AdapterHub/bert-base-uncased-pf-anli_r3
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Dataset Card for "anli"
Dataset Summary
The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset, The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure. ANLI is much more difficult than its predecessors including SNLI and MNLI. It contains three rounds. Each round has train/dev/test splits.
Supported Tasks and Leaderboards
Languages
Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
Data Instances
plain_text
- Size of downloaded dataset files: 17.76 MB
- Size of the generated dataset: 73.55 MB
- Total amount of disk used: 91.31 MB
An example of 'train_r2' looks as follows.
This example was too long and was cropped:
{
"hypothesis": "Idris Sultan was born in the first month of the year preceding 1994.",
"label": 0,
"premise": "\"Idris Sultan (born January 1993) is a Tanzanian Actor and comedian, actor and radio host who won the Big Brother Africa-Hotshot...",
"reason": "",
"uid": "ed5c37ab-77c5-4dbc-ba75-8fd617b19712"
}
Data Fields
The data fields are the same among all splits.
plain_text
uid: astringfeature.premise: astringfeature.hypothesis: astringfeature.label: a classification label, with possible values includingentailment(0),neutral(1),contradiction(2).reason: astringfeature.
Data Splits
| name | train_r1 | dev_r1 | train_r2 | dev_r2 | train_r3 | dev_r3 | test_r1 | test_r2 | test_r3 |
|---|---|---|---|---|---|---|---|---|---|
| plain_text | 16946 | 1000 | 45460 | 1000 | 100459 | 1200 | 1000 | 1000 | 1200 |
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
@InProceedings{nie2019adversarial,
title={Adversarial NLI: A New Benchmark for Natural Language Understanding},
author={Nie, Yixin
and Williams, Adina
and Dinan, Emily
and Bansal, Mohit
and Weston, Jason
and Kiela, Douwe},
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
year = "2020",
publisher = "Association for Computational Linguistics",
}
Contributions
Thanks to @thomwolf, @easonnie, @lhoestq, @patrickvonplaten for adding this dataset.
Homepage:
github.com
Size of downloaded dataset files:
17.76 MB
Size of the generated dataset:
73.55 MB
Total amount of disk used:
91.31 MB
