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Dataset Card for XNLI Parallel Corpus
Dataset Summary
Supported Tasks and Leaderboards
Binary mode classification (spoken vs written)
Languages
- English
- German
- French
Dataset Structure
Data Instances
{ 'text': "And he said , Mama , I 'm home .", 'label': 0 }
Data Fields
- text: sentence
- label: binary label of text (0: spoken 1: written)
Data Splits
- train: 830
- test: 1669
Other Statistics
Vocabulary Size
English
- train: 4363
- test: 7128
German
- train: 5070
- test: 8601
French
- train: 4881
- test: 7935
Average Sentence Length
English
- train: 20.689156626506023
- test: 20.75254643499101
German
- train: 20.367469879518072
- test: 20.639904134212102
French
- train: 23.455421686746988
- test: 23.731575793888556
Label Split
- train:
- 0: 166
- 1: 664
- test:
- 0: 334
- 1: 1335
Out-of-vocabulary words in model
English
BERT (bert-base-uncased)
- train: 800
- test: 1638
mBERT (bert-base-multilingual-uncased)
- train: 1347
- test: 2693
German BERT (bert-base-german-dbmdz-uncased)
- train: 3228
- test: 5581
flauBERT (flaubert-base-uncased)
- train: 4363
- test: 7128
German
BERT (bert-base-uncased)
- train: 4285
- test: 7387
mBERT (bert-base-multilingual-uncased)
- train: 3126
- test: 5863
German BERT (bert-base-german-dbmdz-uncased)
- train: 2033
- test: 3938
flauBERT (flaubert-base-uncased)
- train: 5069
- test: 8600
French
BERT (bert-base-uncased)
- train: 3784
- test: 6289
mBERT (bert-base-multilingual-uncased)
- train: 2847
- test: 5084
German BERT (bert-base-german-dbmdz-uncased)
- train: 4212
- test: 6964
flauBERT (flaubert-base-uncased)
- train: 4881
- test: 7935
Dataset Creation
Curation Rationale
N/A
Source Data
https://github.com/facebookresearch/XNLI
Here is the citation for the original XNLI paper.
@InProceedings{conneau2018xnli,
author = "Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
and Stoyanov, Veselin",
title = "XNLI: Evaluating Cross-lingual Sentence Representations",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods
in Natural Language Processing",
year = "2018",
publisher = "Association for Computational Linguistics",
location = "Brussels, Belgium",
}
Initial Data Collection and Normalization
N/A
Who are the source language producers?
N/A
Annotations
Annotation process
N/A
Who are the annotators?
N/A
Personal and Sensitive Information
N/A
Considerations for Using the Data
Social Impact of Dataset
N/A
Discussion of Biases
N/A
Other Known Limitations
N/A
Additional Information
Dataset Curators
N/A
Licensing Information
N/A
Citation Information
Contributions
N/A
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