distilbert-base-uncased-finetuned-sst-2-english
Text Classification
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Updated
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8.34M
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68
Datasets:
Task Categories:
text-classification
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
unknown
Annotations Creators:
unknown
Source Datasets:
unknown
Dataset Preview
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| sentence (string) | label (class label) | idx (int) |
|---|---|---|
Our friends won't buy this analysis, let alone the next one we propose.
| 1
(acceptable)
| 0
|
One more pseudo generalization and I'm giving up.
| 1
(acceptable)
| 1
|
One more pseudo generalization or I'm giving up.
| 1
(acceptable)
| 2
|
The more we study verbs, the crazier they get.
| 1
(acceptable)
| 3
|
Day by day the facts are getting murkier.
| 1
(acceptable)
| 4
|
I'll fix you a drink.
| 1
(acceptable)
| 5
|
Fred watered the plants flat.
| 1
(acceptable)
| 6
|
Bill coughed his way out of the restaurant.
| 1
(acceptable)
| 7
|
We're dancing the night away.
| 1
(acceptable)
| 8
|
Herman hammered the metal flat.
| 1
(acceptable)
| 9
|
The critics laughed the play off the stage.
| 1
(acceptable)
| 10
|
The pond froze solid.
| 1
(acceptable)
| 11
|
Bill rolled out of the room.
| 1
(acceptable)
| 12
|
The gardener watered the flowers flat.
| 1
(acceptable)
| 13
|
The gardener watered the flowers.
| 1
(acceptable)
| 14
|
Bill broke the bathtub into pieces.
| 1
(acceptable)
| 15
|
Bill broke the bathtub.
| 1
(acceptable)
| 16
|
They drank the pub dry.
| 1
(acceptable)
| 17
|
They drank the pub.
| 0
(unacceptable)
| 18
|
The professor talked us into a stupor.
| 1
(acceptable)
| 19
|
The professor talked us.
| 0
(unacceptable)
| 20
|
We yelled ourselves hoarse.
| 1
(acceptable)
| 21
|
We yelled ourselves.
| 0
(unacceptable)
| 22
|
We yelled Harry hoarse.
| 0
(unacceptable)
| 23
|
Harry coughed himself into a fit.
| 1
(acceptable)
| 24
|
Harry coughed himself.
| 0
(unacceptable)
| 25
|
Harry coughed us into a fit.
| 0
(unacceptable)
| 26
|
Bill followed the road into the forest.
| 1
(acceptable)
| 27
|
We drove Highway 5 from SD to SF.
| 1
(acceptable)
| 28
|
Fred tracked the leak to its source.
| 1
(acceptable)
| 29
|
John danced waltzes across the room.
| 1
(acceptable)
| 30
|
Bill urinated out the window.
| 1
(acceptable)
| 31
|
Bill coughed out the window.
| 1
(acceptable)
| 32
|
Bill bled on the floor.
| 1
(acceptable)
| 33
|
The toilet leaked through the floor into the kitchen below.
| 1
(acceptable)
| 34
|
Bill ate off the floor.
| 1
(acceptable)
| 35
|
Bill drank from the hose.
| 1
(acceptable)
| 36
|
This metal hammers flat easily.
| 1
(acceptable)
| 37
|
They made him president.
| 1
(acceptable)
| 38
|
They made him angry.
| 1
(acceptable)
| 39
|
They caused him to become angry by making him.
| 0
(unacceptable)
| 40
|
They caused him to become president by making him.
| 0
(unacceptable)
| 41
|
They made him to exhaustion.
| 0
(unacceptable)
| 42
|
They made him into a monster.
| 1
(acceptable)
| 43
|
The trolley rumbled through the tunnel.
| 1
(acceptable)
| 44
|
The wagon rumbled down the road.
| 1
(acceptable)
| 45
|
The bullets whistled past the house.
| 1
(acceptable)
| 46
|
The knee replacement candidate groaned up the stairs.
| 1
(acceptable)
| 47
|
The car honked down the road.
| 0
(unacceptable)
| 48
|
The dog barked out of the room.
| 0
(unacceptable)
| 49
|
The dog barked its way out of the room.
| 1
(acceptable)
| 50
|
Bill whistled his way past the house.
| 1
(acceptable)
| 51
|
The witch vanished into the forest.
| 1
(acceptable)
| 52
|
Bill disappeared down the road.
| 1
(acceptable)
| 53
|
The witch went into the forest by vanishing.
| 0
(unacceptable)
| 54
|
The witch went into the forest and thereby vanished.
| 1
(acceptable)
| 55
|
The building is tall and wide.
| 1
(acceptable)
| 56
|
The building is tall and tall.
| 0
(unacceptable)
| 57
|
This building is taller and wider than that one.
| 1
(acceptable)
| 58
|
This building got taller and wider than that one.
| 1
(acceptable)
| 59
|
This building got taller and taller.
| 1
(acceptable)
| 60
|
This building is taller and taller.
| 0
(unacceptable)
| 61
|
This building got than that one.
| 0
(unacceptable)
| 62
|
This building is than that one.
| 0
(unacceptable)
| 63
|
Bill floated into the cave.
| 1
(acceptable)
| 64
|
Bill floated into the cave for hours.
| 0
(unacceptable)
| 65
|
Bill pushed Harry off the sofa for hours.
| 0
(unacceptable)
| 66
|
Bill floated down the river for hours.
| 1
(acceptable)
| 67
|
Bill floated down the river.
| 1
(acceptable)
| 68
|
Bill pushed Harry along the trail for hours.
| 1
(acceptable)
| 69
|
Bill pushed Harry along the trail.
| 1
(acceptable)
| 70
|
The road zigzagged down the hill.
| 1
(acceptable)
| 71
|
The rope stretched over the pulley.
| 1
(acceptable)
| 72
|
The weights stretched the rope over the pulley.
| 1
(acceptable)
| 73
|
The weights kept the rope stretched over the pulley.
| 1
(acceptable)
| 74
|
Sam cut himself free.
| 1
(acceptable)
| 75
|
Sam got free by cutting his finger.
| 1
(acceptable)
| 76
|
Bill cried himself to sleep.
| 1
(acceptable)
| 77
|
Bill cried Sue to sleep.
| 0
(unacceptable)
| 78
|
Bill squeezed himself through the hole.
| 1
(acceptable)
| 79
|
Bill sang himself to sleep.
| 1
(acceptable)
| 80
|
Bill squeezed the puppet through the hole.
| 1
(acceptable)
| 81
|
Bill sang Sue to sleep.
| 1
(acceptable)
| 82
|
The elevator rumbled itself to the ground.
| 0
(unacceptable)
| 83
|
If the telephone rang, it could ring itself silly.
| 1
(acceptable)
| 84
|
She yelled hoarse.
| 0
(unacceptable)
| 85
|
Ted cried to sleep.
| 0
(unacceptable)
| 86
|
The tiger bled to death.
| 1
(acceptable)
| 87
|
He coughed awake and we were all overjoyed, especially Sierra.
| 1
(acceptable)
| 88
|
John coughed awake, rubbing his nose and cursing under his breath.
| 1
(acceptable)
| 89
|
John coughed himself awake on the bank of the lake where he and Bill had their play.
| 1
(acceptable)
| 90
|
Ron yawned himself awake.
| 1
(acceptable)
| 91
|
She coughed herself awake as the leaf landed on her nose.
| 1
(acceptable)
| 92
|
The worm wriggled onto the carpet.
| 1
(acceptable)
| 93
|
The chocolate melted onto the carpet.
| 1
(acceptable)
| 94
|
The ball wriggled itself loose.
| 0
(unacceptable)
| 95
|
Bill wriggled himself loose.
| 1
(acceptable)
| 96
|
Aliza wriggled her tooth loose.
| 1
(acceptable)
| 97
|
The off center spinning flywheel shook itself loose.
| 1
(acceptable)
| 98
|
The more you eat, the less you want.
| 1
(acceptable)
| 99
|
End of preview (truncated to 100
rows)
Dataset Card for GLUE
Dataset Summary
GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.
Supported Tasks and Leaderboards
The leaderboard for the GLUE benchmark can be found at this address. It comprises the following tasks:
ax
A manually-curated evaluation dataset for fine-grained analysis of system performance on a broad range of linguistic phenomena. This dataset evaluates sentence understanding through Natural Language Inference (NLI) problems. Use a model trained on MulitNLI to produce predictions for this dataset.
cola
The Corpus of Linguistic Acceptability consists of English acceptability judgments drawn from books and journal articles on linguistic theory. Each example is a sequence of words annotated with whether it is a grammatical English sentence.
mnli
The Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data.
mnli_matched
The matched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information.
mnli_mismatched
The mismatched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information.
mrpc
The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.
qnli
The Stanford Question Answering Dataset is a question-answering dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The authors of the benchmark convert the task into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue.
qqp
The Quora Question Pairs2 dataset is a collection of question pairs from the community question-answering website Quora. The task is to determine whether a pair of questions are semantically equivalent.
rte
The Recognizing Textual Entailment (RTE) datasets come from a series of annual textual entailment challenges. The authors of the benchmark combined the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009). Examples are constructed based on news and Wikipedia text. The authors of the benchmark convert all datasets to a two-class split, where for three-class datasets they collapse neutral and contradiction into not entailment, for consistency.
sst2
The Stanford Sentiment Treebank consists of sentences from movie reviews and human annotations of their sentiment. The task is to predict the sentiment of a given sentence. It uses the two-way (positive/negative) class split, with only sentence-level labels.
stsb
The Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data. Each pair is human-annotated with a similarity score from 1 to 5.
wnli
The Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun from a list of choices. The examples are manually constructed to foil simple statistical methods: Each one is contingent on contextual information provided by a single word or phrase in the sentence. To convert the problem into sentence pair classification, the authors of the benchmark construct sentence pairs by replacing the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. They use a small evaluation set consisting of new examples derived from fiction books that was shared privately by the authors of the original corpus. While the included training set is balanced between two classes, the test set is imbalanced between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: hypotheses are sometimes shared between training and development examples, so if a model memorizes the training examples, they will predict the wrong label on corresponding development set example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence between a model's score on this task and its score on the unconverted original task. The authors of the benchmark call converted dataset WNLI (Winograd NLI).
Languages
The language data in GLUE is in English (BCP-47 en)
Dataset Structure
Data Instances
ax
- Size of downloaded dataset files: 0.21 MB
- Size of the generated dataset: 0.23 MB
- Total amount of disk used: 0.44 MB
An example of 'test' looks as follows.
{
"premise": "The cat sat on the mat.",
"hypothesis": "The cat did not sit on the mat.",
"label": -1,
"idx: 0
}
cola
- Size of downloaded dataset files: 0.36 MB
- Size of the generated dataset: 0.58 MB
- Total amount of disk used: 0.94 MB
An example of 'train' looks as follows.
{
"sentence": "Our friends won't buy this analysis, let alone the next one we propose.",
"label": 1,
"id": 0
}
mnli
- Size of downloaded dataset files: 298.29 MB
- Size of the generated dataset: 78.65 MB
- Total amount of disk used: 376.95 MB
An example of 'train' looks as follows.
{
"premise": "Conceptually cream skimming has two basic dimensions - product and geography.",
"hypothesis": "Product and geography are what make cream skimming work.",
"label": 1,
"idx": 0
}
mnli_matched
- Size of downloaded dataset files: 298.29 MB
- Size of the generated dataset: 3.52 MB
- Total amount of disk used: 301.82 MB
An example of 'test' looks as follows.
{
"premise": "Hierbas, ans seco, ans dulce, and frigola are just a few names worth keeping a look-out for.",
"hypothesis": "Hierbas is a name worth looking out for.",
"label": -1,
"idx": 0
}
mnli_mismatched
- Size of downloaded dataset files: 298.29 MB
- Size of the generated dataset: 3.73 MB
- Total amount of disk used: 302.02 MB
An example of 'test' looks as follows.
{
"premise": "What have you decided, what are you going to do?",
"hypothesis": "So what's your decision?,
"label": -1,
"idx": 0
}
mrpc
qnli
qqp
rte
sst2
stsb
wnli
Data Fields
The data fields are the same among all splits.
ax
premise: astringfeature.hypothesis: astringfeature.label: a classification label, with possible values includingentailment(0),neutral(1),contradiction(2).idx: aint32feature.
cola
sentence: astringfeature.label: a classification label, with possible values includingunacceptable(0),acceptable(1).idx: aint32feature.
mnli
premise: astringfeature.hypothesis: astringfeature.label: a classification label, with possible values includingentailment(0),neutral(1),contradiction(2).idx: aint32feature.
mnli_matched
premise: astringfeature.hypothesis: astringfeature.label: a classification label, with possible values includingentailment(0),neutral(1),contradiction(2).idx: aint32feature.
mnli_mismatched
premise: astringfeature.hypothesis: astringfeature.label: a classification label, with possible values includingentailment(0),neutral(1),contradiction(2).idx: aint32feature.
mrpc
qnli
qqp
rte
sst2
stsb
wnli
Data Splits
ax
| test | |
|---|---|
| ax | 1104 |
cola
| train | validation | test | |
|---|---|---|---|
| cola | 8551 | 1043 | 1063 |
mnli
| train | validation_matched | validation_mismatched | test_matched | test_mismatched | |
|---|---|---|---|---|---|
| mnli | 392702 | 9815 | 9832 | 9796 | 9847 |
mnli_matched
| validation | test | |
|---|---|---|
| mnli_matched | 9815 | 9796 |
mnli_mismatched
| validation | test | |
|---|---|---|
| mnli_mismatched | 9832 | 9847 |
mrpc
qnli
qqp
rte
sst2
stsb
wnli
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@article{warstadt2018neural,
title={Neural Network Acceptability Judgments},
author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},
journal={arXiv preprint arXiv:1805.12471},
year={2018}
}
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
note={In the Proceedings of ICLR.},
year={2019}
}
Note that each GLUE dataset has its own citation. Please see the source to see
the correct citation for each contained dataset.
Contributions
Thanks to @patpizio, @jeswan, @thomwolf, @patrickvonplaten, @mariamabarham for adding this dataset.
Homepage:
nyu-mll.github.io
Size of downloaded dataset files:
955.33 MB
Size of the generated dataset:
229.68 MB
Total amount of disk used:
1185.01 MB
