The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code: DatasetGenerationError
Exception: ValueError
Message: Not able to read records in the JSON file at /tmp/hf-datasets-cache/heavy/datasets/10966634163068-config-parquet-and-info-NLPFin-Quantitative101-e0514113/downloads/29dbcf51e842322f7c4592f0943f967f1397791be91649efa99a565f7772bebc.
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1995, in _prepare_split_single
for _, table in generator:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 165, in _generate_tables
raise ValueError(f"Not able to read records in the JSON file at {file}.") from None
ValueError: Not able to read records in the JSON file at /tmp/hf-datasets-cache/heavy/datasets/10966634163068-config-parquet-and-info-NLPFin-Quantitative101-e0514113/downloads/29dbcf51e842322f7c4592f0943f967f1397791be91649efa99a565f7772bebc.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1529, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1154, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
statement2
string | statement1_char
string | EQUATE
string | statement1
string | statement2_char
string | statement2_mask
string | answer
string | statement2_sci_10E_char
string | options
string | statement1_sci_10E
string | type
string | statement1_sci_10E_char
string | statement1_mask
string | statement2_sci_10E
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Raul Flores , daughter , 9 , shot dead ; wire calls 911 | '' Someone just came in and shot my daughter and husband , '' Flores ' wife frantically told 9 1 1 . | NewsNLI | '' Someone just came in and shot my daughter and husband , '' Flores ' wife frantically told 911 . | Raul Flores , daughter , 9 , shot dead ; wire calls 9 1 1 | Raul Flores , daughter , [Num] , shot dead ; wire calls [Num]11 | neutral | Raul Flores , daughter , 9 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 , shot dead ; wire calls 9 . 0 0 0 0 0 0 0 0 0 0 E + 0 011 | Entailment or neutral? | '' Someone just came in and shot my daughter and husband , '' Flores ' wife frantically told 9.1100000000E+02 . | Type_7 | '' Someone just came in and shot my daughter and husband , '' Flores ' wife frantically told 9 . 1 1 0 0 0 0 0 0 0 0 E + 0 2 . | '' Someone just came in and shot my daughter and husband , '' Flores ' wife frantically told [Num] . | Raul Flores , daughter , 9.0000000000E+00 , shot dead ; wire calls 9.0000000000E+0011 |
Know something ? Call 641-228-182 . A reward is offered | A $ 5 , 0 0 0 reward is offered . | NewsNLI | A $ 5,000 reward is offered . | Know something ? Call 6 4 1- 2 2 8- 1 8 2 . A reward is offered | Know something ? Call [Num][Num][Num] . A reward is offered | neutral | Know something ? Call 6 . 4 1 0 0 0 0 0 0 0 0 E + 0 2- 2 . 2 8 0 0 0 0 0 0 0 0 E + 0 2- 1 . 8 2 0 0 0 0 0 0 0 0 E + 0 2 . A reward is offered | Entailment or neutral? | A $ 5.0000000000E+03 reward is offered . | Type_7 | A $ 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 3 reward is offered . | A $ [Num] reward is offered . | Know something ? Call 6.4100000000E+02-2.2800000000E+02-1.8200000000E+02 . A reward is offered |
At Veridux Corporation , there are less than 650 employees | At Veridux Corporation , there are 2 5 0 employees | StressTest | At Veridux Corporation , there are 250 employees | At Veridux Corporation , there are less than 6 5 0 employees | At Veridux Corporation , there are less than [Num] employees | Entailment | At Veridux Corporation , there are less than 6 . 5 0 0 0 0 0 0 0 0 0 E + 0 2 employees | Entailment or contradiction or neutral? | At Veridux Corporation , there are 2.5000000000E+02 employees | Type_7 | At Veridux Corporation , there are 2 . 5 0 0 0 0 0 0 0 0 0 E + 0 2 employees | At Veridux Corporation , there are [Num] employees | At Veridux Corporation , there are less than 6.5000000000E+02 employees |
The ratio between the number of sheep and the number of horses at the Stewart farm is more than 2 to 7 , If each horse is fed 230 ounces of horse food per day and the farm needs a total 12,880 ounces of horse food per day , what is the number of sheep in the farm ? | The ratio between the number of sheep and the number of horses at the Stewart farm is 2 to 7 , If each horse is fed 2 3 0 ounces of horse food per day and the farm needs a total 1 2 , 8 8 0 ounces of horse food per day , what is the number of sheep in the farm ? | StressTest | The ratio between the number of sheep and the number of horses at the Stewart farm is 2 to 7 , If each horse is fed 230 ounces of horse food per day and the farm needs a total 12,880 ounces of horse food per day , what is the number of sheep in the farm ? | The ratio between the number of sheep and the number of horses at the Stewart farm is more than 2 to 7 , If each horse is fed 2 3 0 ounces of horse food per day and the farm needs a total 1 2 , 8 8 0 ounces of horse food per day , what is the number of sheep in the farm ? | The ratio between the number of sheep and the number of horses at the Stewart farm is more than [Num] to [Num] , If each horse is fed [Num]30 ounces of horse food per day and the farm needs a total 1[Num],880 ounces of horse food per day , what is the number of sheep in the farm ? | contradiction | The ratio between the number of sheep and the number of horses at the Stewart farm is more than 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 to 7 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 , If each horse is fed 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 030 ounces of horse food per day and the farm needs a total 12 . 0 0 0 0 0 0 0 0 0 0 E + 0 0,880 ounces of horse food per day , what is the number of sheep in the farm ? | Entailment or contradiction or neutral? | The ratio between the number of sheep and the number of horses at the Stewart farm is 2.0000000000E+00 to 7.0000000000E+00 , If each horse is fed 2.0000000000E+0030 ounces of horse food per day and the farm needs a total 12.0000000000E+00,880 ounces of horse food per day , what is the number of sheep in the farm ? | Type_7 | The ratio between the number of sheep and the number of horses at the Stewart farm is 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 to 7 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 , If each horse is fed 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 030 ounces of horse food per day and the farm needs a total 12 . 0 0 0 0 0 0 0 0 0 0 E + 0 0,880 ounces of horse food per day , what is the number of sheep in the farm ? | The ratio between the number of sheep and the number of horses at the Stewart farm is [Num] to [Num] , If each horse is fed [Num]30 ounces of horse food per day and the farm needs a total 1[Num],880 ounces of horse food per day , what is the number of sheep in the farm ? | The ratio between the number of sheep and the number of horses at the Stewart farm is more than 2.0000000000E+00 to 7.0000000000E+00 , If each horse is fed 2.0000000000E+0030 ounces of horse food per day and the farm needs a total 12.0000000000E+00,880 ounces of horse food per day , what is the number of sheep in the farm ? |
Over 1.2 million people have viewed it on YouTube | More than 1 . 2 million people have since viewed it . | NewsNLI | More than 1.2 million people have since viewed it . | Over 1 . 2 million people have viewed it on YouTube | Over [Num] million people have viewed it on YouTube | Entailment | Over 1 . 2 0 0 0 0 0 0 0 0 0 E + 0 0 million people have viewed it on YouTube | Entailment or neutral? | More than 1.2000000000E+00 million people have since viewed it . | Type_7 | More than 1 . 2 0 0 0 0 0 0 0 0 0 E + 0 0 million people have since viewed it . | More than [Num] million people have since viewed it . | Over 1.2000000000E+00 million people have viewed it on YouTube |
Joe drives 120 miles at 60 miles per hour , and then he drives the next 120 miles at 40 miles per hour | Joe drives 4 2 0 miles at 6 0 miles per hour , and then he drives the next 1 2 0 miles at 4 0 miles per hour | StressTest | Joe drives 420 miles at 60 miles per hour , and then he drives the next 120 miles at 40 miles per hour | Joe drives 1 2 0 miles at 6 0 miles per hour , and then he drives the next 1 2 0 miles at 4 0 miles per hour | Joe drives [Num] miles at [Num] miles per hour , and then he drives the next [Num] miles at [Num] miles per hour | contradiction | Joe drives 1 . 2 0 0 0 0 0 0 0 0 0 E + 0 2 miles at 6 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 miles per hour , and then he drives the next 1 . 2 0 0 0 0 0 0 0 0 0 E + 0 2 miles at 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 miles per hour | Entailment or contradiction or neutral? | Joe drives 4.2000000000E+02 miles at 6.0000000000E+01 miles per hour , and then he drives the next 1.2000000000E+02 miles at 4.0000000000E+01 miles per hour | Type_7 | Joe drives 4 . 2 0 0 0 0 0 0 0 0 0 E + 0 2 miles at 6 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 miles per hour , and then he drives the next 1 . 2 0 0 0 0 0 0 0 0 0 E + 0 2 miles at 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 miles per hour | Joe drives [Num] miles at [Num] miles per hour , and then he drives the next [Num] miles at [Num] miles per hour | Joe drives 1.2000000000E+02 miles at 6.0000000000E+01 miles per hour , and then he drives the next 1.2000000000E+02 miles at 4.0000000000E+01 miles per hour |
How many seconds does Sandy take to cover a distance of more than 200 meters , if Sandy runs at a speed of 18 km / hr ? | How many seconds does Sandy take to cover a distance of 7 0 0 meters , if Sandy runs at a speed of 1 8 km / hr ? | StressTest | How many seconds does Sandy take to cover a distance of 700 meters , if Sandy runs at a speed of 18 km / hr ? | How many seconds does Sandy take to cover a distance of more than 2 0 0 meters , if Sandy runs at a speed of 1 8 km / hr ? | How many seconds does Sandy take to cover a distance of more than [Num] meters , if Sandy runs at a speed of [Num] km / hr ? | Entailment | How many seconds does Sandy take to cover a distance of more than 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 2 meters , if Sandy runs at a speed of 1 . 8 0 0 0 0 0 0 0 0 0 E + 0 1 km / hr ? | Entailment or contradiction or neutral? | How many seconds does Sandy take to cover a distance of 7.0000000000E+02 meters , if Sandy runs at a speed of 1.8000000000E+01 km / hr ? | Type_7 | How many seconds does Sandy take to cover a distance of 7 . 0 0 0 0 0 0 0 0 0 0 E + 0 2 meters , if Sandy runs at a speed of 1 . 8 0 0 0 0 0 0 0 0 0 E + 0 1 km / hr ? | How many seconds does Sandy take to cover a distance of [Num] meters , if Sandy runs at a speed of [Num] km / hr ? | How many seconds does Sandy take to cover a distance of more than 2.0000000000E+02 meters , if Sandy runs at a speed of 1.8000000000E+01 km / hr ? |
By how much must Rebecca ' s annual income increase so that it constitutes less than 55 % of Rebecca and Jimmy ' s combined income ? | By how much must Rebecca ' s annual income increase so that it constitutes 5 5 % of Rebecca and Jimmy ' s combined income ? | StressTest | By how much must Rebecca ' s annual income increase so that it constitutes 55 % of Rebecca and Jimmy ' s combined income ? | By how much must Rebecca ' s annual income increase so that it constitutes less than 5 5 % of Rebecca and Jimmy ' s combined income ? | By how much must Rebecca ' s annual income increase so that it constitutes less than [Num] % of Rebecca and Jimmy ' s combined income ? | contradiction | By how much must Rebecca ' s annual income increase so that it constitutes less than 5 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 % of Rebecca and Jimmy ' s combined income ? | Entailment or contradiction or neutral? | By how much must Rebecca ' s annual income increase so that it constitutes 5.5000000000E+01 % of Rebecca and Jimmy ' s combined income ? | Type_7 | By how much must Rebecca ' s annual income increase so that it constitutes 5 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 % of Rebecca and Jimmy ' s combined income ? | By how much must Rebecca ' s annual income increase so that it constitutes [Num] % of Rebecca and Jimmy ' s combined income ? | By how much must Rebecca ' s annual income increase so that it constitutes less than 5.5000000000E+01 % of Rebecca and Jimmy ' s combined income ? |
16.0 more geese were in the marsh | There were 5 8 . 0 geese and 3 7 . 0 ducks in the marsh. | AWPNLI | There were 58.0 geese and 37.0 ducks in the marsh. | 1 6 . 0 more geese were in the marsh | [Num] more geese were in the marsh | contradiction | 1 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 more geese were in the marsh | Entailment or contradiction? | There were 5.8000000000E+01 geese and 3.7000000000E+01 ducks in the marsh. | Type_7 | There were 5 . 8 0 0 0 0 0 0 0 0 0 E + 0 1 geese and 3 . 7 0 0 0 0 0 0 0 0 0 E + 0 1 ducks in the marsh. | There were [Num] geese and [Num] ducks in the marsh. | 1.6000000000E+01 more geese were in the marsh |
Vince Camuto has died at the age of 78 | -LRB- CNN -RRB- Vince Camuto , the iconic women 's footwear designer and co-founder of Nine West , has died . | NewsNLI | -LRB- CNN -RRB- Vince Camuto , the iconic women 's footwear designer and co-founder of Nine West , has died . | Vince Camuto has died at the age of 7 8 | Vince Camuto has died at the age of [Num] | neutral | Vince Camuto has died at the age of 7 . 8 0 0 0 0 0 0 0 0 0 E + 0 1 | Entailment or neutral? | -LRB- CNN -RRB- Vince Camuto , the iconic women 's footwear designer and co-founder of Nine West , has died . | Type_7 | -LRB- CNN -RRB- Vince Camuto , the iconic women 's footwear designer and co-founder of Nine West , has died . | -LRB- CNN -RRB- Vince Camuto , the iconic women 's footwear designer and co-founder of Nine West , has died . | Vince Camuto has died at the age of 7.8000000000E+01 |
Jake can dig a well in less than 66 days | Jake can dig a well in 1 6 days | StressTest | Jake can dig a well in 16 days | Jake can dig a well in less than 6 6 days | Jake can dig a well in less than [Num] days | Entailment | Jake can dig a well in less than 6 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 days | Entailment or contradiction or neutral? | Jake can dig a well in 1.6000000000E+01 days | Type_7 | Jake can dig a well in 1 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 days | Jake can dig a well in [Num] days | Jake can dig a well in less than 6.6000000000E+01 days |
The product of Diana ' s age , in years , and a third of Rashid ' s age , in years , is 32 | The product of Diana ' s age , in years , and a third of Rashid ' s age , in years , is less than 7 2 | StressTest | The product of Diana ' s age , in years , and a third of Rashid ' s age , in years , is less than 72 | The product of Diana ' s age , in years , and a third of Rashid ' s age , in years , is 3 2 | The product of Diana ' s age , in years , and a third of Rashid ' s age , in years , is [Num] | neutral | The product of Diana ' s age , in years , and a third of Rashid ' s age , in years , is 3 . 2 0 0 0 0 0 0 0 0 0 E + 0 1 | Entailment or contradiction or neutral? | The product of Diana ' s age , in years , and a third of Rashid ' s age , in years , is less than 7.2000000000E+01 | Type_7 | The product of Diana ' s age , in years , and a third of Rashid ' s age , in years , is less than 7 . 2 0 0 0 0 0 0 0 0 0 E + 0 1 | The product of Diana ' s age , in years , and a third of Rashid ' s age , in years , is less than [Num] | The product of Diana ' s age , in years , and a third of Rashid ' s age , in years , is 3.2000000000E+01 |
Victor has less than 15 cups if flour , 16 cups of sugar and 8 cups of milk | Victor has 1 5 cups if flour , 1 6 cups of sugar and 8 cups of milk | StressTest | Victor has 15 cups if flour , 16 cups of sugar and 8 cups of milk | Victor has less than 1 5 cups if flour , 1 6 cups of sugar and 8 cups of milk | Victor has less than [Num] cups if flour , [Num] cups of sugar and [Num] cups of milk | contradiction | Victor has less than 1 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 cups if flour , 1 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 cups of sugar and 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 cups of milk | Entailment or contradiction or neutral? | Victor has 1.5000000000E+01 cups if flour , 1.6000000000E+01 cups of sugar and 8.0000000000E+00 cups of milk | Type_7 | Victor has 1 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 cups if flour , 1 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 cups of sugar and 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 cups of milk | Victor has [Num] cups if flour , [Num] cups of sugar and [Num] cups of milk | Victor has less than 1.5000000000E+01 cups if flour , 1.6000000000E+01 cups of sugar and 8.0000000000E+00 cups of milk |
Mr Yadav spends 10 % of his monthly salary on consumable items and 50 % of the remaining on clothes and transport | Mr Yadav spends 6 0 % of his monthly salary on consumable items and 5 0 % of the remaining on clothes and transport | StressTest | Mr Yadav spends 60 % of his monthly salary on consumable items and 50 % of the remaining on clothes and transport | Mr Yadav spends 1 0 % of his monthly salary on consumable items and 5 0 % of the remaining on clothes and transport | Mr Yadav spends [Num] % of his monthly salary on consumable items and [Num] % of the remaining on clothes and transport | contradiction | Mr Yadav spends 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 % of his monthly salary on consumable items and 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 % of the remaining on clothes and transport | Entailment or contradiction or neutral? | Mr Yadav spends 6.0000000000E+01 % of his monthly salary on consumable items and 5.0000000000E+01 % of the remaining on clothes and transport | Type_7 | Mr Yadav spends 6 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 % of his monthly salary on consumable items and 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 % of the remaining on clothes and transport | Mr Yadav spends [Num] % of his monthly salary on consumable items and [Num] % of the remaining on clothes and transport | Mr Yadav spends 1.0000000000E+01 % of his monthly salary on consumable items and 5.0000000000E+01 % of the remaining on clothes and transport |
Exactly 3 / 7 of the ponies have horseshoes , and exactly 2 / 3 of the ponies with horseshoes are from Iceland | Exactly 5 / 7 of the ponies have horseshoes , and exactly 2 / 3 of the ponies with horseshoes are from Iceland | StressTest | Exactly 5 / 7 of the ponies have horseshoes , and exactly 2 / 3 of the ponies with horseshoes are from Iceland | Exactly 3 / 7 of the ponies have horseshoes , and exactly 2 / 3 of the ponies with horseshoes are from Iceland | Exactly [Num] / [Num] of the ponies have horseshoes , and exactly [Num] / [Num] of the ponies with horseshoes are from Iceland | contradiction | Exactly 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 / 7 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 of the ponies have horseshoes , and exactly 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 / 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 of the ponies with horseshoes are from Iceland | Entailment or contradiction or neutral? | Exactly 5.0000000000E+00 / 7.0000000000E+00 of the ponies have horseshoes , and exactly 2.0000000000E+00 / 3.0000000000E+00 of the ponies with horseshoes are from Iceland | Type_7 | Exactly 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 / 7 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 of the ponies have horseshoes , and exactly 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 / 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 of the ponies with horseshoes are from Iceland | Exactly [Num] / [Num] of the ponies have horseshoes , and exactly [Num] / [Num] of the ponies with horseshoes are from Iceland | Exactly 3.0000000000E+00.0000000000E+00 / 7.0000000000E+00 of the ponies have horseshoes , and exactly 2.0000000000E+00 / 3.0000000000E+00.0000000000E+00 of the ponies with horseshoes are from Iceland |
How many hours does it take Little Texas Drilling Company to produce 700 barrels of oil ? | How many hours does it take Little Texas Drilling Company to produce 1 0 0 barrels of oil ? | StressTest | How many hours does it take Little Texas Drilling Company to produce 100 barrels of oil ? | How many hours does it take Little Texas Drilling Company to produce 7 0 0 barrels of oil ? | How many hours does it take Little Texas Drilling Company to produce [Num] barrels of oil ? | contradiction | How many hours does it take Little Texas Drilling Company to produce 7 . 0 0 0 0 0 0 0 0 0 0 E + 0 2 barrels of oil ? | Entailment or contradiction or neutral? | How many hours does it take Little Texas Drilling Company to produce 1.0000000000E+02 barrels of oil ? | Type_7 | How many hours does it take Little Texas Drilling Company to produce 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 2 barrels of oil ? | How many hours does it take Little Texas Drilling Company to produce [Num] barrels of oil ? | How many hours does it take Little Texas Drilling Company to produce 7.0000000000E+02 barrels of oil ? |
The NATO has 16 members . | The 1 6 NATO members and the 1 4 countries which used to form the rival Warsaw Pact agreed that there would be significantly less equipment permitted in the area of application in Europe than there was under the original treaty . | RTE_Quant | The 16 NATO members and the 14 countries which used to form the rival Warsaw Pact agreed that there would be significantly less equipment permitted in the area of application in Europe than there was under the original treaty . | The NATO has 1 6 members . | The NATO has [Num] members . | Entailment | The NATO has 1 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 members . | Entailment or neutral? | The 1.6000000000E+01 NATO members and the 1.4000000000E+01 countries which used to form the rival Warsaw Pact agreed that there would be significantly less equipment permitted in the area of application in Europe than there was under the original treaty . | Type_7 | The 1 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 NATO members and the 1 . 4 0 0 0 0 0 0 0 0 0 E + 0 1 countries which used to form the rival Warsaw Pact agreed that there would be significantly less equipment permitted in the area of application in Europe than there was under the original treaty . | The [Num] NATO members and the [Num] countries which used to form the rival Warsaw Pact agreed that there would be significantly less equipment permitted in the area of application in Europe than there was under the original treaty . | The NATO has 1.6000000000E+01 members . |
If Mary received $ more than 500 more than Harry did , what was the profit made by their business in that year ? | If Mary received $ 8 0 0 more than Harry did , what was the profit made by their business in that year ? | StressTest | If Mary received $ 800 more than Harry did , what was the profit made by their business in that year ? | If Mary received $ more than 5 0 0 more than Harry did , what was the profit made by their business in that year ? | If Mary received $ more than [Num] more than Harry did , what was the profit made by their business in that year ? | Entailment | If Mary received $ more than 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 2 more than Harry did , what was the profit made by their business in that year ? | Entailment or contradiction or neutral? | If Mary received $ 8.0000000000E+02 more than Harry did , what was the profit made by their business in that year ? | Type_7 | If Mary received $ 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 2 more than Harry did , what was the profit made by their business in that year ? | If Mary received $ [Num] more than Harry did , what was the profit made by their business in that year ? | If Mary received $ more than 5.0000000000E+02 more than Harry did , what was the profit made by their business in that year ? |
Jasmine has a toaster with two slots that toasts one side of each piece of bread at a time , and it takes one minute to do so . If she wants to make more than 3 pieces of toast , what is the least amount of time she needs to toast them on both sides ? | Jasmine has a toaster with two slots that toasts one side of each piece of bread at a time , and it takes one minute to do so .
If she wants to make 3 pieces of toast , what is the least amount of time she needs to toast them on both sides ? | StressTest | Jasmine has a toaster with two slots that toasts one side of each piece of bread at a time , and it takes one minute to do so .
If she wants to make 3 pieces of toast , what is the least amount of time she needs to toast them on both sides ? | Jasmine has a toaster with two slots that toasts one side of each piece of bread at a time , and it takes one minute to do so . If she wants to make more than 3 pieces of toast , what is the least amount of time she needs to toast them on both sides ? | Jasmine has a toaster with two slots that toasts one side of each piece of bread at a time , and it takes one minute to do so . If she wants to make more than [Num] pieces of toast , what is the least amount of time she needs to toast them on both sides ? | contradiction | Jasmine has a toaster with two slots that toasts one side of each piece of bread at a time , and it takes one minute to do so . If she wants to make more than 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 pieces of toast , what is the least amount of time she needs to toast them on both sides ? | Entailment or contradiction or neutral? | Jasmine has a toaster with two slots that toasts one side of each piece of bread at a time , and it takes one minute to do so .
If she wants to make 3.0000000000E+00 pieces of toast , what is the least amount of time she needs to toast them on both sides ? | Type_7 | Jasmine has a toaster with two slots that toasts one side of each piece of bread at a time , and it takes one minute to do so .
If she wants to make 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 pieces of toast , what is the least amount of time she needs to toast them on both sides ? | Jasmine has a toaster with two slots that toasts one side of each piece of bread at a time , and it takes one minute to do so .
If she wants to make [Num] pieces of toast , what is the least amount of time she needs to toast them on both sides ? | Jasmine has a toaster with two slots that toasts one side of each piece of bread at a time , and it takes one minute to do so . If she wants to make more than 3.0000000000E+00 pieces of toast , what is the least amount of time she needs to toast them on both sides ? |
Mary can do a piece of work in less than 38 days | Mary can do a piece of work in 2 8 days | StressTest | Mary can do a piece of work in 28 days | Mary can do a piece of work in less than 3 8 days | Mary can do a piece of work in less than [Num] days | Entailment | Mary can do a piece of work in less than 3 . 8 0 0 0 0 0 0 0 0 0 E + 0 1 days | Entailment or contradiction or neutral? | Mary can do a piece of work in 2.8000000000E+01 days | Type_7 | Mary can do a piece of work in 2 . 8 0 0 0 0 0 0 0 0 0 E + 0 1 days | Mary can do a piece of work in [Num] days | Mary can do a piece of work in less than 3.8000000000E+01 days |
In the city of San Durango , more than 40 people own cats , dogs , or rabbits | In the city of San Durango , 6 0 people own cats , dogs , or rabbits | StressTest | In the city of San Durango , 60 people own cats , dogs , or rabbits | In the city of San Durango , more than 4 0 people own cats , dogs , or rabbits | In the city of San Durango , more than [Num] people own cats , dogs , or rabbits | Entailment | In the city of San Durango , more than 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 people own cats , dogs , or rabbits | Entailment or contradiction or neutral? | In the city of San Durango , 6.0000000000E+01 people own cats , dogs , or rabbits | Type_7 | In the city of San Durango , 6 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 people own cats , dogs , or rabbits | In the city of San Durango , [Num] people own cats , dogs , or rabbits | In the city of San Durango , more than 4.0000000000E+01 people own cats , dogs , or rabbits |
Bilking a large number of people out of millions of dollars can lead to sentences that vary from ten to twenty years . | Prosecutions tended to be more aggressive and sentences tended to be longer for financial crimes with many victims and massive losses of money , as in the Minkow case . | RTE_Quant | Prosecutions tended to be more aggressive and sentences tended to be longer for financial crimes with many victims and massive losses of money , as in the Minkow case . | Bilking a large number of people out of millions of dollars can lead to sentences that vary from ten to twenty years . | Bilking a large number of people out of millions of dollars can lead to sentences that vary from ten to twenty years . | neutral | Bilking a large number of people out of millions of dollars can lead to sentences that vary from ten to twenty years . | Entailment or neutral? | Prosecutions tended to be more aggressive and sentences tended to be longer for financial crimes with many victims and massive losses of money , as in the Minkow case . | Type_7 | Prosecutions tended to be more aggressive and sentences tended to be longer for financial crimes with many victims and massive losses of money , as in the Minkow case . | Prosecutions tended to be more aggressive and sentences tended to be longer for financial crimes with many victims and massive losses of money , as in the Minkow case . | Bilking a large number of people out of millions of dollars can lead to sentences that vary from ten to twenty years . |
Fred and Sam are standing 50 miles apart and they start walking in a straight line toward each other at the same time | Fred and Sam are standing less than 7 0 miles apart and they start walking in a straight line toward each other at the same time | StressTest | Fred and Sam are standing less than 70 miles apart and they start walking in a straight line toward each other at the same time | Fred and Sam are standing 5 0 miles apart and they start walking in a straight line toward each other at the same time | Fred and Sam are standing [Num] miles apart and they start walking in a straight line toward each other at the same time | neutral | Fred and Sam are standing 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 miles apart and they start walking in a straight line toward each other at the same time | Entailment or contradiction or neutral? | Fred and Sam are standing less than 7.0000000000E+01 miles apart and they start walking in a straight line toward each other at the same time | Type_7 | Fred and Sam are standing less than 7 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 miles apart and they start walking in a straight line toward each other at the same time | Fred and Sam are standing less than [Num] miles apart and they start walking in a straight line toward each other at the same time | Fred and Sam are standing 5.0000000000E+01 miles apart and they start walking in a straight line toward each other at the same time |
Joan has 5.0 orange balloons now | Joan has 8 . 0 orange balloons and her friend gives her 2 . 0 more | AWPNLI | Joan has 8.0 orange balloons and her friend gives her 2.0 more | Joan has 5 . 0 orange balloons now | Joan has [Num] orange balloons now | contradiction | Joan has 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 orange balloons now | Entailment or contradiction? | Joan has 8.0000000000E+00 orange balloons and her friend gives her 2.0000000000E+00 more | Type_7 | Joan has 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 orange balloons and her friend gives her 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 more | Joan has [Num] orange balloons and her friend gives her [Num] more | Joan has 5.0000000000E+00 orange balloons now |
A shoe store sells Adidas shoes for $ 10 each and Puma shoes for $ 50 each | A shoe store sells Adidas shoes for $ 6 0 each and Puma shoes for $ 5 0 each | StressTest | A shoe store sells Adidas shoes for $ 60 each and Puma shoes for $ 50 each | A shoe store sells Adidas shoes for $ 1 0 each and Puma shoes for $ 5 0 each | A shoe store sells Adidas shoes for $ [Num] each and Puma shoes for $ [Num] each | contradiction | A shoe store sells Adidas shoes for $ 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 each and Puma shoes for $ 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 each | Entailment or contradiction or neutral? | A shoe store sells Adidas shoes for $ 6.0000000000E+01 each and Puma shoes for $ 5.0000000000E+01 each | Type_7 | A shoe store sells Adidas shoes for $ 6 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 each and Puma shoes for $ 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 each | A shoe store sells Adidas shoes for $ [Num] each and Puma shoes for $ [Num] each | A shoe store sells Adidas shoes for $ 1.0000000000E+01 each and Puma shoes for $ 5.0000000000E+01 each |
Lali builds a tower using only red , green , and blue toy bricks in a ratio of 4 : 3 : 1 | Lali builds a tower using only red , green , and blue toy bricks in a ratio of less than 7 : 3 : 1 | StressTest | Lali builds a tower using only red , green , and blue toy bricks in a ratio of less than 7 : 3 : 1 | Lali builds a tower using only red , green , and blue toy bricks in a ratio of 4 : 3 : 1 | Lali builds a tower using only red , green , and blue toy bricks in a ratio of [Num] : [Num] : [Num] | neutral | Lali builds a tower using only red , green , and blue toy bricks in a ratio of 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 : 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 : 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 | Entailment or contradiction or neutral? | Lali builds a tower using only red , green , and blue toy bricks in a ratio of less than 7.0000000000E+00 : 3.0000000000E+00 : 1.0000000000E+00 | Type_7 | Lali builds a tower using only red , green , and blue toy bricks in a ratio of less than 7 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 : 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 : 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 | Lali builds a tower using only red , green , and blue toy bricks in a ratio of less than [Num] : [Num] : [Num] | Lali builds a tower using only red , green , and blue toy bricks in a ratio of 4.0000000000E+00 : 3.0000000000E+00 : 1.0000000000E+00 |
If the boys at Jones Elementary make up 20 % of the total school population of x students , what is x ? | If the boys at Jones Elementary make up less than 6 0 % of the total school population of x students , what is x ? | StressTest | If the boys at Jones Elementary make up less than 60 % of the total school population of x students , what is x ? | If the boys at Jones Elementary make up 2 0 % of the total school population of x students , what is x ? | If the boys at Jones Elementary make up [Num] % of the total school population of x students , what is x ? | neutral | If the boys at Jones Elementary make up 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 % of the total school population of x students , what is x ? | Entailment or contradiction or neutral? | If the boys at Jones Elementary make up less than 6.0000000000E+01 % of the total school population of x students , what is x ? | Type_7 | If the boys at Jones Elementary make up less than 6 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 % of the total school population of x students , what is x ? | If the boys at Jones Elementary make up less than [Num] % of the total school population of x students , what is x ? | If the boys at Jones Elementary make up 2.0000000000E+01 % of the total school population of x students , what is x ? |
There are 44 stations between Ernakulam and Chennai | There are 2 4 stations between Ernakulam and Chennai | StressTest | There are 24 stations between Ernakulam and Chennai | There are 4 4 stations between Ernakulam and Chennai | There are [Num] stations between Ernakulam and Chennai | contradiction | There are 4 . 4 0 0 0 0 0 0 0 0 0 E + 0 1 stations between Ernakulam and Chennai | Entailment or contradiction or neutral? | There are 2.4000000000E+01 stations between Ernakulam and Chennai | Type_7 | There are 2 . 4 0 0 0 0 0 0 0 0 0 E + 0 1 stations between Ernakulam and Chennai | There are [Num] stations between Ernakulam and Chennai | There are 4.4000000000E+01 stations between Ernakulam and Chennai |
Dacid obtained 76 , 65 , 82 , 67 and 85 marks ( out of 100 ) in English , Mathematics , Physics , Chemistry and Biology | Dacid obtained more than 3 6 , 6 5 , 8 2 , 6 7 and 8 5 marks ( out of 1 0 0 ) in English , Mathematics , Physics , Chemistry and Biology | StressTest | Dacid obtained more than 36 , 65 , 82 , 67 and 85 marks ( out of 100 ) in English , Mathematics , Physics , Chemistry and Biology | Dacid obtained 7 6 , 6 5 , 8 2 , 6 7 and 8 5 marks ( out of 1 0 0 ) in English , Mathematics , Physics , Chemistry and Biology | Dacid obtained [Num] , [Num] , [Num] , [Num] and [Num] marks ( out of [Num] ) in English , Mathematics , Physics , Chemistry and Biology | neutral | Dacid obtained 7 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 , 6 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 , 8 . 2 0 0 0 0 0 0 0 0 0 E + 0 1 , 6 . 7 0 0 0 0 0 0 0 0 0 E + 0 1 and 8 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 marks ( out of 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 2 ) in English , Mathematics , Physics , Chemistry and Biology | Entailment or contradiction or neutral? | Dacid obtained more than 3.6000000000E+01 , 6.5000000000E+01 , 8.2000000000E+01 , 6.7000000000E+01 and 8.5000000000E+01 marks ( out of 1.0000000000E+02 ) in English , Mathematics , Physics , Chemistry and Biology | Type_7 | Dacid obtained more than 3 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 , 6 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 , 8 . 2 0 0 0 0 0 0 0 0 0 E + 0 1 , 6 . 7 0 0 0 0 0 0 0 0 0 E + 0 1 and 8 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 marks ( out of 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 2 ) in English , Mathematics , Physics , Chemistry and Biology | Dacid obtained more than [Num] , [Num] , [Num] , [Num] and [Num] marks ( out of [Num] ) in English , Mathematics , Physics , Chemistry and Biology | Dacid obtained 7.6000000000E+01 , 6.5000000000E+01 , 8.2000000000E+01 , 6.7000000000E+01 and 8.5000000000E+01 marks ( out of 1.0000000000E+02 ) in English , Mathematics , Physics , Chemistry and Biology |
NHAI employs 400 men to build a highway of 2 km in 50 days working 8 hours a day | NHAI employs 1 0 0 men to build a highway of 2 km in 5 0 days working 8 hours a day | StressTest | NHAI employs 100 men to build a highway of 2 km in 50 days working 8 hours a day | NHAI employs 4 0 0 men to build a highway of 2 km in 5 0 days working 8 hours a day | NHAI employs [Num] men to build a highway of [Num] km in [Num] days working [Num] hours a day | contradiction | NHAI employs 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 men to build a highway of 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 km in 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 days working 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 hours a day | Entailment or contradiction or neutral? | NHAI employs 1.0000000000E+02.0000000000E+00 men to build a highway of 2.0000000000E+00 km in 5.0000000000E+01 days working 8.0000000000E+00 hours a day | Type_7 | NHAI employs 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 men to build a highway of 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 km in 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 days working 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 hours a day | NHAI employs [Num] men to build a highway of [Num] km in [Num] days working [Num] hours a day | NHAI employs 4.0000000000E+02.0000000000E+00 men to build a highway of 2.0000000000E+00 km in 5.0000000000E+01 days working 8.0000000000E+00 hours a day |
more than 1620 in 8 % stock , Michael earns Rs | 1 6 2 0 in 8 % stock , Michael earns Rs | StressTest | 1620 in 8 % stock , Michael earns Rs | more than 1 6 2 0 in 8 % stock , Michael earns Rs | more than [Num] in [Num] % stock , Michael earns Rs | contradiction | more than 1 . 6 2 0 0 0 0 0 0 0 0 E + 0 3 in 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 % stock , Michael earns Rs | Entailment or contradiction or neutral? | 1.6200000000E+03 in 8.0000000000E+00 % stock , Michael earns Rs | Type_7 | 1 . 6 2 0 0 0 0 0 0 0 0 E + 0 3 in 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 % stock , Michael earns Rs | [Num] in [Num] % stock , Michael earns Rs | more than 1.6200000000E+03 in 8.0000000000E+00 % stock , Michael earns Rs |
Media report 16 deaths linked to the outbreak , including 1 in Sweden | News reports citing local authorities reported 1 6 deaths linked to E. coli in some raw vegetables . | NewsNLI | News reports citing local authorities reported 16 deaths linked to E. coli in some raw vegetables . | Media report 1 6 deaths linked to the outbreak , including 1 in Sweden | Media report [Num] deaths linked to the outbreak , including [Num] in Sweden | neutral | Media report 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 deaths linked to the outbreak , including 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 in Sweden | Entailment or neutral? | News reports citing local authorities reported 1.6000000000E+01 deaths linked to E. coli in some raw vegetables . | Type_7 | News reports citing local authorities reported 1 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 deaths linked to E. coli in some raw vegetables . | News reports citing local authorities reported [Num] deaths linked to E. coli in some raw vegetables . | Media report 1.0000000000E+00.6000000000E+01.0000000000E+00 deaths linked to the outbreak , including 1.0000000000E+00 in Sweden |
If Sanoop returned less than 3 t - shirts to the retailer , and the average price of the remaining t - shirts was Rs | If Sanoop returned 2 t - shirts to the retailer , and the average price of the remaining t - shirts was Rs | StressTest | If Sanoop returned 2 t - shirts to the retailer , and the average price of the remaining t - shirts was Rs | If Sanoop returned less than 3 t - shirts to the retailer , and the average price of the remaining t - shirts was Rs | If Sanoop returned less than [Num] t - shirts to the retailer , and the average price of the remaining t - shirts was Rs | Entailment | If Sanoop returned less than 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 t - shirts to the retailer , and the average price of the remaining t - shirts was Rs | Entailment or contradiction or neutral? | If Sanoop returned 2.0000000000E+00 t - shirts to the retailer , and the average price of the remaining t - shirts was Rs | Type_7 | If Sanoop returned 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 t - shirts to the retailer , and the average price of the remaining t - shirts was Rs | If Sanoop returned [Num] t - shirts to the retailer , and the average price of the remaining t - shirts was Rs | If Sanoop returned less than 3.0000000000E+00 t - shirts to the retailer , and the average price of the remaining t - shirts was Rs |
Fred and Sam are standing 20 miles apart and they start walking in a straight line toward each other at the same time | Fred and Sam are standing 4 0 miles apart and they start walking in a straight line toward each other at the same time | StressTest | Fred and Sam are standing 40 miles apart and they start walking in a straight line toward each other at the same time | Fred and Sam are standing 2 0 miles apart and they start walking in a straight line toward each other at the same time | Fred and Sam are standing [Num] miles apart and they start walking in a straight line toward each other at the same time | contradiction | Fred and Sam are standing 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 miles apart and they start walking in a straight line toward each other at the same time | Entailment or contradiction or neutral? | Fred and Sam are standing 4.0000000000E+01 miles apart and they start walking in a straight line toward each other at the same time | Type_7 | Fred and Sam are standing 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 miles apart and they start walking in a straight line toward each other at the same time | Fred and Sam are standing [Num] miles apart and they start walking in a straight line toward each other at the same time | Fred and Sam are standing 2.0000000000E+01 miles apart and they start walking in a straight line toward each other at the same time |
The Labor Department said this sector has added nearly 1 of every 5 of the new jobs created during the last 12 months . | Employers created 1 4 4 , 0 0 0 new payroll jobs in August as the unemployment rate dipped to 5 . 4 percent , a modest improvement over the 5 . 5 percent jobless rate in July , the Labor Department reported Friday . | RTE_Quant | Employers created 144,000 new payroll jobs in August as the unemployment rate dipped to 5.4 percent , a modest improvement over the 5.5 percent jobless rate in July , the Labor Department reported Friday . | The Labor Department said this sector has added nearly 1 of every 5 of the new jobs created during the last 1 2 months . | The Labor Department said this sector has added nearly [Num] of every [Num] of the new jobs created during the last [Num]2 months . | neutral | The Labor Department said this sector has added nearly 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 of every 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 of the new jobs created during the last 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 02 months . | Entailment or neutral? | Employers created 1.4400000000E+05 new payroll jobs in August as the unemployment rate dipped to 5.4000000000E+00 percent , a modest improvement over the 5.5000000000E+00 percent jobless rate in July , the Labor Department reported Friday . | Type_7 | Employers created 1 . 4 4 0 0 0 0 0 0 0 0 E + 0 5 new payroll jobs in August as the unemployment rate dipped to 5 . 4 0 0 0 0 0 0 0 0 0 E + 0 0 percent , a modest improvement over the 5 . 5 0 0 0 0 0 0 0 0 0 E + 0 0 percent jobless rate in July , the Labor Department reported Friday . | Employers created [Num] new payroll jobs in August as the unemployment rate dipped to [Num] percent , a modest improvement over the [Num] percent jobless rate in July , the Labor Department reported Friday . | The Labor Department said this sector has added nearly 1.0000000000E+00 of every 5.0000000000E+00 of the new jobs created during the last 1.0000000000E+002 months . |
2.0 cakes are left | A restaurant baked 5 . 0 cakes during lunch and sold 6 . 0 during dinner today and the restaurant baked 3 . 0 cakes yesterday | AWPNLI | A restaurant baked 5.0 cakes during lunch and sold 6.0 during dinner today and the restaurant baked 3.0 cakes yesterday | 2 . 0 cakes are left | [Num] cakes are left | Entailment | 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 cakes are left | Entailment or contradiction? | A restaurant baked 5.0000000000E+00 cakes during lunch and sold 6.0000000000E+00 during dinner today and the restaurant baked 3.0000000000E+00 cakes yesterday | Type_7 | A restaurant baked 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 cakes during lunch and sold 6 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 during dinner today and the restaurant baked 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 cakes yesterday | A restaurant baked [Num] cakes during lunch and sold [Num] during dinner today and the restaurant baked [Num] cakes yesterday | 2.0000000000E+00 cakes are left |
What will Sally have to sell each item for to generate a 15 % profit ? | What will Sally have to sell each item for to generate a less than 8 5 % profit ? | StressTest | What will Sally have to sell each item for to generate a less than 85 % profit ? | What will Sally have to sell each item for to generate a 1 5 % profit ? | What will Sally have to sell each item for to generate a [Num] % profit ? | neutral | What will Sally have to sell each item for to generate a 1 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 % profit ? | Entailment or contradiction or neutral? | What will Sally have to sell each item for to generate a less than 8.5000000000E+01 % profit ? | Type_7 | What will Sally have to sell each item for to generate a less than 8 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 % profit ? | What will Sally have to sell each item for to generate a less than [Num] % profit ? | What will Sally have to sell each item for to generate a 1.5000000000E+01 % profit ? |
Accardo composed 24 Caprices . | In 1 9 5 6 Accardo won the Geneva Competition and in 1 9 5 8 became the first prize winner of the Paganini Competition in Genoa . He has recorded Paganini 's famous 2 4 Caprices ( re-recorded in 1 9 9 9 ) for solo violin and was the first to record all six of the Paganini Violin Concertos . | RTE_Quant | In 1956 Accardo won the Geneva Competition and in 1958 became the first prize winner of the Paganini Competition in Genoa . He has recorded Paganini 's famous 24 Caprices ( re-recorded in 1999 ) for solo violin and was the first to record all six of the Paganini Violin Concertos . | Accardo composed 2 4 Caprices . | Accardo composed [Num] Caprices . | neutral | Accardo composed 2 . 4 0 0 0 0 0 0 0 0 0 E + 0 1 Caprices . | Entailment or neutral? | In 1.9560000000E+03 Accardo won the Geneva Competition and in 1.9580000000E+03 became the first prize winner of the Paganini Competition in Genoa . He has recorded Paganini 's famous 2.4000000000E+01 Caprices ( re-recorded in 1.9990000000E+03 ) for solo violin and was the first to record all six of the Paganini Violin Concertos . | Type_7 | In 1 . 9 5 6 0 0 0 0 0 0 0 E + 0 3 Accardo won the Geneva Competition and in 1 . 9 5 8 0 0 0 0 0 0 0 E + 0 3 became the first prize winner of the Paganini Competition in Genoa . He has recorded Paganini 's famous 2 . 4 0 0 0 0 0 0 0 0 0 E + 0 1 Caprices ( re-recorded in 1 . 9 9 9 0 0 0 0 0 0 0 E + 0 3 ) for solo violin and was the first to record all six of the Paganini Violin Concertos . | In [Num] Accardo won the Geneva Competition and in [Num] became the first prize winner of the Paganini Competition in Genoa . He has recorded Paganini 's famous [Num] Caprices ( re-recorded in [Num] ) for solo violin and was the first to record all six of the Paganini Violin Concertos . | Accardo composed 2.4000000000E+01 Caprices . |
If Bill needs to walk from the corner of 2 nd Rd and 3 rd Ave to the corner of 8 th Rd and 7 th Ave in the shortest possible time , how many different routes could he take ? | If Bill needs to walk from the corner of less than 3 nd Rd and 3 rd Ave to the corner of 8 th Rd and 7 th Ave in the shortest possible time , how many different routes could he take ? | StressTest | If Bill needs to walk from the corner of less than 3 nd Rd and 3 rd Ave to the corner of 8 th Rd and 7 th Ave in the shortest possible time , how many different routes could he take ? | If Bill needs to walk from the corner of 2 nd Rd and 3 rd Ave to the corner of 8 th Rd and 7 th Ave in the shortest possible time , how many different routes could he take ? | If Bill needs to walk from the corner of [Num] nd Rd and [Num] rd Ave to the corner of [Num] th Rd and [Num] th Ave in the shortest possible time , how many different routes could he take ? | neutral | If Bill needs to walk from the corner of 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 nd Rd and 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 rd Ave to the corner of 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 th Rd and 7 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 th Ave in the shortest possible time , how many different routes could he take ? | Entailment or contradiction or neutral? | If Bill needs to walk from the corner of less than 3.0000000000E+00.0000000000E+00 nd Rd and 3.0000000000E+00.0000000000E+00 rd Ave to the corner of 8.0000000000E+00 th Rd and 7.0000000000E+00 th Ave in the shortest possible time , how many different routes could he take ? | Type_7 | If Bill needs to walk from the corner of less than 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 nd Rd and 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 rd Ave to the corner of 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 th Rd and 7 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 th Ave in the shortest possible time , how many different routes could he take ? | If Bill needs to walk from the corner of less than [Num] nd Rd and [Num] rd Ave to the corner of [Num] th Rd and [Num] th Ave in the shortest possible time , how many different routes could he take ? | If Bill needs to walk from the corner of 2.0000000000E+00 nd Rd and 3.0000000000E+00 rd Ave to the corner of 8.0000000000E+00 th Rd and 7.0000000000E+00 th Ave in the shortest possible time , how many different routes could he take ? |
50 Cent served food to New Yorkers affected by Superstorm Sandy | The event provided meals to more than 1 , 0 0 0 New Yorkers impacted by Superstorm Sandy and was a collaborative effort between Food Bank for New York City , Feeding America , the Food Network , the Cooking Channel and Southern Wine and Spirits of America . | NewsNLI | The event provided meals to more than 1,000 New Yorkers impacted by Superstorm Sandy and was a collaborative effort between Food Bank for New York City , Feeding America , the Food Network , the Cooking Channel and Southern Wine and Spirits of America . | 5 0 Cent served food to New Yorkers affected by Superstorm Sandy | [Num] Cent served food to New Yorkers affected by Superstorm Sandy | neutral | 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 Cent served food to New Yorkers affected by Superstorm Sandy | Entailment or neutral? | The event provided meals to more than 1.0000000000E+03 New Yorkers impacted by Superstorm Sandy and was a collaborative effort between Food Bank for New York City , Feeding America , the Food Network , the Cooking Channel and Southern Wine and Spirits of America . | Type_7 | The event provided meals to more than 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 3 New Yorkers impacted by Superstorm Sandy and was a collaborative effort between Food Bank for New York City , Feeding America , the Food Network , the Cooking Channel and Southern Wine and Spirits of America . | The event provided meals to more than [Num] New Yorkers impacted by Superstorm Sandy and was a collaborative effort between Food Bank for New York City , Feeding America , the Food Network , the Cooking Channel and Southern Wine and Spirits of America . | 5.0000000000E+01 Cent served food to New Yorkers affected by Superstorm Sandy |
In an exam , Amar scored 64 percent , Bhavan scored 36 percent and Chetan 44 percent | In an exam , Amar scored less than 7 4 percent , Bhavan scored 3 6 percent and Chetan 4 4 percent | StressTest | In an exam , Amar scored less than 74 percent , Bhavan scored 36 percent and Chetan 44 percent | In an exam , Amar scored 6 4 percent , Bhavan scored 3 6 percent and Chetan 4 4 percent | In an exam , Amar scored [Num] percent , Bhavan scored [Num] percent and Chetan [Num] percent | neutral | In an exam , Amar scored 6 . 4 0 0 0 0 0 0 0 0 0 E + 0 1 percent , Bhavan scored 3 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 percent and Chetan 4 . 4 0 0 0 0 0 0 0 0 0 E + 0 1 percent | Entailment or contradiction or neutral? | In an exam , Amar scored less than 7.4000000000E+01 percent , Bhavan scored 3.6000000000E+01 percent and Chetan 4.4000000000E+01 percent | Type_7 | In an exam , Amar scored less than 7 . 4 0 0 0 0 0 0 0 0 0 E + 0 1 percent , Bhavan scored 3 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 percent and Chetan 4 . 4 0 0 0 0 0 0 0 0 0 E + 0 1 percent | In an exam , Amar scored less than [Num] percent , Bhavan scored [Num] percent and Chetan [Num] percent | In an exam , Amar scored 6.4000000000E+01 percent , Bhavan scored 3.6000000000E+01 percent and Chetan 4.4000000000E+01 percent |
The meeting between the two nations is the first since the flareup | The meeting marks the first between Kim and a top official from China since the flareup between the two Koreas . | NewsNLI | The meeting marks the first between Kim and a top official from China since the flareup between the two Koreas . | The meeting between the two nations is the first since the flareup | The meeting between the two nations is the first since the flareup | Entailment | The meeting between the two nations is the first since the flareup | Entailment or neutral? | The meeting marks the first between Kim and a top official from China since the flareup between the two Koreas . | Type_7 | The meeting marks the first between Kim and a top official from China since the flareup between the two Koreas . | The meeting marks the first between Kim and a top official from China since the flareup between the two Koreas . | The meeting between the two nations is the first since the flareup |
If the average speed of the whole journey was 36 mph , then what is Tom ' s speed driving from B to C in miles per hour ? | If the average speed of the whole journey was more than 1 6 mph , then what is Tom ' s speed driving from B to C in miles per hour ? | StressTest | If the average speed of the whole journey was more than 16 mph , then what is Tom ' s speed driving from B to C in miles per hour ? | If the average speed of the whole journey was 3 6 mph , then what is Tom ' s speed driving from B to C in miles per hour ? | If the average speed of the whole journey was [Num] mph , then what is Tom ' s speed driving from B to C in miles per hour ? | neutral | If the average speed of the whole journey was 3 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 mph , then what is Tom ' s speed driving from B to C in miles per hour ? | Entailment or contradiction or neutral? | If the average speed of the whole journey was more than 1.6000000000E+01 mph , then what is Tom ' s speed driving from B to C in miles per hour ? | Type_7 | If the average speed of the whole journey was more than 1 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 mph , then what is Tom ' s speed driving from B to C in miles per hour ? | If the average speed of the whole journey was more than [Num] mph , then what is Tom ' s speed driving from B to C in miles per hour ? | If the average speed of the whole journey was 3.6000000000E+01 mph , then what is Tom ' s speed driving from B to C in miles per hour ? |
If twice the age of Sunil is more than Syam ' s age by more than 2 years , what is Sunil ' s age ? | If twice the age of Sunil is more than Syam ' s age by 4 years , what is Sunil ' s age ? | StressTest | If twice the age of Sunil is more than Syam ' s age by 4 years , what is Sunil ' s age ? | If twice the age of Sunil is more than Syam ' s age by more than 2 years , what is Sunil ' s age ? | If twice the age of Sunil is more than Syam ' s age by more than [Num] years , what is Sunil ' s age ? | Entailment | If twice the age of Sunil is more than Syam ' s age by more than 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 years , what is Sunil ' s age ? | Entailment or contradiction or neutral? | If twice the age of Sunil is more than Syam ' s age by 4.0000000000E+00 years , what is Sunil ' s age ? | Type_7 | If twice the age of Sunil is more than Syam ' s age by 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 years , what is Sunil ' s age ? | If twice the age of Sunil is more than Syam ' s age by [Num] years , what is Sunil ' s age ? | If twice the age of Sunil is more than Syam ' s age by more than 2.0000000000E+00 years , what is Sunil ' s age ? |
Since you ' re nice , you give John more than 7 baseball cards | Since you ' re nice , you give John 7 baseball cards | StressTest | Since you ' re nice , you give John 7 baseball cards | Since you ' re nice , you give John more than 7 baseball cards | Since you ' re nice , you give John more than [Num] baseball cards | contradiction | Since you ' re nice , you give John more than 7 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 baseball cards | Entailment or contradiction or neutral? | Since you ' re nice , you give John 7.0000000000E+00 baseball cards | Type_7 | Since you ' re nice , you give John 7 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 baseball cards | Since you ' re nice , you give John [Num] baseball cards | Since you ' re nice , you give John more than 7.0000000000E+00 baseball cards |
Jill works as a waitress at the local diner where she earns an hourly wage of $ 4.00 per hour and a standard tip rate of 85 % of the cost of the orders she serves | Jill works as a waitress at the local diner where she earns an hourly wage of $ 4 . 0 0 per hour and a standard tip rate of 1 5 % of the cost of the orders she serves | StressTest | Jill works as a waitress at the local diner where she earns an hourly wage of $ 4.00 per hour and a standard tip rate of 15 % of the cost of the orders she serves | Jill works as a waitress at the local diner where she earns an hourly wage of $ 4 . 0 0 per hour and a standard tip rate of 8 5 % of the cost of the orders she serves | Jill works as a waitress at the local diner where she earns an hourly wage of $ [Num] per hour and a standard tip rate of [Num] % of the cost of the orders she serves | contradiction | Jill works as a waitress at the local diner where she earns an hourly wage of $ 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 per hour and a standard tip rate of 8 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 % of the cost of the orders she serves | Entailment or contradiction or neutral? | Jill works as a waitress at the local diner where she earns an hourly wage of $ 4.0000000000E+00 per hour and a standard tip rate of 1.5000000000E+01 % of the cost of the orders she serves | Type_7 | Jill works as a waitress at the local diner where she earns an hourly wage of $ 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 per hour and a standard tip rate of 1 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 % of the cost of the orders she serves | Jill works as a waitress at the local diner where she earns an hourly wage of $ [Num] per hour and a standard tip rate of [Num] % of the cost of the orders she serves | Jill works as a waitress at the local diner where she earns an hourly wage of $ 4.0000000000E+00 per hour and a standard tip rate of 8.5000000000E+01 % of the cost of the orders she serves |
The average marks scored by Ganesh in English , Science , Mathematics and History is less than 18 from that scored by him in English , History , Geography and Mathematics | The average marks scored by Ganesh in English , Science , Mathematics and History is less than less than 8 8 from that scored by him in English , History , Geography and Mathematics | StressTest | The average marks scored by Ganesh in English , Science , Mathematics and History is less than less than 88 from that scored by him in English , History , Geography and Mathematics | The average marks scored by Ganesh in English , Science , Mathematics and History is less than 1 8 from that scored by him in English , History , Geography and Mathematics | The average marks scored by Ganesh in English , Science , Mathematics and History is less than [Num] from that scored by him in English , History , Geography and Mathematics | neutral | The average marks scored by Ganesh in English , Science , Mathematics and History is less than 1 . 8 0 0 0 0 0 0 0 0 0 E + 0 1 from that scored by him in English , History , Geography and Mathematics | Entailment or contradiction or neutral? | The average marks scored by Ganesh in English , Science , Mathematics and History is less than less than 8.8000000000E+01 from that scored by him in English , History , Geography and Mathematics | Type_7 | The average marks scored by Ganesh in English , Science , Mathematics and History is less than less than 8 . 8 0 0 0 0 0 0 0 0 0 E + 0 1 from that scored by him in English , History , Geography and Mathematics | The average marks scored by Ganesh in English , Science , Mathematics and History is less than less than [Num] from that scored by him in English , History , Geography and Mathematics | The average marks scored by Ganesh in English , Science , Mathematics and History is less than 1.8000000000E+01 from that scored by him in English , History , Geography and Mathematics |
He said investigators found a map that included 34 locations marked as targets in McQuilliams ' possessions . | Investigators found a map in his possessions that included 3 4 locations marked as targets | NewsNLI | Investigators found a map in his possessions that included 34 locations marked as targets | He said investigators found a map that included 3 4 locations marked as targets in McQuilliams ' possessions . | He said investigators found a map that included [Num] locations marked as targets in McQuilliams ' possessions . | Entailment | He said investigators found a map that included 3 . 4 0 0 0 0 0 0 0 0 0 E + 0 1 locations marked as targets in McQuilliams ' possessions . | Entailment or neutral? | Investigators found a map in his possessions that included 3.4000000000E+01 locations marked as targets | Type_7 | Investigators found a map in his possessions that included 3 . 4 0 0 0 0 0 0 0 0 0 E + 0 1 locations marked as targets | Investigators found a map in his possessions that included [Num] locations marked as targets | He said investigators found a map that included 3.4000000000E+01 locations marked as targets in McQuilliams ' possessions . |
A train leaves Delhi at more than 8 a | A train leaves Delhi at 9 a | StressTest | A train leaves Delhi at 9 a | A train leaves Delhi at more than 8 a | A train leaves Delhi at more than [Num] a | Entailment | A train leaves Delhi at more than 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 a | Entailment or contradiction or neutral? | A train leaves Delhi at 9.0000000000E+00 a | Type_7 | A train leaves Delhi at 9 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 a | A train leaves Delhi at [Num] a | A train leaves Delhi at more than 8.0000000000E+00 a |
If KING is coded as 17 and MASS is coded as 29 Then DOG is coded as | If KING is coded as less than 6 7 and MASS is coded as 2 9 Then DOG is coded as | StressTest | If KING is coded as less than 67 and MASS is coded as 29 Then DOG is coded as | If KING is coded as 1 7 and MASS is coded as 2 9 Then DOG is coded as | If KING is coded as [Num] and MASS is coded as [Num] Then DOG is coded as | neutral | If KING is coded as 1 . 7 0 0 0 0 0 0 0 0 0 E + 0 1 and MASS is coded as 2 . 9 0 0 0 0 0 0 0 0 0 E + 0 1 Then DOG is coded as | Entailment or contradiction or neutral? | If KING is coded as less than 6.7000000000E+01 and MASS is coded as 2.9000000000E+01 Then DOG is coded as | Type_7 | If KING is coded as less than 6 . 7 0 0 0 0 0 0 0 0 0 E + 0 1 and MASS is coded as 2 . 9 0 0 0 0 0 0 0 0 0 E + 0 1 Then DOG is coded as | If KING is coded as less than [Num] and MASS is coded as [Num] Then DOG is coded as | If KING is coded as 1.7000000000E+01 and MASS is coded as 2.9000000000E+01 Then DOG is coded as |
more than 4000 which he and Dave earned at the end of 1 years | 9 0 0 0 which he and Dave earned at the end of 1 years | StressTest | 9000 which he and Dave earned at the end of 1 years | more than 4 0 0 0 which he and Dave earned at the end of 1 years | more than [Num] which he and Dave earned at the end of [Num] years | Entailment | more than 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 3 which he and Dave earned at the end of 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 years | Entailment or contradiction or neutral? | 9.0000000000E+03 which he and Dave earned at the end of 1.0000000000E+00 years | Type_7 | 9 . 0 0 0 0 0 0 0 0 0 0 E + 0 3 which he and Dave earned at the end of 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 years | [Num] which he and Dave earned at the end of [Num] years | more than 4.0000000000E+03 which he and Dave earned at the end of 1.0000000000E+00 years |
Two passengers on the missing Malaysian Airlines flight were traveling on stolen passports | An investigation was launched into the Flight 3 7 0 matter , with Malaysian and aviation authorities reviewing video and other documentation to try to identify not only who the passengers were that used the stolen passports but how the illegal passports cleared security . | NewsNLI | An investigation was launched into the Flight 370 matter , with Malaysian and aviation authorities reviewing video and other documentation to try to identify not only who the passengers were that used the stolen passports but how the illegal passports cleared security . | Two passengers on the missing Malaysian Airlines flight were traveling on stolen passports | Two passengers on the missing Malaysian Airlines flight were traveling on stolen passports | neutral | Two passengers on the missing Malaysian Airlines flight were traveling on stolen passports | Entailment or neutral? | An investigation was launched into the Flight 3.7000000000E+02 matter , with Malaysian and aviation authorities reviewing video and other documentation to try to identify not only who the passengers were that used the stolen passports but how the illegal passports cleared security . | Type_7 | An investigation was launched into the Flight 3 . 7 0 0 0 0 0 0 0 0 0 E + 0 2 matter , with Malaysian and aviation authorities reviewing video and other documentation to try to identify not only who the passengers were that used the stolen passports but how the illegal passports cleared security . | An investigation was launched into the Flight [Num] matter , with Malaysian and aviation authorities reviewing video and other documentation to try to identify not only who the passengers were that used the stolen passports but how the illegal passports cleared security . | Two passengers on the missing Malaysian Airlines flight were traveling on stolen passports |
One hour after Matthew started waking from w to y , a distance of less than 55 km , Johnny started walking along the same road from y to w | One hour after Matthew started waking from w to y , a distance of 4 5 km , Johnny started walking along the same road from y to w | StressTest | One hour after Matthew started waking from w to y , a distance of 45 km , Johnny started walking along the same road from y to w | One hour after Matthew started waking from w to y , a distance of less than 5 5 km , Johnny started walking along the same road from y to w | One hour after Matthew started waking from w to y , a distance of less than [Num] km , Johnny started walking along the same road from y to w | Entailment | One hour after Matthew started waking from w to y , a distance of less than 5 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 km , Johnny started walking along the same road from y to w | Entailment or contradiction or neutral? | One hour after Matthew started waking from w to y , a distance of 4.5000000000E+01 km , Johnny started walking along the same road from y to w | Type_7 | One hour after Matthew started waking from w to y , a distance of 4 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 km , Johnny started walking along the same road from y to w | One hour after Matthew started waking from w to y , a distance of [Num] km , Johnny started walking along the same road from y to w | One hour after Matthew started waking from w to y , a distance of less than 5.5000000000E+01 km , Johnny started walking along the same road from y to w |
In 6003 , the number of girls attending Jefferson High School was equal to the number of boys | In 2 0 0 3 , the number of girls attending Jefferson High School was equal to the number of boys | StressTest | In 2003 , the number of girls attending Jefferson High School was equal to the number of boys | In 6 0 0 3 , the number of girls attending Jefferson High School was equal to the number of boys | In [Num] , the number of girls attending Jefferson High School was equal to the number of boys | contradiction | In 6 . 0 0 3 0 0 0 0 0 0 0 E + 0 3 , the number of girls attending Jefferson High School was equal to the number of boys | Entailment or contradiction or neutral? | In 2.0030000000E+03 , the number of girls attending Jefferson High School was equal to the number of boys | Type_7 | In 2 . 0 0 3 0 0 0 0 0 0 0 E + 0 3 , the number of girls attending Jefferson High School was equal to the number of boys | In [Num] , the number of girls attending Jefferson High School was equal to the number of boys | In 6.0030000000E+03 , the number of girls attending Jefferson High School was equal to the number of boys |
Rahim bought 50 books for Rs | Rahim bought more than 4 0 books for Rs | StressTest | Rahim bought more than 40 books for Rs | Rahim bought 5 0 books for Rs | Rahim bought [Num] books for Rs | neutral | Rahim bought 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 books for Rs | Entailment or contradiction or neutral? | Rahim bought more than 4.0000000000E+01 books for Rs | Type_7 | Rahim bought more than 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 books for Rs | Rahim bought more than [Num] books for Rs | Rahim bought 5.0000000000E+01 books for Rs |
Plan could send nearly 3,000 troops , source says | The plan could send nearly 3 , 0 0 0 troops , another U.S. military official familiar with the proposal said . | NewsNLI | The plan could send nearly 3,000 troops , another U.S. military official familiar with the proposal said . | Plan could send nearly 3 , 0 0 0 troops , source says | Plan could send nearly [Num] troops , source says | Entailment | Plan could send nearly 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 3 troops , source says | Entailment or neutral? | The plan could send nearly 3.0000000000E+03 troops , another U.S. military official familiar with the proposal said . | Type_7 | The plan could send nearly 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 3 troops , another U.S. military official familiar with the proposal said . | The plan could send nearly [Num] troops , another U.S. military official familiar with the proposal said . | Plan could send nearly 3.0000000000E+03 troops , source says |
If Joe goes with her more than 1 years old twin brothers , and they each took 3 rides in total | If Joe goes with her 6 years old twin brothers , and they each took 3 rides in total | StressTest | If Joe goes with her 6 years old twin brothers , and they each took 3 rides in total | If Joe goes with her more than 1 years old twin brothers , and they each took 3 rides in total | If Joe goes with her more than [Num] years old twin brothers , and they each took [Num] rides in total | Entailment | If Joe goes with her more than 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 years old twin brothers , and they each took 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 rides in total | Entailment or contradiction or neutral? | If Joe goes with her 6.0000000000E+00 years old twin brothers , and they each took 3.0000000000E+00 rides in total | Type_7 | If Joe goes with her 6 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 years old twin brothers , and they each took 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 rides in total | If Joe goes with her [Num] years old twin brothers , and they each took [Num] rides in total | If Joe goes with her more than 1.0000000000E+00 years old twin brothers , and they each took 3.0000000000E+00 rides in total |
At the end of ' n ' years , Sandy got back more than 3 times the original investment | At the end of ' n ' years , Sandy got back 5 times the original investment | StressTest | At the end of ' n ' years , Sandy got back 5 times the original investment | At the end of ' n ' years , Sandy got back more than 3 times the original investment | At the end of ' n ' years , Sandy got back more than [Num] times the original investment | Entailment | At the end of ' n ' years , Sandy got back more than 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 times the original investment | Entailment or contradiction or neutral? | At the end of ' n ' years , Sandy got back 5.0000000000E+00 times the original investment | Type_7 | At the end of ' n ' years , Sandy got back 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 times the original investment | At the end of ' n ' years , Sandy got back [Num] times the original investment | At the end of ' n ' years , Sandy got back more than 3.0000000000E+00 times the original investment |
Almost 1,000 members of a banned religious group arrested in China | Hong Kong ( CNN ) -- Chinese police have arrested nearly 1 , 0 0 0 suspected members of a banned religious group , state media reported . | NewsNLI | Hong Kong ( CNN ) -- Chinese police have arrested nearly 1,000 suspected members of a banned religious group , state media reported . | Almost 1 , 0 0 0 members of a banned religious group arrested in China | Almost [Num] members of a banned religious group arrested in China | Entailment | Almost 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 3 members of a banned religious group arrested in China | Entailment or neutral? | Hong Kong ( CNN ) -- Chinese police have arrested nearly 1.0000000000E+03 suspected members of a banned religious group , state media reported . | Type_7 | Hong Kong ( CNN ) -- Chinese police have arrested nearly 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 3 suspected members of a banned religious group , state media reported . | Hong Kong ( CNN ) -- Chinese police have arrested nearly [Num] suspected members of a banned religious group , state media reported . | Almost 1.0000000000E+03 members of a banned religious group arrested in China |
Sally has 13.0 orange balloons now | Sally has 9 . 0 orange balloons and 4 . 0 blue balloons and she found 2 . 0 more of the orange balloons | AWPNLI | Sally has 9.0 orange balloons and 4.0 blue balloons and she found 2.0 more of the orange balloons | Sally has 1 3 . 0 orange balloons now | Sally has [Num] orange balloons now | contradiction | Sally has 1 . 3 0 0 0 0 0 0 0 0 0 E + 0 1 orange balloons now | Entailment or contradiction? | Sally has 9.0000000000E+00 orange balloons and 4.0000000000E+00 blue balloons and she found 2.0000000000E+00 more of the orange balloons | Type_7 | Sally has 9 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 orange balloons and 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 blue balloons and she found 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 more of the orange balloons | Sally has [Num] orange balloons and [Num] blue balloons and she found [Num] more of the orange balloons | Sally has 1.3000000000E+01 orange balloons now |
Spotted eagle ray weighed 75 to 80 pounds , official says | The spotted-eagle ray weighed about 7 5 to 8 0 pounds and had a 6-foot wingspan , Pino said . | NewsNLI | The spotted-eagle ray weighed about 75 to 80 pounds and had a 6-foot wingspan , Pino said . | Spotted eagle ray weighed 7 5 to 8 0 pounds , official says | Spotted eagle ray weighed [Num] to [Num] pounds , official says | Entailment | Spotted eagle ray weighed 7 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 to 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 pounds , official says | Entailment or neutral? | The spotted-eagle ray weighed about 7.5000000000E+01 to 8.0000000000E+01 pounds and had a 6.0000000000E+00-foot wingspan , Pino said . | Type_7 | The spotted-eagle ray weighed about 7 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 to 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 pounds and had a 6 . 0 0 0 0 0 0 0 0 0 0 E + 0 0-foot wingspan , Pino said . | The spotted-eagle ray weighed about [Num] to [Num] pounds and had a [Num]-foot wingspan , Pino said . | Spotted eagle ray weighed 7.5000000000E+01 to 8.0000000000E+01 pounds , official says |
Gang sweep nabs 158 members and associates of notorious MS-13 gang , among others | Among those charged are 1 5 8 members and associates of MS- 1 3 , with 1 0 5 others allegedly belonging to other gangs . | NewsNLI | Among those charged are 158 members and associates of MS-13 , with 105 others allegedly belonging to other gangs . | Gang sweep nabs 1 5 8 members and associates of notorious MS- 1 3 gang , among others | Gang sweep nabs [Num] members and associates of notorious MS[Num] gang , among others | Entailment | Gang sweep nabs 1 . 5 8 0 0 0 0 0 0 0 0 E + 0 2 members and associates of notorious MS- 1 . 3 0 0 0 0 0 0 0 0 0 E + 0 1 gang , among others | Entailment or neutral? | Among those charged are 1.5800000000E+02 members and associates of MS-1.3000000000E+01 , with 1.0500000000E+02 others allegedly belonging to other gangs . | Type_7 | Among those charged are 1 . 5 8 0 0 0 0 0 0 0 0 E + 0 2 members and associates of MS- 1 . 3 0 0 0 0 0 0 0 0 0 E + 0 1 , with 1 . 0 5 0 0 0 0 0 0 0 0 E + 0 2 others allegedly belonging to other gangs . | Among those charged are [Num] members and associates of MS[Num] , with [Num] others allegedly belonging to other gangs . | Gang sweep nabs 1.5800000000E+02 members and associates of notorious MS-1.3000000000E+01 gang , among others |
David obtained 76 , 65 , 82 , 67 and 85 marks ( out of 100 ) in English , Mathematics , Physics , Chemistry and Biology What are his average marks ? | David obtained more than 5 6 , 6 5 , 8 2 , 6 7 and 8 5 marks ( out of 1 0 0 ) in English , Mathematics , Physics , Chemistry and Biology What are his average marks ? | StressTest | David obtained more than 56 , 65 , 82 , 67 and 85 marks ( out of 100 ) in English , Mathematics , Physics , Chemistry and Biology What are his average marks ? | David obtained 7 6 , 6 5 , 8 2 , 6 7 and 8 5 marks ( out of 1 0 0 ) in English , Mathematics , Physics , Chemistry and Biology What are his average marks ? | David obtained [Num] , [Num] , [Num] , [Num] and [Num] marks ( out of [Num] ) in English , Mathematics , Physics , Chemistry and Biology What are his average marks ? | neutral | David obtained 7 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 , 6 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 , 8 . 2 0 0 0 0 0 0 0 0 0 E + 0 1 , 6 . 7 0 0 0 0 0 0 0 0 0 E + 0 1 and 8 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 marks ( out of 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 2 ) in English , Mathematics , Physics , Chemistry and Biology What are his average marks ? | Entailment or contradiction or neutral? | David obtained more than 5.6000000000E+01 , 6.5000000000E+01 , 8.2000000000E+01 , 6.7000000000E+01 and 8.5000000000E+01 marks ( out of 1.0000000000E+02 ) in English , Mathematics , Physics , Chemistry and Biology What are his average marks ? | Type_7 | David obtained more than 5 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 , 6 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 , 8 . 2 0 0 0 0 0 0 0 0 0 E + 0 1 , 6 . 7 0 0 0 0 0 0 0 0 0 E + 0 1 and 8 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 marks ( out of 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 2 ) in English , Mathematics , Physics , Chemistry and Biology What are his average marks ? | David obtained more than [Num] , [Num] , [Num] , [Num] and [Num] marks ( out of [Num] ) in English , Mathematics , Physics , Chemistry and Biology What are his average marks ? | David obtained 7.6000000000E+01 , 6.5000000000E+01 , 8.2000000000E+01 , 6.7000000000E+01 and 8.5000000000E+01 marks ( out of 1.0000000000E+02 ) in English , Mathematics , Physics , Chemistry and Biology What are his average marks ? |
Paul runs the first leg of the course in 22 seconds | Paul runs the first leg of the course in less than 8 2 seconds | StressTest | Paul runs the first leg of the course in less than 82 seconds | Paul runs the first leg of the course in 2 2 seconds | Paul runs the first leg of the course in [Num] seconds | neutral | Paul runs the first leg of the course in 2 . 2 0 0 0 0 0 0 0 0 0 E + 0 1 seconds | Entailment or contradiction or neutral? | Paul runs the first leg of the course in less than 8.2000000000E+01 seconds | Type_7 | Paul runs the first leg of the course in less than 8 . 2 0 0 0 0 0 0 0 0 0 E + 0 1 seconds | Paul runs the first leg of the course in less than [Num] seconds | Paul runs the first leg of the course in 2.2000000000E+01 seconds |
Srinivas saves one coin of 5 on first day of the
week , three coins of 5 on the second day of the week .
Five coins of 5 on third day and so on | Srinivas saves one coin of more than 4 on first day of the week , three coins of 5 on the second day of the week . Five coins of 5 on third day and so on | StressTest | Srinivas saves one coin of more than 4 on first day of the week , three coins of 5 on the second day of the week . Five coins of 5 on third day and so on | Srinivas saves one coin of 5 on first day of the
week , three coins of 5 on the second day of the week .
Five coins of 5 on third day and so on | Srinivas saves one coin of [Num] on first day of the
week , three coins of [Num] on the second day of the week .
Five coins of [Num] on third day and so on | neutral | Srinivas saves one coin of 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 on first day of the
week , three coins of 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 on the second day of the week .
Five coins of 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 on third day and so on | Entailment or contradiction or neutral? | Srinivas saves one coin of more than 4.0000000000E+00 on first day of the week , three coins of 5.0000000000E+00.0000000000E+00 on the second day of the week . Five coins of 5.0000000000E+00.0000000000E+00 on third day and so on | Type_7 | Srinivas saves one coin of more than 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 on first day of the week , three coins of 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 on the second day of the week . Five coins of 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 on third day and so on | Srinivas saves one coin of more than [Num] on first day of the week , three coins of [Num] on the second day of the week . Five coins of [Num] on third day and so on | Srinivas saves one coin of 5.0000000000E+00.0000000000E+00.0000000000E+00 on first day of the
week , three coins of 5.0000000000E+00.0000000000E+00.0000000000E+00 on the second day of the week .
Five coins of 5.0000000000E+00.0000000000E+00.0000000000E+00 on third day and so on |
Each week , Harry is paid x dollars per hour for the first less than 68 hours and 1.5 x dollars for each additional hour worked that week | Each week , Harry is paid x dollars per hour for the first 1 8 hours and 1 . 5 x dollars for each additional hour worked that week | StressTest | Each week , Harry is paid x dollars per hour for the first 18 hours and 1.5 x dollars for each additional hour worked that week | Each week , Harry is paid x dollars per hour for the first less than 6 8 hours and 1 . 5 x dollars for each additional hour worked that week | Each week , Harry is paid x dollars per hour for the first less than [Num] hours and [Num] x dollars for each additional hour worked that week | Entailment | Each week , Harry is paid x dollars per hour for the first less than 6 . 8 0 0 0 0 0 0 0 0 0 E + 0 1 hours and 1 . 5 0 0 0 0 0 0 0 0 0 E + 0 0 x dollars for each additional hour worked that week | Entailment or contradiction or neutral? | Each week , Harry is paid x dollars per hour for the first 1.8000000000E+01 hours and 1.5000000000E+00 x dollars for each additional hour worked that week | Type_7 | Each week , Harry is paid x dollars per hour for the first 1 . 8 0 0 0 0 0 0 0 0 0 E + 0 1 hours and 1 . 5 0 0 0 0 0 0 0 0 0 E + 0 0 x dollars for each additional hour worked that week | Each week , Harry is paid x dollars per hour for the first [Num] hours and [Num] x dollars for each additional hour worked that week | Each week , Harry is paid x dollars per hour for the first less than 6.8000000000E+01 hours and 1.5000000000E+00 x dollars for each additional hour worked that week |
Cross is accused of killing three people at two Jewish-affiliated facilities | Overland Park , Kansas ( CNN ) -- The man accused of killing three people at two Jewish-affiliated facilities in Kansas made no secret of his racist views , writing letters to newspapers and inviting people to white-supremacist meetings at his home , say those who knew him . | NewsNLI | Overland Park , Kansas ( CNN ) -- The man accused of killing three people at two Jewish-affiliated facilities in Kansas made no secret of his racist views , writing letters to newspapers and inviting people to white-supremacist meetings at his home , say those who knew him . | Cross is accused of killing three people at two Jewish-affiliated facilities | Cross is accused of killing three people at two Jewish-affiliated facilities | neutral | Cross is accused of killing three people at two Jewish-affiliated facilities | Entailment or neutral? | Overland Park , Kansas ( CNN ) -- The man accused of killing three people at two Jewish-affiliated facilities in Kansas made no secret of his racist views , writing letters to newspapers and inviting people to white-supremacist meetings at his home , say those who knew him . | Type_7 | Overland Park , Kansas ( CNN ) -- The man accused of killing three people at two Jewish-affiliated facilities in Kansas made no secret of his racist views , writing letters to newspapers and inviting people to white-supremacist meetings at his home , say those who knew him . | Overland Park , Kansas ( CNN ) -- The man accused of killing three people at two Jewish-affiliated facilities in Kansas made no secret of his racist views , writing letters to newspapers and inviting people to white-supremacist meetings at his home , say those who knew him . | Cross is accused of killing three people at two Jewish-affiliated facilities |
Victor has less than 65 cups if flour , 16 cups of sugar and 8 cups of milk | Victor has 1 5 cups if flour , 1 6 cups of sugar and 8 cups of milk | StressTest | Victor has 15 cups if flour , 16 cups of sugar and 8 cups of milk | Victor has less than 6 5 cups if flour , 1 6 cups of sugar and 8 cups of milk | Victor has less than [Num] cups if flour , [Num] cups of sugar and [Num] cups of milk | Entailment | Victor has less than 6 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 cups if flour , 1 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 cups of sugar and 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 cups of milk | Entailment or contradiction or neutral? | Victor has 1.5000000000E+01 cups if flour , 1.6000000000E+01 cups of sugar and 8.0000000000E+00 cups of milk | Type_7 | Victor has 1 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 cups if flour , 1 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 cups of sugar and 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 cups of milk | Victor has [Num] cups if flour , [Num] cups of sugar and [Num] cups of milk | Victor has less than 6.5000000000E+01 cups if flour , 1.6000000000E+01 cups of sugar and 8.0000000000E+00 cups of milk |
What is the average speed of Murali from A to C given that the ratio of distances between A to B and B to C is 8 : 4 ? | What is the average speed of Murali from A to C given that the ratio of distances between A to B and B to C is more than 4 : 4 ? | StressTest | What is the average speed of Murali from A to C given that the ratio of distances between A to B and B to C is more than 4 : 4 ? | What is the average speed of Murali from A to C given that the ratio of distances between A to B and B to C is 8 : 4 ? | What is the average speed of Murali from A to C given that the ratio of distances between A to B and B to C is [Num] : [Num] ? | neutral | What is the average speed of Murali from A to C given that the ratio of distances between A to B and B to C is 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 : 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 ? | Entailment or contradiction or neutral? | What is the average speed of Murali from A to C given that the ratio of distances between A to B and B to C is more than 4.0000000000E+00.0000000000E+00 : 4.0000000000E+00.0000000000E+00 ? | Type_7 | What is the average speed of Murali from A to C given that the ratio of distances between A to B and B to C is more than 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 : 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 ? | What is the average speed of Murali from A to C given that the ratio of distances between A to B and B to C is more than [Num] : [Num] ? | What is the average speed of Murali from A to C given that the ratio of distances between A to B and B to C is 8.0000000000E+00 : 4.0000000000E+00 ? |
1000 , what is Isabella ' s capital ? | less than 2 0 0 0 , what is Isabella ' s capital ? | StressTest | less than 2000 , what is Isabella ' s capital ? | 1 0 0 0 , what is Isabella ' s capital ? | [Num] , what is Isabella ' s capital ? | neutral | 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 3 , what is Isabella ' s capital ? | Entailment or contradiction or neutral? | less than 2.0000000000E+03 , what is Isabella ' s capital ? | Type_7 | less than 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 3 , what is Isabella ' s capital ? | less than [Num] , what is Isabella ' s capital ? | 1.0000000000E+03 , what is Isabella ' s capital ? |
Nevada stretch of U.S. Route 50 dubbed '' The Loneliest Road in America '' | The Loneliest Road in America , U.S. Route 5 0 , Nevada | NewsNLI | The Loneliest Road in America , U.S. Route 50 , Nevada | Nevada stretch of U.S. Route 5 0 dubbed '' The Loneliest Road in America '' | Nevada stretch of U.S. Route [Num] dubbed '' The Loneliest Road in America '' | Entailment | Nevada stretch of U.S. Route 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 dubbed '' The Loneliest Road in America '' | Entailment or neutral? | The Loneliest Road in America , U.S. Route 5.0000000000E+01 , Nevada | Type_7 | The Loneliest Road in America , U.S. Route 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 , Nevada | The Loneliest Road in America , U.S. Route [Num] , Nevada | Nevada stretch of U.S. Route 5.0000000000E+01 dubbed '' The Loneliest Road in America '' |
Fernando Alonso finishes second and loses out by three points | But Vettel roared back through the field to finish sixth and deny title rival Fernando Alonso by three points with the Spaniard finishing the race in second place . | NewsNLI | But Vettel roared back through the field to finish sixth and deny title rival Fernando Alonso by three points with the Spaniard finishing the race in second place . | Fernando Alonso finishes second and loses out by three points | Fernando Alonso finishes second and loses out by three points | neutral | Fernando Alonso finishes second and loses out by three points | Entailment or neutral? | But Vettel roared back through the field to finish sixth and deny title rival Fernando Alonso by three points with the Spaniard finishing the race in second place . | Type_7 | But Vettel roared back through the field to finish sixth and deny title rival Fernando Alonso by three points with the Spaniard finishing the race in second place . | But Vettel roared back through the field to finish sixth and deny title rival Fernando Alonso by three points with the Spaniard finishing the race in second place . | Fernando Alonso finishes second and loses out by three points |
Faiza has 7 purses , she gives 3 purse as gift | Faiza has more than 5 purses , she gives 3 purse as gift | StressTest | Faiza has more than 5 purses , she gives 3 purse as gift | Faiza has 7 purses , she gives 3 purse as gift | Faiza has [Num] purses , she gives [Num] purse as gift | neutral | Faiza has 7 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 purses , she gives 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 purse as gift | Entailment or contradiction or neutral? | Faiza has more than 5.0000000000E+00 purses , she gives 3.0000000000E+00 purse as gift | Type_7 | Faiza has more than 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 purses , she gives 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 purse as gift | Faiza has more than [Num] purses , she gives [Num] purse as gift | Faiza has 7.0000000000E+00 purses , she gives 3.0000000000E+00 purse as gift |
If Dravid paid a total of $ 38 , excluding sales tax , to rent the tool , for how many hours did she rent it ? | If Dravid paid a total of $ less than 8 8 , excluding sales tax , to rent the tool , for how many hours did she rent it ? | StressTest | If Dravid paid a total of $ less than 88 , excluding sales tax , to rent the tool , for how many hours did she rent it ? | If Dravid paid a total of $ 3 8 , excluding sales tax , to rent the tool , for how many hours did she rent it ? | If Dravid paid a total of $ [Num] , excluding sales tax , to rent the tool , for how many hours did she rent it ? | neutral | If Dravid paid a total of $ 3 . 8 0 0 0 0 0 0 0 0 0 E + 0 1 , excluding sales tax , to rent the tool , for how many hours did she rent it ? | Entailment or contradiction or neutral? | If Dravid paid a total of $ less than 8.8000000000E+01 , excluding sales tax , to rent the tool , for how many hours did she rent it ? | Type_7 | If Dravid paid a total of $ less than 8 . 8 0 0 0 0 0 0 0 0 0 E + 0 1 , excluding sales tax , to rent the tool , for how many hours did she rent it ? | If Dravid paid a total of $ less than [Num] , excluding sales tax , to rent the tool , for how many hours did she rent it ? | If Dravid paid a total of $ 3.8000000000E+01 , excluding sales tax , to rent the tool , for how many hours did she rent it ? |
Ayesha ' s father was 40 years of age when she was born while her mother was 32 years old when her brother six years younger to her was born | Ayesha ' s father was less than 6 0 years of age when she was born while her mother was 3 2 years old when her brother six years younger to her was born | StressTest | Ayesha ' s father was less than 60 years of age when she was born while her mother was 32 years old when her brother six years younger to her was born | Ayesha ' s father was 4 0 years of age when she was born while her mother was 3 2 years old when her brother six years younger to her was born | Ayesha ' s father was [Num] years of age when she was born while her mother was [Num] years old when her brother six years younger to her was born | neutral | Ayesha ' s father was 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 years of age when she was born while her mother was 3 . 2 0 0 0 0 0 0 0 0 0 E + 0 1 years old when her brother six years younger to her was born | Entailment or contradiction or neutral? | Ayesha ' s father was less than 6.0000000000E+01 years of age when she was born while her mother was 3.2000000000E+01 years old when her brother six years younger to her was born | Type_7 | Ayesha ' s father was less than 6 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 years of age when she was born while her mother was 3 . 2 0 0 0 0 0 0 0 0 0 E + 0 1 years old when her brother six years younger to her was born | Ayesha ' s father was less than [Num] years of age when she was born while her mother was [Num] years old when her brother six years younger to her was born | Ayesha ' s father was 4.0000000000E+01 years of age when she was born while her mother was 3.2000000000E+01 years old when her brother six years younger to her was born |
Angela ’ s grade was in the 90 th percentile out of 80 grades in her class | Angela ’ s grade was in the more than 4 0 th percentile out of 8 0 grades in her class | StressTest | Angela ’ s grade was in the more than 40 th percentile out of 80 grades in her class | Angela ’ s grade was in the 9 0 th percentile out of 8 0 grades in her class | Angela ’ s grade was in the [Num] th percentile out of [Num] grades in her class | neutral | Angela ’ s grade was in the 9 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 th percentile out of 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 grades in her class | Entailment or contradiction or neutral? | Angela ’ s grade was in the more than 4.0000000000E+01 th percentile out of 8.0000000000E+01 grades in her class | Type_7 | Angela ’ s grade was in the more than 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 th percentile out of 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 grades in her class | Angela ’ s grade was in the more than [Num] th percentile out of [Num] grades in her class | Angela ’ s grade was in the 9.0000000000E+01 th percentile out of 8.0000000000E+01 grades in her class |
Kim can do a work in 3 days while David can do the same work in 2 days | Kim can do a work in more than 1 days while David can do the same work in 2 days | StressTest | Kim can do a work in more than 1 days while David can do the same work in 2 days | Kim can do a work in 3 days while David can do the same work in 2 days | Kim can do a work in [Num] days while David can do the same work in [Num] days | neutral | Kim can do a work in 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 days while David can do the same work in 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 days | Entailment or contradiction or neutral? | Kim can do a work in more than 1.0000000000E+00 days while David can do the same work in 2.0000000000E+00 days | Type_7 | Kim can do a work in more than 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 days while David can do the same work in 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 days | Kim can do a work in more than [Num] days while David can do the same work in [Num] days | Kim can do a work in 3.0000000000E+00 days while David can do the same work in 2.0000000000E+00 days |
8.0 pictures were in each of the albums | Robin uploaded 3 5 . 0 pictures from her phone and 5 . 0 from her camera to facebook, and she sorted the pics into 5 . 0 different albums with the same amount of pics in each album | AWPNLI | Robin uploaded 35.0 pictures from her phone and 5.0 from her camera to facebook, and she sorted the pics into 5.0 different albums with the same amount of pics in each album | 8 . 0 pictures were in each of the albums | [Num] pictures were in each of the albums | Entailment | 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 pictures were in each of the albums | Entailment or contradiction? | Robin uploaded 3.5000000000E+01 pictures from her phone and 5.0000000000E+00000000000E+00 from her camera to facebook, and she sorted the pics into 5.0000000000E+00000000000E+00 different albums with the same amount of pics in each album | Type_7 | Robin uploaded 3 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 pictures from her phone and 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 from her camera to facebook, and she sorted the pics into 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 different albums with the same amount of pics in each album | Robin uploaded [Num] pictures from her phone and [Num] from her camera to facebook, and she sorted the pics into [Num] different albums with the same amount of pics in each album | 8.0000000000E+00 pictures were in each of the albums |
The German-born Boateng had told the referee three times he was being abused | During the friendly match , the AC Milan midfielder had told the referee three times he was being abused . | NewsNLI | During the friendly match , the AC Milan midfielder had told the referee three times he was being abused . | The German-born Boateng had told the referee three times he was being abused | The German-born Boateng had told the referee three times he was being abused | neutral | The German-born Boateng had told the referee three times he was being abused | Entailment or neutral? | During the friendly match , the AC Milan midfielder had told the referee three times he was being abused . | Type_7 | During the friendly match , the AC Milan midfielder had told the referee three times he was being abused . | During the friendly match , the AC Milan midfielder had told the referee three times he was being abused . | The German-born Boateng had told the referee three times he was being abused |
Amanda goes to the toy store to buy 1 ball and 3 different board games | Amanda goes to the toy store to buy less than 7 ball and 3 different board games | StressTest | Amanda goes to the toy store to buy less than 7 ball and 3 different board games | Amanda goes to the toy store to buy 1 ball and 3 different board games | Amanda goes to the toy store to buy [Num] ball and [Num] different board games | neutral | Amanda goes to the toy store to buy 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 ball and 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 different board games | Entailment or contradiction or neutral? | Amanda goes to the toy store to buy less than 7.0000000000E+00 ball and 3.0000000000E+00 different board games | Type_7 | Amanda goes to the toy store to buy less than 7 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 ball and 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 different board games | Amanda goes to the toy store to buy less than [Num] ball and [Num] different board games | Amanda goes to the toy store to buy 1.0000000000E+00 ball and 3.0000000000E+00 different board games |
After 7 years , Arun ' s age will be 25 years | After 5 years , Arun ' s age will be 2 5 years | StressTest | After 5 years , Arun ' s age will be 25 years | After 7 years , Arun ' s age will be 2 5 years | After [Num] years , Arun ' s age will be [Num] years | contradiction | After 7 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 years , Arun ' s age will be 2 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 years | Entailment or contradiction or neutral? | After 5.0000000000E+00 years , Arun ' s age will be 2.5000000000E+01.0000000000E+00 years | Type_7 | After 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 years , Arun ' s age will be 2 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 years | After [Num] years , Arun ' s age will be 2[Num] years | After 7.0000000000E+00 years , Arun ' s age will be 2.5000000000E+01 years |
Kamal will complete work in more than 20 days | Kamal will complete work in 2 0 days | StressTest | Kamal will complete work in 20 days | Kamal will complete work in more than 2 0 days | Kamal will complete work in more than [Num] days | contradiction | Kamal will complete work in more than 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 days | Entailment or contradiction or neutral? | Kamal will complete work in 2.0000000000E+01 days | Type_7 | Kamal will complete work in 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 days | Kamal will complete work in [Num] days | Kamal will complete work in more than 2.0000000000E+01 days |
16.0 cakes were served in total | A restaurant served 5 . 0 cakes during lunch and 6 . 0 during dinner today and the restaurant served 3 . 0 cakes yesterday | AWPNLI | A restaurant served 5.0 cakes during lunch and 6.0 during dinner today and the restaurant served 3.0 cakes yesterday | 1 6 . 0 cakes were served in total | [Num] cakes were served in total | contradiction | 1 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 cakes were served in total | Entailment or contradiction? | A restaurant served 5.0000000000E+00 cakes during lunch and 6.0000000000E+00 during dinner today and the restaurant served 3.0000000000E+00 cakes yesterday | Type_7 | A restaurant served 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 cakes during lunch and 6 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 during dinner today and the restaurant served 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 cakes yesterday | A restaurant served [Num] cakes during lunch and [Num] during dinner today and the restaurant served [Num] cakes yesterday | 1.6000000000E+01 cakes were served in total |
Present ages of Sameer and Anand are in the ratio of 5 : 4 respectively | Present ages of Sameer and Anand are in the ratio of less than 6 : 4 respectively | StressTest | Present ages of Sameer and Anand are in the ratio of less than 6 : 4 respectively | Present ages of Sameer and Anand are in the ratio of 5 : 4 respectively | Present ages of Sameer and Anand are in the ratio of [Num] : [Num] respectively | neutral | Present ages of Sameer and Anand are in the ratio of 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 : 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 respectively | Entailment or contradiction or neutral? | Present ages of Sameer and Anand are in the ratio of less than 6.0000000000E+00 : 4.0000000000E+00 respectively | Type_7 | Present ages of Sameer and Anand are in the ratio of less than 6 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 : 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 respectively | Present ages of Sameer and Anand are in the ratio of less than [Num] : [Num] respectively | Present ages of Sameer and Anand are in the ratio of 5.0000000000E+00 : 4.0000000000E+00 respectively |
164.0 slices of pizza are there altogether | We ordered 2 1 . 0 pizzas and each pizza has 8 . 0 slices | AWPNLI | We ordered 21.0 pizzas and each pizza has 8.0 slices | 1 6 4 . 0 slices of pizza are there altogether | [Num] slices of pizza are there altogether | contradiction | 1 . 6 4 0 0 0 0 0 0 0 0 E + 0 2 slices of pizza are there altogether | Entailment or contradiction? | We ordered 2.1000000000E+01 pizzas and each pizza has 8.0000000000E+00 slices | Type_7 | We ordered 2 . 1 0 0 0 0 0 0 0 0 0 E + 0 1 pizzas and each pizza has 8 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 slices | We ordered [Num] pizzas and each pizza has [Num] slices | 1.6400000000E+02 slices of pizza are there altogether |
Dan Hotels operates 12 moderate to upscale hotels in Israel | Properties : 1 2 moderate to upscale hotels in Israel . | NewsNLI | Properties : 12 moderate to upscale hotels in Israel . | Dan Hotels operates 1 2 moderate to upscale hotels in Israel | Dan Hotels operates [Num] moderate to upscale hotels in Israel | neutral | Dan Hotels operates 1 . 2 0 0 0 0 0 0 0 0 0 E + 0 1 moderate to upscale hotels in Israel | Entailment or neutral? | Properties : 1.2000000000E+01 moderate to upscale hotels in Israel . | Type_7 | Properties : 1 . 2 0 0 0 0 0 0 0 0 0 E + 0 1 moderate to upscale hotels in Israel . | Properties : [Num] moderate to upscale hotels in Israel . | Dan Hotels operates 1.2000000000E+01 moderate to upscale hotels in Israel |
The ratio of men to women in the Snyder community choir is 4 to 3 | The ratio of men to women in the Snyder community choir is less than 7 to 3 | StressTest | The ratio of men to women in the Snyder community choir is less than 7 to 3 | The ratio of men to women in the Snyder community choir is 4 to 3 | The ratio of men to women in the Snyder community choir is [Num] to [Num] | neutral | The ratio of men to women in the Snyder community choir is 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 to 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 | Entailment or contradiction or neutral? | The ratio of men to women in the Snyder community choir is less than 7.0000000000E+00 to 3.0000000000E+00 | Type_7 | The ratio of men to women in the Snyder community choir is less than 7 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 to 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 | The ratio of men to women in the Snyder community choir is less than [Num] to [Num] | The ratio of men to women in the Snyder community choir is 4.0000000000E+00 to 3.0000000000E+00 |
Martin bought 30 concert tickets , some at the full price of $ 2.00 per ticket , and some at a discounted price of $ 1.60 per ticket | Martin bought 1 0 concert tickets , some at the full price of $ 2 . 0 0 per ticket , and some at a discounted price of $ 1 . 6 0 per ticket | StressTest | Martin bought 10 concert tickets , some at the full price of $ 2.00 per ticket , and some at a discounted price of $ 1.60 per ticket | Martin bought 3 0 concert tickets , some at the full price of $ 2 . 0 0 per ticket , and some at a discounted price of $ 1 . 6 0 per ticket | Martin bought [Num] concert tickets , some at the full price of $ [Num] per ticket , and some at a discounted price of $ [Num] per ticket | contradiction | Martin bought 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 concert tickets , some at the full price of $ 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 per ticket , and some at a discounted price of $ 1 . 6 0 0 0 0 0 0 0 0 0 E + 0 0 per ticket | Entailment or contradiction or neutral? | Martin bought 1.0000000000E+01 concert tickets , some at the full price of $ 2.0000000000E+00 per ticket , and some at a discounted price of $ 1.6000000000E+00 per ticket | Type_7 | Martin bought 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 concert tickets , some at the full price of $ 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 per ticket , and some at a discounted price of $ 1 . 6 0 0 0 0 0 0 0 0 0 E + 0 0 per ticket | Martin bought [Num] concert tickets , some at the full price of $ [Num] per ticket , and some at a discounted price of $ [Num] per ticket | Martin bought 3.0000000000E+01 concert tickets , some at the full price of $ 2.0000000000E+00 per ticket , and some at a discounted price of $ 1.6000000000E+00 per ticket |
Andrea played the game , getting at least one score of each of more than 1 , 2 , 3 , 4 , and 5 , and never getting the same score in consecutive steps | Andrea played the game , getting at least one score of each of 1 , 2 , 3 , 4 , and 5 , and never getting the same score in consecutive steps | StressTest | Andrea played the game , getting at least one score of each of 1 , 2 , 3 , 4 , and 5 , and never getting the same score in consecutive steps | Andrea played the game , getting at least one score of each of more than 1 , 2 , 3 , 4 , and 5 , and never getting the same score in consecutive steps | Andrea played the game , getting at least one score of each of more than [Num] , [Num] , [Num] , [Num] , and [Num] , and never getting the same score in consecutive steps | contradiction | Andrea played the game , getting at least one score of each of more than 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 , 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 , 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 , 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 , and 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 , and never getting the same score in consecutive steps | Entailment or contradiction or neutral? | Andrea played the game , getting at least one score of each of 1.0000000000E+00 , 2.0000000000E+00 , 3.0000000000E+00 , 4.0000000000E+00 , and 5.0000000000E+00 , and never getting the same score in consecutive steps | Type_7 | Andrea played the game , getting at least one score of each of 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 , 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 , 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 , 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 , and 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 , and never getting the same score in consecutive steps | Andrea played the game , getting at least one score of each of [Num] , [Num] , [Num] , [Num] , and [Num] , and never getting the same score in consecutive steps | Andrea played the game , getting at least one score of each of more than 1.0000000000E+00 , 2.0000000000E+00 , 3.0000000000E+00 , 4.0000000000E+00 , and 5.0000000000E+00 , and never getting the same score in consecutive steps |
Jack had framed less than 84 photographs taken by Octavia , and 12 photographs taken by other photographers | Jack had framed 2 4 photographs taken by Octavia , and 1 2 photographs taken by other photographers | StressTest | Jack had framed 24 photographs taken by Octavia , and 12 photographs taken by other photographers | Jack had framed less than 8 4 photographs taken by Octavia , and 1 2 photographs taken by other photographers | Jack had framed less than [Num] photographs taken by Octavia , and [Num] photographs taken by other photographers | Entailment | Jack had framed less than 8 . 4 0 0 0 0 0 0 0 0 0 E + 0 1 photographs taken by Octavia , and 1 . 2 0 0 0 0 0 0 0 0 0 E + 0 1 photographs taken by other photographers | Entailment or contradiction or neutral? | Jack had framed 2.4000000000E+01 photographs taken by Octavia , and 1.2000000000E+01 photographs taken by other photographers | Type_7 | Jack had framed 2 . 4 0 0 0 0 0 0 0 0 0 E + 0 1 photographs taken by Octavia , and 1 . 2 0 0 0 0 0 0 0 0 0 E + 0 1 photographs taken by other photographers | Jack had framed [Num] photographs taken by Octavia , and [Num] photographs taken by other photographers | Jack had framed less than 8.4000000000E+01 photographs taken by Octavia , and 1.2000000000E+01 photographs taken by other photographers |
Sandy gets less than 3 marks for each correct sum and loses 2 marks for each incorrect sum | Sandy gets 3 marks for each correct sum and loses 2 marks for each incorrect sum | StressTest | Sandy gets 3 marks for each correct sum and loses 2 marks for each incorrect sum | Sandy gets less than 3 marks for each correct sum and loses 2 marks for each incorrect sum | Sandy gets less than [Num] marks for each correct sum and loses [Num] marks for each incorrect sum | contradiction | Sandy gets less than 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 marks for each correct sum and loses 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 marks for each incorrect sum | Entailment or contradiction or neutral? | Sandy gets 3.0000000000E+00 marks for each correct sum and loses 2.0000000000E+00 marks for each incorrect sum | Type_7 | Sandy gets 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 marks for each correct sum and loses 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 marks for each incorrect sum | Sandy gets [Num] marks for each correct sum and loses [Num] marks for each incorrect sum | Sandy gets less than 3.0000000000E+00 marks for each correct sum and loses 2.0000000000E+00 marks for each incorrect sum |
Sandy has 3.0 carrots left | Sandy grew 6 . 0 carrots and Sam took 3 . 0 carrots | AWPNLI | Sandy grew 6.0 carrots and Sam took 3.0 carrots | Sandy has 3 . 0 carrots left | Sandy has [Num] carrots left | Entailment | Sandy has 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 carrots left | Entailment or contradiction? | Sandy grew 6.0000000000E+00 carrots and Sam took 3.0000000000E+00 carrots | Type_7 | Sandy grew 6 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 carrots and Sam took 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 carrots | Sandy grew [Num] carrots and Sam took [Num] carrots | Sandy has 3.0000000000E+00 carrots left |
less than 7535 find the share of Sravan . | 5 5 3 5 find the share of Sravan . | StressTest | 5535 find the share of Sravan . | less than 7 5 3 5 find the share of Sravan . | less than [Num] find the share of Sravan . | Entailment | less than 7 . 5 3 5 0 0 0 0 0 0 0 E + 0 3 find the share of Sravan . | Entailment or contradiction or neutral? | 5.5350000000E+03 find the share of Sravan . | Type_7 | 5 . 5 3 5 0 0 0 0 0 0 0 E + 0 3 find the share of Sravan . | [Num] find the share of Sravan . | less than 7.5350000000E+03 find the share of Sravan . |
The ratio of men to women in the Snyder community choir is more than 3 to 5 | The ratio of men to women in the Snyder community choir is 4 to 5 | StressTest | The ratio of men to women in the Snyder community choir is 4 to 5 | The ratio of men to women in the Snyder community choir is more than 3 to 5 | The ratio of men to women in the Snyder community choir is more than [Num] to [Num] | Entailment | The ratio of men to women in the Snyder community choir is more than 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 to 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 | Entailment or contradiction or neutral? | The ratio of men to women in the Snyder community choir is 4.0000000000E+00 to 5.0000000000E+00 | Type_7 | The ratio of men to women in the Snyder community choir is 4 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 to 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 | The ratio of men to women in the Snyder community choir is [Num] to [Num] | The ratio of men to women in the Snyder community choir is more than 3.0000000000E+00 to 5.0000000000E+00 |
John ' s Bank ' s saving amount is decreased 12 % due to loan payment and current balance is Rs | John ' s Bank ' s saving amount is decreased less than 4 2 % due to loan payment and current balance is Rs | StressTest | John ' s Bank ' s saving amount is decreased less than 42 % due to loan payment and current balance is Rs | John ' s Bank ' s saving amount is decreased 1 2 % due to loan payment and current balance is Rs | John ' s Bank ' s saving amount is decreased [Num] % due to loan payment and current balance is Rs | neutral | John ' s Bank ' s saving amount is decreased 1 . 2 0 0 0 0 0 0 0 0 0 E + 0 1 % due to loan payment and current balance is Rs | Entailment or contradiction or neutral? | John ' s Bank ' s saving amount is decreased less than 4.2000000000E+01 % due to loan payment and current balance is Rs | Type_7 | John ' s Bank ' s saving amount is decreased less than 4 . 2 0 0 0 0 0 0 0 0 0 E + 0 1 % due to loan payment and current balance is Rs | John ' s Bank ' s saving amount is decreased less than [Num] % due to loan payment and current balance is Rs | John ' s Bank ' s saving amount is decreased 1.2000000000E+01 % due to loan payment and current balance is Rs |
Two U.S. Navy ships rescue a total of 282 people | The 2 8 2 people were brought aboard the Bataan , and the Navy posted a YouTube video of the rescued people -- apparent African migrants -- looking tired or grateful as they stepped aboard the warship . | NewsNLI | The 282 people were brought aboard the Bataan , and the Navy posted a YouTube video of the rescued people -- apparent African migrants -- looking tired or grateful as they stepped aboard the warship . | Two U.S. Navy ships rescue a total of 2 8 2 people | Two U.S. Navy ships rescue a total of [Num] people | neutral | Two U.S. Navy ships rescue a total of 2 . 8 2 0 0 0 0 0 0 0 0 E + 0 2 people | Entailment or neutral? | The 2.8200000000E+02 people were brought aboard the Bataan , and the Navy posted a YouTube video of the rescued people -- apparent African migrants -- looking tired or grateful as they stepped aboard the warship . | Type_7 | The 2 . 8 2 0 0 0 0 0 0 0 0 E + 0 2 people were brought aboard the Bataan , and the Navy posted a YouTube video of the rescued people -- apparent African migrants -- looking tired or grateful as they stepped aboard the warship . | The [Num] people were brought aboard the Bataan , and the Navy posted a YouTube video of the rescued people -- apparent African migrants -- looking tired or grateful as they stepped aboard the warship . | Two U.S. Navy ships rescue a total of 2.8200000000E+02 people |
Teresa jogged for 5.0 hours | Teresa jogged 2 5 . 0 kilometers at 5 . 0 kilometers per hour | AWPNLI | Teresa jogged 25.0 kilometers at 5.0 kilometers per hour | Teresa jogged for 5 . 0 hours | Teresa jogged for [Num] hours | Entailment | Teresa jogged for 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 hours | Entailment or contradiction? | Teresa jogged 2.5000000000E+01 kilometers at 5.0000000000E+00 kilometers per hour | Type_7 | Teresa jogged 2 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 kilometers at 5 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 kilometers per hour | Teresa jogged [Num] kilometers at [Num] kilometers per hour | Teresa jogged for 5.0000000000E+00 hours |
Assuming that Karen drives at an average speed of more than 20 mph and Tom drives at an average speed of 45 mph , how many H miles will Tom drive before Karen wins the bet ? | Assuming that Karen drives at an average speed of 6 0 mph and Tom drives at an average speed of 4 5 mph , how many H miles will Tom drive before Karen wins the bet ? | StressTest | Assuming that Karen drives at an average speed of 60 mph and Tom drives at an average speed of 45 mph , how many H miles will Tom drive before Karen wins the bet ? | Assuming that Karen drives at an average speed of more than 2 0 mph and Tom drives at an average speed of 4 5 mph , how many H miles will Tom drive before Karen wins the bet ? | Assuming that Karen drives at an average speed of more than [Num] mph and Tom drives at an average speed of [Num] mph , how many H miles will Tom drive before Karen wins the bet ? | Entailment | Assuming that Karen drives at an average speed of more than 2 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 mph and Tom drives at an average speed of 4 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 mph , how many H miles will Tom drive before Karen wins the bet ? | Entailment or contradiction or neutral? | Assuming that Karen drives at an average speed of 6.0000000000E+01 mph and Tom drives at an average speed of 4.5000000000E+01 mph , how many H miles will Tom drive before Karen wins the bet ? | Type_7 | Assuming that Karen drives at an average speed of 6 . 0 0 0 0 0 0 0 0 0 0 E + 0 1 mph and Tom drives at an average speed of 4 . 5 0 0 0 0 0 0 0 0 0 E + 0 1 mph , how many H miles will Tom drive before Karen wins the bet ? | Assuming that Karen drives at an average speed of [Num] mph and Tom drives at an average speed of [Num] mph , how many H miles will Tom drive before Karen wins the bet ? | Assuming that Karen drives at an average speed of more than 2.0000000000E+01 mph and Tom drives at an average speed of 4.5000000000E+01 mph , how many H miles will Tom drive before Karen wins the bet ? |
When Professor Wang looked at the rosters for this term ' s classes , she saw that the roster for her economics class ( E ) had 16 names , the roster for her marketing class ( M ) had 28 , and the roster for her statistics class ( S ) had 18 | When Professor Wang looked at the rosters for this term ' s classes , she saw that the roster for her economics class ( E ) had less than 7 6 names , the roster for her marketing class ( M ) had 2 8 , and the roster for her statistics class ( S ) had 1 8 | StressTest | When Professor Wang looked at the rosters for this term ' s classes , she saw that the roster for her economics class ( E ) had less than 76 names , the roster for her marketing class ( M ) had 28 , and the roster for her statistics class ( S ) had 18 | When Professor Wang looked at the rosters for this term ' s classes , she saw that the roster for her economics class ( E ) had 1 6 names , the roster for her marketing class ( M ) had 2 8 , and the roster for her statistics class ( S ) had 1 8 | When Professor Wang looked at the rosters for this term ' s classes , she saw that the roster for her economics class ( E ) had [Num] names , the roster for her marketing class ( M ) had [Num] , and the roster for her statistics class ( S ) had [Num] | neutral | When Professor Wang looked at the rosters for this term ' s classes , she saw that the roster for her economics class ( E ) had 1 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 names , the roster for her marketing class ( M ) had 2 . 8 0 0 0 0 0 0 0 0 0 E + 0 1 , and the roster for her statistics class ( S ) had 1 . 8 0 0 0 0 0 0 0 0 0 E + 0 1 | Entailment or contradiction or neutral? | When Professor Wang looked at the rosters for this term ' s classes , she saw that the roster for her economics class ( E ) had less than 7.6000000000E+01 names , the roster for her marketing class ( M ) had 2.8000000000E+01 , and the roster for her statistics class ( S ) had 1.8000000000E+01 | Type_7 | When Professor Wang looked at the rosters for this term ' s classes , she saw that the roster for her economics class ( E ) had less than 7 . 6 0 0 0 0 0 0 0 0 0 E + 0 1 names , the roster for her marketing class ( M ) had 2 . 8 0 0 0 0 0 0 0 0 0 E + 0 1 , and the roster for her statistics class ( S ) had 1 . 8 0 0 0 0 0 0 0 0 0 E + 0 1 | When Professor Wang looked at the rosters for this term ' s classes , she saw that the roster for her economics class ( E ) had less than [Num] names , the roster for her marketing class ( M ) had [Num] , and the roster for her statistics class ( S ) had [Num] | When Professor Wang looked at the rosters for this term ' s classes , she saw that the roster for her economics class ( E ) had 1.6000000000E+01 names , the roster for her marketing class ( M ) had 2.8000000000E+01 , and the roster for her statistics class ( S ) had 1.8000000000E+01 |
How many possible ways can less than 7 girls ( Rebecca , Kate , Ashley ) go on a date with 3 boys ( Peter , Kyle , Sam ) ? | How many possible ways can 1 girls ( Rebecca , Kate , Ashley ) go on a date with 3 boys ( Peter , Kyle , Sam ) ? | StressTest | How many possible ways can 1 girls ( Rebecca , Kate , Ashley ) go on a date with 3 boys ( Peter , Kyle , Sam ) ? | How many possible ways can less than 7 girls ( Rebecca , Kate , Ashley ) go on a date with 3 boys ( Peter , Kyle , Sam ) ? | How many possible ways can less than [Num] girls ( Rebecca , Kate , Ashley ) go on a date with [Num] boys ( Peter , Kyle , Sam ) ? | Entailment | How many possible ways can less than 7 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 girls ( Rebecca , Kate , Ashley ) go on a date with 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 boys ( Peter , Kyle , Sam ) ? | Entailment or contradiction or neutral? | How many possible ways can 1.0000000000E+00 girls ( Rebecca , Kate , Ashley ) go on a date with 3.0000000000E+00 boys ( Peter , Kyle , Sam ) ? | Type_7 | How many possible ways can 1 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 girls ( Rebecca , Kate , Ashley ) go on a date with 3 . 0 0 0 0 0 0 0 0 0 0 E + 0 0 boys ( Peter , Kyle , Sam ) ? | How many possible ways can [Num] girls ( Rebecca , Kate , Ashley ) go on a date with [Num] boys ( Peter , Kyle , Sam ) ? | How many possible ways can less than 7.0000000000E+00 girls ( Rebecca , Kate , Ashley ) go on a date with 3.0000000000E+00 boys ( Peter , Kyle , Sam ) ? |
End of preview.