Datasets:
Tasks:
Text Classification
Size:
100K<n<1M
annotations_creators: | |
- expert-generated | |
language: | |
- en | |
- de | |
- fr | |
language_creators: | |
- found | |
license: [] | |
multilinguality: | |
- multilingual | |
pretty_name: XNLI Parallel Corpus | |
size_categories: | |
- 100K<n<1M | |
source_datasets: | |
- extended|xnli | |
tags: | |
- mode classification | |
- aligned | |
task_categories: | |
- text-classification | |
task_ids: [] | |
dataset_info: | |
- config_name: en | |
features: | |
- name: text | |
dtype: string | |
- name: label | |
dtype: | |
class_label: | |
names: | |
'0': spoken | |
'1': written | |
splits: | |
- name: train | |
num_bytes: 92288 | |
num_examples: 830 | |
- name: test | |
num_bytes: 186853 | |
num_examples: 1669 | |
- config_name: de | |
features: | |
- name: text | |
dtype: string | |
- name: label | |
dtype: | |
class_label: | |
names: | |
'0': spoken | |
'1': written | |
splits: | |
- name: train | |
num_bytes: 105681 | |
num_examples: 830 | |
- name: test | |
num_bytes: 214008 | |
num_examples: 1669 | |
- config_name: fr | |
features: | |
- name: text | |
dtype: string | |
- name: label | |
dtype: | |
class_label: | |
names: | |
'0': spoken | |
'1': written | |
splits: | |
- name: train | |
num_bytes: 830 | |
num_examples: 109164 | |
- name: test | |
num_bytes: 221286 | |
num_examples: 1669 | |
download_size: 1864 | |
dataset_size: 1840 | |
# Dataset Card for XNLI Parallel Corpus | |
## Dataset Description | |
- **Homepage:** | |
- **Repository:** | |
- **Paper:** | |
- **Leaderboard:** | |
- **Point of Contact:** | |
### 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 |