
# 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
task_categories:text-classification annotations_creators:expert-generated language_creators:found multilinguality:multilingual size_categories:100K<n<1M source_datasets:extended|xnli language:en language:de language:fr mode classification aligned region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en", "de", "fr"], "license": [], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended|xnli"], "task_categories": ["text-classification"], "task_ids": [], "pretty_name": "XNLI Parallel Corpus", "tags": ["mode classification", "aligned"], "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}
TAGS
#task_categories-text-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-extended|xnli #language-English #language-German #language-French #mode classification #aligned #region-us 


# 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

URL

Here is the citation for the original XNLI paper.



#### 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




### Contributions

N/A
# 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

URL

Here is the citation for the original XNLI paper. #### 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 ### Contributions

N/A
TAGS
#task_categories-text-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-extended|xnli #language-English #language-German #language-French #mode classification #aligned #region-us 
 # 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

URL

Here is the citation for the original XNLI paper. #### 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 ### Contributions

N/A
passage: TAGS
#task_categories-text-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-extended|xnli #language-English #language-German #language-French #mode classification #aligned #region-us 
# 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