Datasets:

viswadarshan06
/

stsb-binary-paraphrase-labelled

idx int64 1 5.75k	sentence1 stringlengths 16 367	sentence2 stringlengths 15 311	label int64 0 1
1	A plane is taking off.	An air plane is taking off.	1
2	A man is playing a large flute.	A man is playing a flute.	0
3	A man is spreading shreded cheese on a pizza.	A man is spreading shredded cheese on an uncooked pizza.	1
4	Three men are playing chess.	Two men are playing chess.	1
5	A man is playing the cello.	A man seated is playing the cello.	0
6	Some men are fighting.	Two men are fighting.	0
7	A man is smoking.	A man is skating.	0
8	The man is playing the piano.	The man is playing the guitar.	1
9	A man is playing on a guitar and singing.	A woman is playing an acoustic guitar and singing.	0
10	A person is throwing a cat on to the ceiling.	A person throws a cat on the ceiling.	1
11	The man hit the other man with a stick.	The man spanked the other man with a stick.	1
12	A woman picks up and holds a baby kangaroo.	A woman picks up and holds a baby kangaroo in her arms.	1
13	A man is playing a flute.	A man is playing a bamboo flute.	1
14	A person is folding a piece of paper.	Someone is folding a piece of paper.	1
15	A man is running on the road.	A panda dog is running on the road.	0
16	A dog is trying to get bacon off his back.	A dog is trying to eat the bacon on its back.	1
17	The polar bear is sliding on the snow.	A polar bear is sliding across the snow.	1
18	A woman is writing.	A woman is swimming.	0
19	A cat is rubbing against baby's face.	A cat is rubbing against a baby.	1
20	The man is riding a horse.	A man is riding on a horse.	1
21	A man pours oil into a pot.	A man pours wine in a pot.	1
22	A man is playing a guitar.	A girl is playing a guitar.	0
23	A panda is sliding down a slide.	A panda slides down a slide.	1
24	A woman is eating something.	A woman is eating meat.	1
25	A woman peels a potato.	A woman is peeling a potato.	1
26	The boy fell off his bike.	A boy falls off his bike.	1
27	The woman is playing the flute.	A woman is playing a flute.	1
28	A rabbit is running from an eagle.	A hare is running from a eagle.	1
29	The woman is frying a breaded pork chop.	A woman is cooking a breaded pork chop.	1
30	A girl is flying a kite.	A girl running is flying a kite.	0
31	A man is riding a mechanical bull.	A man rode a mechanical bull.	0
32	The man is playing the guitar.	A man is playing a guitar.	1
33	A woman is dancing and singing with other women.	A woman is dancing and singing in the rain.	0
34	A man is slicing a bun.	A man is slicing an onion.	0
35	A man is pouring oil into a pan.	A man is pouring oil into a skillet.	1
36	A lion is playing with people.	A lion is playing with two men.	0
37	A dog rides a skateboard.	A dog is riding a skateboard.	1
38	Someone is carving a statue.	A man is carving a statue.	1
39	A woman is slicing an onion.	A man is cutting an onion.	0
40	A woman peels shrimp.	A woman is peeling shrimp.	1
41	A woman is frying fish.	A woman is cooking fish.	1
42	A woman is playing an electric guitar.	A woman is playing a guitar.	1
43	A baby tiger is playing with a ball.	A baby is playing with a doll.	0
44	A person is slicing a tomato.	A person is slicing some meat.	1
45	A person cuts an onion.	A person is cutting an onion.	1
46	A man is playing the piano.	A woman is playing the violin.	0
47	A woman is playing the flute.	A man is playing the guitar.	0
48	A man is cutting up a potato.	A man is cutting up carrots.	1
49	A kid is playing guitar.	A boy is playing a guitar.	1
50	A boy is playing guitar.	A man is playing a guitar.	1
51	A man is playing guitar.	A boy is playing a guitar.	1
52	A little boy is playing a keyboard.	A boy is playing key board.	1
53	A man is playing a guitar.	A man is playing an electric guitar.	1
54	A dog licks a baby.	A dog is licking a baby.	1
55	A woman is slicing an onion.	A man is cutting and onion.	0
56	A man is playing the guitar.	A man is playing the drums.	1
57	A woman is slicing a pepper.	A woman is cutting a red pepper.	1
58	A man is playing the drums.	A man plays the drum.	0
59	A woman rides a horse.	A woman is riding a horse.	1
60	A man is eating a banana by a tree.	A man is eating a banana.	0
61	A cat is playing a key board.	A man is playing two keyboards.	0
62	A man chops down a tree with an axe.	A man cut a tree with an axe.	1
63	A kid plays with a toy phone.	A little boy plays with a toy phone.	1
64	A man is riding a motorcycle.	A man is riding a horse.	1
65	A man is riding a motorcycle.	A man is riding a horse.	1
66	A squirrel is spinning around in circles.	A squirrel runs around in circles.	1
67	A man and a woman are kissing.	A man and woman kiss.	1
68	A man is getting into a car.	A man is getting into a car in a garage.	1
69	A man is dancing.	A man is talking.	0
70	A man is playing the guitar and singing.	A man is playing the guitar.	0
71	A person is cutting mushrooms.	A person is cutting mushrooms with a knife.	1
72	A tiger cub is making a sound.	A tiger is walking around.	0
73	A person is slicing onions.	A person is peeling an onion.	1
74	A man is playing the piano.	A man is playing the trumpet.	1
75	A woman is peeling a potato.	A woman is peeling an apple.	1
76	A pankda is eating bamboo.	A panda bear is eating some bamboo.	1
77	A person is peeling an onion.	A person is peeling an eggplant.	1
78	A monkey pushes another monkey.	The monkey pushed the other monkey.	1
79	A squirrel runs around in circles.	A squirrel is moving in circles.	1
80	A man is tying his shoe.	A man ties his shoe.	1
81	A boy is singing and playing the piano.	A boy is playing the piano.	1
82	A dog is eating water melon.	A dog is eating a piece of watermelon.	1
83	A woman is chopping broccoli.	A woman is chopping broccoli with a knife.	1
84	A man is peeling a potato.	A man peeled a potatoe.	0
85	A woman is playing a guitar.	A man plays a guitar.	0
86	A woman is slicing tomato.	A man is slicing onion.	0
87	A man swims underwater.	A woman is swimming underwater.	1
88	A man and woman are talking.	A man and woman is eating.	0
89	A small dog is chasing a yoga ball.	A dog is chasing a ball.	0
90	The men are playing cricket.	The men are playing basketball.	1
91	A man rides off on a motorcycle.	A man is riding on a motorcycle.	0
92	A man is playing a guitar.	A man is singing and playing a guitar.	1
93	The man talked on the telephone.	The man is talking on the phone.	1
94	A man is fishing.	A man is exercising.	1
95	A man is levitating.	A man is talking.	0
96	Two boys are driving.	Two bays are dancing.	0
97	A man is riding on a horse.	A girl is riding a horse.	0
98	A man is riding a bicycle.	A monkey is riding a bike.	1
99	A man is slicing potatoes.	A woman is peeling potato.	1
100	A woman is peeling a potato.	A man is slicing potato.	0

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Paraphrase Detection Dataset (Derived from SetFit/stsb)

Description:

This dataset originates from the SetFit/stsb dataset, which was initially created for semantic textual similarity (STS) tasks with a label range of 0 to 5. It has been adapted for binary paraphrase detection by leveraging the high-accuracy paraphrase classification model viswadarshan06/pd-robert.

Each sentence pair in the original dataset has been re-labeled according to the following binary scheme:

1 → Paraphrase: The two sentences convey the same meaning.
0 → Not Paraphrase: The two sentences have different meanings.

This binary labeling makes the dataset directly applicable for paraphrase detection tasks within Natural Language Processing (NLP). It is particularly useful for:

Training paraphrase detection models.
Evaluating the performance of paraphrase detection models.
Facilitating transfer learning for binary classification tasks related to sentence similarity.

Dataset Features:

The dataset contains the following features for each instance:

sentence1: The first sentence in English.
sentence2: The second sentence in English.
label: A binary label indicating whether the two sentences are paraphrases:
- 1: The sentences are paraphrases.
- 0: The sentences are not paraphrases.

Use Cases:

This dataset is suitable for a variety of NLP applications, including:

Paraphrase detection model training: Training new models to accurately identify paraphrases.
Sentence similarity tasks: Evaluating how well models can determine if two sentences have similar meanings.
Fine-tuning binary classification models: Adapting pre-trained binary classification models for the specific task of paraphrase detection.

Downloads last month: 29

Size of downloaded dataset files:

1.09 MB

Size of the auto-converted Parquet files:

762 kB

Number of rows:

8,628