Datasets:

MtCelesteMa
/

multiglue

task stringclasses 7 values	sentence1 stringlengths 1 1.39k	sentence2 stringlengths 1 2.46k ⌀	label class label 2 classes
cola	Our friends won't buy this analysis, let alone the next one we propose.	null	11
cola	One more pseudo generalization and I'm giving up.	null	11
cola	One more pseudo generalization or I'm giving up.	null	11
cola	The more we study verbs, the crazier they get.	null	11
cola	Day by day the facts are getting murkier.	null	11
cola	I'll fix you a drink.	null	11
cola	Fred watered the plants flat.	null	11
cola	Bill coughed his way out of the restaurant.	null	11
cola	We're dancing the night away.	null	11
cola	Herman hammered the metal flat.	null	11
cola	The critics laughed the play off the stage.	null	11
cola	The pond froze solid.	null	11
cola	Bill rolled out of the room.	null	11
cola	The gardener watered the flowers flat.	null	11
cola	The gardener watered the flowers.	null	11
cola	Bill broke the bathtub into pieces.	null	11
cola	Bill broke the bathtub.	null	11
cola	They drank the pub dry.	null	11
cola	They drank the pub.	null	00
cola	The professor talked us into a stupor.	null	11
cola	The professor talked us.	null	00
cola	We yelled ourselves hoarse.	null	11
cola	We yelled ourselves.	null	00
cola	We yelled Harry hoarse.	null	00
cola	Harry coughed himself into a fit.	null	11
cola	Harry coughed himself.	null	00
cola	Harry coughed us into a fit.	null	00
cola	Bill followed the road into the forest.	null	11
cola	We drove Highway 5 from SD to SF.	null	11
cola	Fred tracked the leak to its source.	null	11
cola	John danced waltzes across the room.	null	11
cola	Bill urinated out the window.	null	11
cola	Bill coughed out the window.	null	11
cola	Bill bled on the floor.	null	11
cola	The toilet leaked through the floor into the kitchen below.	null	11
cola	Bill ate off the floor.	null	11
cola	Bill drank from the hose.	null	11
cola	This metal hammers flat easily.	null	11
cola	They made him president.	null	11
cola	They made him angry.	null	11
cola	They caused him to become angry by making him.	null	00
cola	They caused him to become president by making him.	null	00
cola	They made him to exhaustion.	null	00
cola	They made him into a monster.	null	11
cola	The trolley rumbled through the tunnel.	null	11
cola	The wagon rumbled down the road.	null	11
cola	The bullets whistled past the house.	null	11
cola	The knee replacement candidate groaned up the stairs.	null	11
cola	The car honked down the road.	null	00
cola	The dog barked out of the room.	null	00
cola	The dog barked its way out of the room.	null	11
cola	Bill whistled his way past the house.	null	11
cola	The witch vanished into the forest.	null	11
cola	Bill disappeared down the road.	null	11
cola	The witch went into the forest by vanishing.	null	00
cola	The witch went into the forest and thereby vanished.	null	11
cola	The building is tall and wide.	null	11
cola	The building is tall and tall.	null	00
cola	This building is taller and wider than that one.	null	11
cola	This building got taller and wider than that one.	null	11
cola	This building got taller and taller.	null	11
cola	This building is taller and taller.	null	00
cola	This building got than that one.	null	00
cola	This building is than that one.	null	00
cola	Bill floated into the cave.	null	11
cola	Bill floated into the cave for hours.	null	00
cola	Bill pushed Harry off the sofa for hours.	null	00
cola	Bill floated down the river for hours.	null	11
cola	Bill floated down the river.	null	11
cola	Bill pushed Harry along the trail for hours.	null	11
cola	Bill pushed Harry along the trail.	null	11
cola	The road zigzagged down the hill.	null	11
cola	The rope stretched over the pulley.	null	11
cola	The weights stretched the rope over the pulley.	null	11
cola	The weights kept the rope stretched over the pulley.	null	11
cola	Sam cut himself free.	null	11
cola	Sam got free by cutting his finger.	null	11
cola	Bill cried himself to sleep.	null	11
cola	Bill cried Sue to sleep.	null	00
cola	Bill squeezed himself through the hole.	null	11
cola	Bill sang himself to sleep.	null	11
cola	Bill squeezed the puppet through the hole.	null	11
cola	Bill sang Sue to sleep.	null	11
cola	The elevator rumbled itself to the ground.	null	00
cola	If the telephone rang, it could ring itself silly.	null	11
cola	She yelled hoarse.	null	00
cola	Ted cried to sleep.	null	00
cola	The tiger bled to death.	null	11
cola	He coughed awake and we were all overjoyed, especially Sierra.	null	11
cola	John coughed awake, rubbing his nose and cursing under his breath.	null	11
cola	John coughed himself awake on the bank of the lake where he and Bill had their play.	null	11
cola	Ron yawned himself awake.	null	11
cola	She coughed herself awake as the leaf landed on her nose.	null	11
cola	The worm wriggled onto the carpet.	null	11
cola	The chocolate melted onto the carpet.	null	11
cola	The ball wriggled itself loose.	null	00
cola	Bill wriggled himself loose.	null	11
cola	Aliza wriggled her tooth loose.	null	11
cola	The off center spinning flywheel shook itself loose.	null	11
cola	The more you eat, the less you want.	null	11

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Dataset Card for MultiGLUE

Dataset Summary

This dataset is a combination of the cola, mrpc, qnli, qqp, rte, sst2, and wnli subsets of the GLUE dataset. Its intended use is to benchmark language models on multitask binary classification.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

Like the GLUE dataset, this dataset is in English.

Dataset Structure

Data Instances

An example instance looks like this:

{
  "label": 1,
  "task": "cola",
  "sentence1": "The sailors rode the breeze clear of the rocks.",
  "sentence2": null
}

Data Fields

task: A string feature, indicating the GLUE task the instance is from.
sentence1: A string feature.
sentence2: A string feature.
label: A classification label, either 0 or 1.

Data Splits

train: 551,282 instances
validation: 48,564 instances
test: 404,183 instances, no classification label (same as GLUE)

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

This dataset is created by combining the cola, mrpc, qnli, qqp, rte, sst2, and wnli subsets of the GLUE dataset.

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

[More Information Needed]

Contributions

[More Information Needed]

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Size of downloaded dataset files:

186 MB

Size of the auto-converted Parquet files:

94 MB

Number of rows:

1,004,029

Models trained or fine-tuned on MtCelesteMa/multiglue

MtCelesteMa/bert-base-uncased-finetuned-multiglue

Text Classification • Updated Jan 30, 2023 • 2