Dataset Preview
Go to dataset viewer
sentence_pair_id (int64)premise (string)hypothesis (string)relatedness_score (float32)entailment_judgment (class label)
1
"A group of kids is playing in a yard and an old man is standing in the background"
"A group of boys in a yard is playing and a man is standing in the background"
4.5
0 (NEUTRAL)
2
"A group of children is playing in the house and there is no man standing in the background"
"A group of kids is playing in a yard and an old man is standing in the background"
3.2
0 (NEUTRAL)
3
"The young boys are playing outdoors and the man is smiling nearby"
"The kids are playing outdoors near a man with a smile"
4.7
1 (ENTAILMENT)
5
"The kids are playing outdoors near a man with a smile"
"A group of kids is playing in a yard and an old man is standing in the background"
3.4
0 (NEUTRAL)
9
"The young boys are playing outdoors and the man is smiling nearby"
"A group of kids is playing in a yard and an old man is standing in the background"
3.7
0 (NEUTRAL)
12
"Two dogs are fighting"
"Two dogs are wrestling and hugging"
4
0 (NEUTRAL)
14
"A brown dog is attacking another animal in front of the man in pants"
"Two dogs are fighting"
3.5
0 (NEUTRAL)
18
"A brown dog is attacking another animal in front of the man in pants"
"Two dogs are wrestling and hugging"
3.2
0 (NEUTRAL)
25
"Nobody is riding the bicycle on one wheel"
"A person in a black jacket is doing tricks on a motorbike"
2.8
0 (NEUTRAL)
26
"A person is riding the bicycle on one wheel"
"A man in a black jacket is doing tricks on a motorbike"
3.7
0 (NEUTRAL)
28
"A person on a black motorbike is doing tricks with a jacket"
"A person is riding the bicycle on one wheel"
3.4
0 (NEUTRAL)
30
"A man with a jersey is dunking the ball at a basketball game"
"The ball is being dunked by a man with a jersey at a basketball game"
4.9
1 (ENTAILMENT)
35
"A man with a jersey is dunking the ball at a basketball game"
"A man who is playing dunks the basketball into the net and a crowd is in background"
3.6
0 (NEUTRAL)
40
"The player is dunking the basketball into the net and a crowd is in background"
"A man with a jersey is dunking the ball at a basketball game"
3.8
0 (NEUTRAL)
42
"Two people are kickboxing and spectators are not watching"
"Two people are kickboxing and spectators are watching"
3.4
2 (CONTRADICTION)
44
"Two young women are sparring in a kickboxing fight"
"Two women are sparring in a kickboxing match"
4.9
1 (ENTAILMENT)
45
"Two young women are not sparring in a kickboxing fight"
"Two women are sparring in a kickboxing match"
3.9
0 (NEUTRAL)
47
"Two people are kickboxing and spectators are watching"
"Two young women are not sparring in a kickboxing fight"
3.415
0 (NEUTRAL)
49
"Two women are sparring in a kickboxing match"
"Two people are kickboxing and spectators are not watching"
3.7
0 (NEUTRAL)
55
"Three boys are jumping in the leaves"
"Three kids are jumping in the leaves"
4.4
1 (ENTAILMENT)
56
"Three kids are sitting in the leaves"
"Three kids are jumping in the leaves"
3.8
0 (NEUTRAL)
58
"Children in red shirts are playing in the leaves"
"Three kids are sitting in the leaves"
3.5
0 (NEUTRAL)
62
"Children in red shirts are playing in the leaves"
"Three kids are jumping in the leaves"
4
0 (NEUTRAL)
65
"Two angels are making snow on the lying children"
"Two children are lying in the snow and are making snow angels"
2.9
0 (NEUTRAL)
70
"Two children are lying in the snow and are drawing angels"
"Two people in snowsuits are lying in the snow and making snow angels"
4.1
0 (NEUTRAL)
72
"Two people in snowsuits are lying in the snow and making snow angels"
"Two angels are making snow on the lying children"
2.5
0 (NEUTRAL)
73
"Two children are lying in the snow and are making snow angels"
"Two people in snowsuits are lying in the snow and making snow angels"
4.2
0 (NEUTRAL)
77
"People wearing costumes are gathering in a forest and are looking in the same direction"
"Masked people are looking in the same direction in a forest"
4.4
1 (ENTAILMENT)
78
"People wearing costumes are gathering in a forest and are looking in the same direction"
"People wearing costumes are scattering in a forest and are looking in different directions"
3.2
0 (NEUTRAL)
79
"People are looking at some costumes gathered in the vicinity of the forest"
"People wearing costumes are gathering in a forest and are looking in the same direction"
3.635
0 (NEUTRAL)
81
"A little girl is looking at a woman in costume"
"People wearing costumes are scattering in a forest and are looking in different directions"
2.4
0 (NEUTRAL)
82
"A little girl is looking at a woman in costume"
"People are looking at some costumes gathered in the vicinity of the forest"
2.6
0 (NEUTRAL)
83
"A young girl is looking at a woman in costume"
"People wearing costumes are gathering in a forest and are looking in the same direction"
2.2
0 (NEUTRAL)
84
"People wearing costumes are gathering in a forest and are looking in the same direction"
"The little girl is looking at a man in costume"
3
0 (NEUTRAL)
85
"People wearing costumes are gathering in a forest and are looking in the same direction"
"A little girl in costume looks like a woman"
2
0 (NEUTRAL)
87
"A lone biker is jumping in the air"
"A biker is jumping in the air, alone"
5
1 (ENTAILMENT)
88
"There is no biker jumping in the air"
"A lone biker is jumping in the air"
4.2
2 (CONTRADICTION)
90
"A man is jumping into an empty pool"
"A man is jumping into a full pool"
3
2 (CONTRADICTION)
91
"A man is jumping into an empty pool"
"The man's jumper is in the empty pool"
3.1
0 (NEUTRAL)
93
"A lone biker is jumping in the air"
"A man is jumping into a full pool"
1.7
0 (NEUTRAL)
94
"The man's jumper is in the empty pool"
"A lone biker is jumping in the air"
1.4
0 (NEUTRAL)
97
"A lone biker is jumping in the air"
"A man is jumping into an empty pool"
1.5
0 (NEUTRAL)
98
"Four kids are doing backbends in the park"
"Four children are doing backbends in the park"
4.8
1 (ENTAILMENT)
99
"Four children are doing backbends in the gym"
"Four children are doing backbends in the park"
3.8
0 (NEUTRAL)
100
"Four girls are doing backbends and playing in the garden"
"Four girls are doing backbends and playing outdoors"
4.1
1 (ENTAILMENT)
107
"A man who is playing is running with the ball in his hands"
"A player is running with the ball"
4.3
1 (ENTAILMENT)
112
"Two groups of people are playing football"
"A player is running with the ball"
2.1
0 (NEUTRAL)
113
"Two teams are competing in a baseball game"
"A player is running with the ball"
3
0 (NEUTRAL)
117
"A player is running with the ball"
"Two teams are competing in a football match"
2.6
0 (NEUTRAL)
120
"Five wooden stands are in front of each child's hut"
"Five children are standing in front of a wooden hut"
3.2
0 (NEUTRAL)
122
"Five kids are standing close together and one kid has a gun"
"Five kids are standing close together and none of the kids has a gun"
3.7
2 (CONTRADICTION)
124
"Five kids are standing close together and none of the kids has a gun"
"Five children are standing in front of a wooden hut"
2.6
0 (NEUTRAL)
126
"Five children are standing in a wooden hut"
"Five kids are standing close together and one kid has a gun"
2.7
0 (NEUTRAL)
127
"Five wooden stands are in front of each child's hut"
"Five kids are standing close together and one kid has a gun"
2.3
0 (NEUTRAL)
129
"An old man is sitting in a field"
"A man is sitting in a field"
4.4
1 (ENTAILMENT)
130
"A man is sitting in a field"
"A man is running in a field"
2.6
0 (NEUTRAL)
131
"A person is wearing a hat and is sitting on the grass"
"A person is sitting in a field and is wearing a hat"
4.1
0 (NEUTRAL)
133
"A person is sitting and wearing a grass hat"
"A person is wearing a hat and is sitting on the grass"
3.4
0 (NEUTRAL)
136
"A person is sitting and wearing a grass hat"
"A man is sitting in a field"
3.3
0 (NEUTRAL)
139
"A man is sitting in a field"
"A person is wearing a hat and is sitting on the grass"
3.8
0 (NEUTRAL)
140
"The current is being ridden by a group of friends in a raft"
"A group of friends are riding the current in a raft"
4.9
1 (ENTAILMENT)
141
"A group of friends are riding the current in a raft"
"A group is not riding the current in a raft"
3.7
2 (CONTRADICTION)
146
"The current is being ridden by a group of friends in a raft"
"This group of people is practicing water safety and wearing preservers"
3.2
0 (NEUTRAL)
148
"A group of friends are riding the current in a raft"
"This group of people is practicing water safety and wearing preservers"
3.1
0 (NEUTRAL)
150
"A deer is jumping over a fence"
"A deer isn't jumping over the fence"
3.9
2 (CONTRADICTION)
159
"People are walking inside a building that has many murals on it"
"People are walking outside a building that has many murals on it"
3.4
0 (NEUTRAL)
161
"Several people are in front of a colorful building"
"Nobody is in front of the colorful building"
3.5
2 (CONTRADICTION)
163
"People are walking outside a building that has many murals on it"
"Nobody is in front of the colorful building"
3.6
0 (NEUTRAL)
164
"People are walking outside the building that has several murals on it"
"Several people are in front of a colorful building"
3.6
1 (ENTAILMENT)
170
"A family is watching a little boy who is hitting a baseball"
"A family is watching a little boy who is missing a baseball"
3.9
0 (NEUTRAL)
173
"A child is hitting a baseball"
"A family is watching a little boy who is missing a baseball"
3.015
0 (NEUTRAL)
176
"A child is missing a baseball"
"A family is watching a little boy who is hitting a baseball"
2.7
0 (NEUTRAL)
183
"A purple crowd of people is eating on various red lit restaurant tables"
"Various people are eating at red tables in a crowded restaurant with purple lights"
3.3
0 (NEUTRAL)
184
"A large group of Asian people is eating at a restaurant"
"Various customers are eating in a crowded restaurant with purple lights"
2.9
0 (NEUTRAL)
186
"A purple crowd of people is eating on various red lit restaurant tables"
"A large group of Asian people is eating at a restaurant"
3.1
0 (NEUTRAL)
188
"Various people are eating at red tables in a crowded restaurant with purple lights"
"A small group of people is waiting to eat in a restaurant"
3.2
0 (NEUTRAL)
192
"A motorcycle rider is standing up on the seat of a white motorcycle"
"No motorcycle rider is standing up on the seat of a motorcycle"
3.8
2 (CONTRADICTION)
194
"Nobody is on a motorcycle and is standing on the seat"
"Someone is on a black and white motorcycle and is standing on the seat"
3.7
2 (CONTRADICTION)
200
"A motorcyclist is riding a motorbike dangerously along a roadway"
"A motorcyclist is riding a motorbike along a roadway"
4.6
1 (ENTAILMENT)
201
"There is no motorcyclist riding a motorbike along a roadway"
"A motorcyclist is riding a motorbike along a roadway"
3.7
2 (CONTRADICTION)
202
"A man with a helmet painted red is riding a blue motorcycle down the road"
"A motorcyclist with a red helmet is riding a blue motorcycle down the road"
4.8
1 (ENTAILMENT)
205
"A motorcyclist without a helmet is waiting on a blue motorcycle near the road"
"A motorcyclist is riding a motorbike along a roadway"
3.3
0 (NEUTRAL)
215
"Two dogs are playing by a tree"
"A dog is catching a stick in the air and another is watching"
3.7
0 (NEUTRAL)
216
"Two dogs are playing by a tree"
"There is no dog leaping in the air"
2.7
0 (NEUTRAL)
217
"Two dogs are playing by a tree"
"A dog is leaping high in the air and another is watching"
3
0 (NEUTRAL)
223
"A girl in white is dancing"
"The dancer is dancing in front of the sound equipment"
4
0 (NEUTRAL)
224
"A girl in white is dancing"
"The blond girl is dancing behind the sound equipment"
3.3
0 (NEUTRAL)
226
"A girl is wearing white clothes and is dancing"
"The blond girl is dancing in front of the sound equipment"
3.5
0 (NEUTRAL)
227
"The blond girl is dancing in front of the sound equipment"
"There is no girl in white dancing"
3.3
0 (NEUTRAL)
232
"Three Asian kids are dancing and a man is looking"
"Three Asian kids are dancing and there is no man looking"
3.9
2 (CONTRADICTION)
233
"Three Asian kids are dancing and a man is looking"
"An Asian man is dancing and three kids are looking"
3.7
0 (NEUTRAL)
234
"The children of a family are playing and waiting"
"Three Asian kids are dancing and a serious man is looking"
1.9
0 (NEUTRAL)
237
"The children of a family are patiently playing and waiting"
"Three Asian kids are dancing and a man is looking"
2.3
0 (NEUTRAL)
238
"There are no children playing and waiting"
"Three Asian kids are dancing and a man is looking"
1.6
0 (NEUTRAL)
240
"A woman is wearing an Egyptian hat on her head"
"A woman is wearing an Egyptian headdress"
4.3
1 (ENTAILMENT)
241
"A woman is wearing an Egyptian headdress"
"A woman is wearing an Indian headdress"
4
0 (NEUTRAL)
248
"The black woman is wearing glasses over the headdress"
"A woman is wearing an Egyptian headdress"
3.6
0 (NEUTRAL)
249
"A woman is wearing an Egyptian hat on her head"
"The woman is wearing glasses and a black headdress"
2.5
0 (NEUTRAL)
254
"A hiker is on top of the mountain and is dancing"
"There is no hiker dancing on top of the mountain"
3.2
2 (CONTRADICTION)
256
"There is no man on a rock high above some trees standing in a strange position"
"A man is on a rock high above some trees and is standing in a strange position"
4.3
2 (CONTRADICTION)
End of preview (truncated to 100 rows)

Dataset Card for SemEval 2014 - Task 1

Dataset Summary

[More Information Needed]

Supported Tasks and Leaderboards

[More Information Needed]

Languages

[More Information Needed]

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

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

[More Information Needed]

Citation Information

[More Information Needed]

Contributions

Thanks to @ashmeet13 for adding this dataset.

Edit dataset card
Evaluate models HF Leaderboard