are [MASK].
1text-masked
7
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
Most toothbrushes are [MASK].
1text-masked
8
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
This toothbrush is [MASK].
1text-masked
9
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
a jpeg corrupted photo of a toothbrush.
0clip-imagenet
10
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
a photo of a large toothbrush.
0clip-imagenet
11
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
a photo of hard to see toothbrushes.
0clip-imagenet
12
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
a photo of the hard to see toothbrush.
0clip-imagenet
13
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
a pixelated photo of a toothbrush.
0clip-imagenet
14
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
a pixelated photo of toothbrushes.
0clip-imagenet
15
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
itap of many toothbrushes.
0clip-imagenet
16
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
itap of my toothbrush.
0clip-imagenet
17
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
itap of some toothbrushes.
0clip-imagenet
18
/m/012xff
Toothbrush
toothbrush
[ 0.049543049186468124, 0.21091870963573456, 0.04689754545688629, 0.04689754545688629, 0.10906685888767242, 0.04292929172515869, 0.10377585142850876, 0.04292929172515869, 0.08261182904243469, 0.1699134260416031, 0.09451659768819809 ]
2Any
the cartoon toothbrush.
0clip-imagenet
19
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
All jellyfish are [MASK].
1text-masked
0
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
Commonly jellyfish are [MASK].
1text-masked
1
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
Everyone knows that most jellyfish are [MASK].
1text-masked
2
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
Everyone knows that jellyfish are [MASK].
1text-masked
3
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
It is known that most jellyfish are [MASK].
1text-masked
4
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
It is known that jellyfish are [MASK].
1text-masked
5
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
It's known that most jellyfish are [MASK].
1text-masked
6
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
It's known that jellyfish are [MASK].
1text-masked
7
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
Most jellyfish are [MASK].
1text-masked
8
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
This jellyfish is [MASK].
1text-masked
9
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
a jpeg corrupted photo of a jellyfish.
0clip-imagenet
10
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
a photo of a large jellyfish.
0clip-imagenet
11
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
a photo of hard to see jellyfish.
0clip-imagenet
12
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
a photo of the hard to see jellyfish.
0clip-imagenet
13
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
a pixelated photo of a jellyfish.
0clip-imagenet
14
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
a pixelated photo of jellyfish.
0clip-imagenet
15
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
itap of many jellyfish.
0clip-imagenet
16
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
itap of my jellyfish.
0clip-imagenet
17
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
itap of some jellyfish.
0clip-imagenet
18
/m/0d8zb
Jellyfish
jellyfish
[ 0.022510822862386703, 0.07727272808551788, 0.18441557884216309, 0.06125541031360626, 0.022510822862386703, 0.022510822862386703, 0.21406926214694977, 0.07662338018417358, 0.03982684016227722, 0.24393939971923828, 0.0350649356842041 ]
2Any
the cartoon jellyfish.
0clip-imagenet
19
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
All houses are [MASK].
1text-masked
0
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
Commonly houses are [MASK].
1text-masked
1
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
Everyone knows that most houses are [MASK].
1text-masked
2
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
Everyone knows that houses are [MASK].
1text-masked
3
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
It is known that most houses are [MASK].
1text-masked
4
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
It is known that houses are [MASK].
1text-masked
5
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
It's known that most houses are [MASK].
1text-masked
6
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
It's known that houses are [MASK].
1text-masked
7
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
Most houses are [MASK].
1text-masked
8
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
This house is [MASK].
1text-masked
9
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
a jpeg corrupted photo of a house.
0clip-imagenet
10
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
a photo of a large house.
0clip-imagenet
11
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
a photo of hard to see houses.
0clip-imagenet
12
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
a photo of the hard to see house.
0clip-imagenet
13
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
a pixelated photo of a house.
0clip-imagenet
14
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
a pixelated photo of houses.
0clip-imagenet
15
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
itap of many houses.
0clip-imagenet
16
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
itap of my house.
0clip-imagenet
17
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
itap of some houses.
0clip-imagenet
18
/m/03jm5
House
house
[ 0.03851540759205818, 0.07498525828123093, 0.23225343227386475, 0.12385743856430054, 0.07623839378356934, 0.007352941203862429, 0.01925770379602909, 0.007352941203862429, 0.03396358713507652, 0.30368199944496155, 0.08254091441631317 ]
2Any
the cartoon house.
0clip-imagenet
19
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
All stationary bicycles are [MASK].
1text-masked
0
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
Commonly stationary bicycles are [MASK].
1text-masked
1
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
Everyone knows that most stationary bicycles are [MASK].
1text-masked
2
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
Everyone knows that stationary bicycles are [MASK].
1text-masked
3
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
It is known that most stationary bicycles are [MASK].
1text-masked
4
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
It is known that stationary bicycles are [MASK].
1text-masked
5
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
It's known that most stationary bicycles are [MASK].
1text-masked
6
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
It's known that stationary bicycles are [MASK].
1text-masked
7
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
Most stationary bicycles are [MASK].
1text-masked
8
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
This stationary bicycle is [MASK].
1text-masked
9
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
a jpeg corrupted photo of a stationary bicycle.
0clip-imagenet
10
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
a photo of a large stationary bicycle.
0clip-imagenet
11
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
a photo of hard to see stationary bicycles.
0clip-imagenet
12
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
a photo of the hard to see stationary bicycle.
0clip-imagenet
13
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
a pixelated photo of a stationary bicycle.
0clip-imagenet
14
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
a pixelated photo of stationary bicycles.
0clip-imagenet
15
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
itap of many stationary bicycles.
0clip-imagenet
16
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
itap of my stationary bicycle.
0clip-imagenet
17
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
itap of some stationary bicycles.
0clip-imagenet
18
/m/03kt2w
Stationary bicycle
stationary bicycle
[ 0.4010526239871979, 0.0671052634716034, 0.0335526317358017, 0.18605263531208038, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.02302631549537182, 0.12355262786149979, 0.07355263084173203, 0.02302631549537182 ]
2Any
the cartoon stationary bicycle.
0clip-imagenet
19
YAML Metadata Warning: The task_categories "text-scoring" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other
YAML Metadata Warning: The task_ids "text-scoring-other-distribution-prediction" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering

Dataset Card for CoDa

Dataset Summary

The Color Dataset (CoDa) is a probing dataset to evaluate the representation of visual properties in language models. CoDa consists of color distributions for 521 common objects, which are split into 3 groups. We denote these groups as Single, Multi, and Any, which represents the typical object of each group.

The default configuration of CoDa uses 10 CLIP-style templates (e.g. "A photo of a [object]"), and 10 cloze-style templates (e.g. "Everyone knows most [object] are [color]." )

Supported Tasks and Leaderboards

This version of the dataset consists of the filtered and templated examples as cloze style questions. See the GitHub repo for the raw data (e.g. unfiltered annotations) as well as example usage with GPT-2, RoBERTa, ALBERT, and CLIP.

Languages

The text in the dataset is in English. The associated BCP-47 code is en-US.

Dataset Structure

Data Instances

An example looks like this:

{
  "text": "All rulers are [MASK].",
  "label": [
    0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352,
    0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636,
    0.301363647
  ],
  "template_group": 1,
  "template_idx": 0,
  "class_id": "/m/0hdln",
  "display_name": "Ruler",
  "object_group": 2,
  "ngram": "ruler"
}

Data Fields

  • text: The templated example. What this is depends on the value of template_group.
    • template_group=0: A CLIP style example. There are no [MASK] tokens in these examples.
    • template_group=1: A cloze style example. Note that all templates have [MASK] as the last word, but in most cases, the period should be included.
  • label: A list of probability values for the 11 colors. Note that these are sorted by the alphabetic order of the 11 colors (black, blue, brown, gray, green, orange, pink, purple, red, white, yellow).
  • template_group: Type of template, 0 corresponds to A CLIP style template (clip-imagenet), and 1 corresponds to A cloze style templates (text-masked).
  • template_idx: The index of the template out of all templates
  • class_id: The Corresponding OpenImages v6 ClassID.
  • display_name: The Corresponding OpenImages v6 DisplayName.
  • object_group: Object Group, values correspond to Single, Multi, and Any.
  • ngram: Corresponding n-gram used for lookups.

Data Splits

Object Splits:

Group All Train Valid Test
Single 198 118 39 41
Multi 208 124 41 43
Any 115 69 23 23
Total 521 311 103 107

Example Splits:

Group All Train Valid Test
Single 3946 2346 780 820
Multi 4146 2466 820 860
Any 2265 1352 460 453
Total 10357 6164 2060 2133

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

CoDa is licensed under the Apache 2.0 license.

Citation Information

@misc{paik2021world,
      title={The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of Color},
      author={Cory Paik and Stéphane Aroca-Ouellette and Alessandro Roncone and Katharina Kann},
      year={2021},
      eprint={2110.08182},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Contributions

Thanks to @github-username for adding this dataset.

Downloads last month
79