Datasets:
coda

Languages: English
Multilinguality: monolingual
Size Categories: 10K<n<100K
Language Creators: expert-generated
Annotations Creators: crowdsourced
Source Datasets: original
Licenses: apache-2.0
Dataset Preview
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class_id (string)display_name (string)ngram (string)label (sequence)object_group (class label)text (string)template_group (class label)template_idx (int32)
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"All rulers are [MASK]."
1 (text-masked)
0
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"Commonly rulers are [MASK]."
1 (text-masked)
1
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"Everyone knows that most rulers are [MASK]."
1 (text-masked)
2
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"Everyone knows that rulers are [MASK]."
1 (text-masked)
3
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"It is known that most rulers are [MASK]."
1 (text-masked)
4
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"It is known that rulers are [MASK]."
1 (text-masked)
5
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"It's known that most rulers are [MASK]."
1 (text-masked)
6
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"It's known that rulers are [MASK]."
1 (text-masked)
7
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"Most rulers are [MASK]."
1 (text-masked)
8
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"This ruler is [MASK]."
1 (text-masked)
9
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"a jpeg corrupted photo of a ruler."
0 (clip-imagenet)
10
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"a photo of a large ruler."
0 (clip-imagenet)
11
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"a photo of hard to see rulers."
0 (clip-imagenet)
12
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"a photo of the hard to see ruler."
0 (clip-imagenet)
13
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"a pixelated photo of a ruler."
0 (clip-imagenet)
14
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"a pixelated photo of rulers."
0 (clip-imagenet)
15
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"itap of many rulers."
0 (clip-imagenet)
16
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"itap of my ruler."
0 (clip-imagenet)
17
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"itap of some rulers."
0 (clip-imagenet)
18
"/m/0hdln"
"Ruler"
"ruler"
[ 0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352, 0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636, 0.301363647 ]
2 (Any)
"the cartoon ruler."
0 (clip-imagenet)
19
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"All toothbrushes are [MASK]."
1 (text-masked)
0
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"Commonly toothbrushes are [MASK]."
1 (text-masked)
1
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"Everyone knows that most toothbrushes are [MASK]."
1 (text-masked)
2
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"Everyone knows that toothbrushes are [MASK]."
1 (text-masked)
3
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"It is known that most toothbrushes are [MASK]."
1 (text-masked)
4
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"It is known that toothbrushes are [MASK]."
1 (text-masked)
5
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"It's known that most toothbrushes are [MASK]."
1 (text-masked)
6
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"It's known that toothbrushes are [MASK]."
1 (text-masked)
7
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"Most toothbrushes are [MASK]."
1 (text-masked)
8
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"This toothbrush is [MASK]."
1 (text-masked)
9
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"a jpeg corrupted photo of a toothbrush."
0 (clip-imagenet)
10
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"a photo of a large toothbrush."
0 (clip-imagenet)
11
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"a photo of hard to see toothbrushes."
0 (clip-imagenet)
12
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"a photo of the hard to see toothbrush."
0 (clip-imagenet)
13
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"a pixelated photo of a toothbrush."
0 (clip-imagenet)
14
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"a pixelated photo of toothbrushes."
0 (clip-imagenet)
15
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"itap of many toothbrushes."
0 (clip-imagenet)
16
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"itap of my toothbrush."
0 (clip-imagenet)
17
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"itap of some toothbrushes."
0 (clip-imagenet)
18
"/m/012xff"
"Toothbrush"
"toothbrush"
[ 0.0495430492, 0.2109187096, 0.0468975455, 0.0468975455, 0.1090668589, 0.0429292917, 0.1037758514, 0.0429292917, 0.082611829, 0.169913426, 0.0945165977 ]
2 (Any)
"the cartoon toothbrush."
0 (clip-imagenet)
19
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"All jellyfish are [MASK]."
1 (text-masked)
0
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"Commonly jellyfish are [MASK]."
1 (text-masked)
1
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"Everyone knows that most jellyfish are [MASK]."
1 (text-masked)
2
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"Everyone knows that jellyfish are [MASK]."
1 (text-masked)
3
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"It is known that most jellyfish are [MASK]."
1 (text-masked)
4
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"It is known that jellyfish are [MASK]."
1 (text-masked)
5
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"It's known that most jellyfish are [MASK]."
1 (text-masked)
6
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"It's known that jellyfish are [MASK]."
1 (text-masked)
7
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"Most jellyfish are [MASK]."
1 (text-masked)
8
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"This jellyfish is [MASK]."
1 (text-masked)
9
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"a jpeg corrupted photo of a jellyfish."
0 (clip-imagenet)
10
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"a photo of a large jellyfish."
0 (clip-imagenet)
11
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"a photo of hard to see jellyfish."
0 (clip-imagenet)
12
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"a photo of the hard to see jellyfish."
0 (clip-imagenet)
13
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"a pixelated photo of a jellyfish."
0 (clip-imagenet)
14
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"a pixelated photo of jellyfish."
0 (clip-imagenet)
15
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"itap of many jellyfish."
0 (clip-imagenet)
16
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"itap of my jellyfish."
0 (clip-imagenet)
17
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"itap of some jellyfish."
0 (clip-imagenet)
18
"/m/0d8zb"
"Jellyfish"
"jellyfish"
[ 0.0225108229, 0.0772727281, 0.1844155788, 0.0612554103, 0.0225108229, 0.0225108229, 0.2140692621, 0.0766233802, 0.0398268402, 0.2439393997, 0.0350649357 ]
2 (Any)
"the cartoon jellyfish."
0 (clip-imagenet)
19
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"All houses are [MASK]."
1 (text-masked)
0
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"Commonly houses are [MASK]."
1 (text-masked)
1
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"Everyone knows that most houses are [MASK]."
1 (text-masked)
2
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"Everyone knows that houses are [MASK]."
1 (text-masked)
3
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"It is known that most houses are [MASK]."
1 (text-masked)
4
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"It is known that houses are [MASK]."
1 (text-masked)
5
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"It's known that most houses are [MASK]."
1 (text-masked)
6
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"It's known that houses are [MASK]."
1 (text-masked)
7
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"Most houses are [MASK]."
1 (text-masked)
8
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"This house is [MASK]."
1 (text-masked)
9
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"a jpeg corrupted photo of a house."
0 (clip-imagenet)
10
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"a photo of a large house."
0 (clip-imagenet)
11
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"a photo of hard to see houses."
0 (clip-imagenet)
12
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"a photo of the hard to see house."
0 (clip-imagenet)
13
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"a pixelated photo of a house."
0 (clip-imagenet)
14
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"a pixelated photo of houses."
0 (clip-imagenet)
15
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"itap of many houses."
0 (clip-imagenet)
16
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"itap of my house."
0 (clip-imagenet)
17
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"itap of some houses."
0 (clip-imagenet)
18
"/m/03jm5"
"House"
"house"
[ 0.0385154076, 0.0749852583, 0.2322534323, 0.1238574386, 0.0762383938, 0.0073529412, 0.0192577038, 0.0073529412, 0.0339635871, 0.3036819994, 0.0825409144 ]
2 (Any)
"the cartoon house."
0 (clip-imagenet)
19
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"All stationary bicycles are [MASK]."
1 (text-masked)
0
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"Commonly stationary bicycles are [MASK]."
1 (text-masked)
1
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"Everyone knows that most stationary bicycles are [MASK]."
1 (text-masked)
2
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"Everyone knows that stationary bicycles are [MASK]."
1 (text-masked)
3
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"It is known that most stationary bicycles are [MASK]."
1 (text-masked)
4
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"It is known that stationary bicycles are [MASK]."
1 (text-masked)
5
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"It's known that most stationary bicycles are [MASK]."
1 (text-masked)
6
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"It's known that stationary bicycles are [MASK]."
1 (text-masked)
7
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"Most stationary bicycles are [MASK]."
1 (text-masked)
8
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"This stationary bicycle is [MASK]."
1 (text-masked)
9
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"a jpeg corrupted photo of a stationary bicycle."
0 (clip-imagenet)
10
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"a photo of a large stationary bicycle."
0 (clip-imagenet)
11
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"a photo of hard to see stationary bicycles."
0 (clip-imagenet)
12
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"a photo of the hard to see stationary bicycle."
0 (clip-imagenet)
13
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"a pixelated photo of a stationary bicycle."
0 (clip-imagenet)
14
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"a pixelated photo of stationary bicycles."
0 (clip-imagenet)
15
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"itap of many stationary bicycles."
0 (clip-imagenet)
16
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"itap of my stationary bicycle."
0 (clip-imagenet)
17
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"itap of some stationary bicycles."
0 (clip-imagenet)
18
"/m/03kt2w"
"Stationary bicycle"
"stationary bicycle"
[ 0.401052624, 0.0671052635, 0.0335526317, 0.1860526353, 0.0230263155, 0.0230263155, 0.0230263155, 0.0230263155, 0.1235526279, 0.0735526308, 0.0230263155 ]
2 (Any)
"the cartoon stationary bicycle."
0 (clip-imagenet)
19
End of preview (truncated to 100 rows)
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, conversational, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, 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, 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, visual-question-answering, document-question-answering, zero-shot-image-classification, 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, 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-generation, dialogue-modeling, 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, 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.

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