
# Dataset Card for [opus]

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description
**Disclaimer.** Loading of dataset is slow, thus it may not be feasible when loading at scale. I'd suggest to use the other OPUS datasets on Huggingface which loads a specific corpus.

Loads [OPUS](https://opus.nlpl.eu/) as HuggingFace dataset. OPUS is an open parallel corpus covering 700+ languages and 1100+ datasets.

Given a `src` and `tgt` language, this repository can load *all* available parallel corpus. To my knowledge, other OPUS datasets on Huggingface loads a specific corpus


**Requirements**. 

```
pip install pandas

# pip install my fork of `opustools`
git clone https://github.com/larrylawl/OpusTools.git
pip install -e OpusTools/opustools_pkg
```

**Example Usage**.

```
# args follows `opustools`: https://pypi.org/project/opustools/

src="en"
tgt="id"
download_dir="data"  # dir to save downloaded files
corpus="bible-uedin" # corpus name. Leave as `None` to download all available corpus for the src-tgt pair.


dataset = load_dataset("larrylawl/opus", 
                       src=src, 
                       tgt=tgt,
                       download_dir=download_dir,
                       corpus=corpus)
                       )
```

**Disclaimer**.

This repository is still in active development. Do make a PR if there're any issues!

### Dataset Summary

[More Information Needed]

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

Available languages can be viewed on the [OPUS API](https://opus.nlpl.eu/opusapi/?languages=True)

## Dataset Structure

### Data Instances

```
{'src': 'In the beginning God created the heavens and the earth .',
 'tgt': 'Pada mulanya , waktu Allah mulai menciptakan alam semesta'}
```

### Data Fields

```
features = {
            "src": datasets.Value("string"),
            "tgt": datasets.Value("string"),
        }
```

### Data Splits

Merged all data into train split.

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

[More Information Needed]

### Contributions

Thanks to [@larrylawl](https://larrylawl.github.io/) for adding this dataset.
task_categories:translation annotations_creators:expert-generated annotations_creators:found language_creators:found language_creators:expert-generated multilinguality:translation parallel-corpus region:us
{"annotations_creators": ["expert-generated", "found"], "language_creators": ["found", "expert-generated"], "license": [], "multilinguality": ["translation"], "size_categories": [], "source_datasets": [], "task_categories": ["translation"], "task_ids": [], "pretty_name": "opus", "tags": ["parallel-corpus"]}
TAGS
#task_categories-translation #annotations_creators-expert-generated #annotations_creators-found #language_creators-found #language_creators-expert-generated #multilinguality-translation #parallel-corpus #region-us 


# Dataset Card for [opus]

## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - 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
  - Contributions

## Dataset Description
Disclaimer. Loading of dataset is slow, thus it may not be feasible when loading at scale. I'd suggest to use the other OPUS datasets on Huggingface which loads a specific corpus.

Loads OPUS as HuggingFace dataset. OPUS is an open parallel corpus covering 700+ languages and 1100+ datasets.

Given a 'src' and 'tgt' language, this repository can load *all* available parallel corpus. To my knowledge, other OPUS datasets on Huggingface loads a specific corpus


Requirements. 



Example Usage.



Disclaimer.

This repository is still in active development. Do make a PR if there're any issues!

### Dataset Summary



### Supported Tasks and Leaderboards



### Languages

Available languages can be viewed on the OPUS API

## Dataset Structure

### Data Instances



### Data Fields



### Data Splits

Merged all data into train split.

## 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







### Contributions

Thanks to @larrylawl for adding this dataset.
# Dataset Card for [opus] ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - 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
  - Contributions ## Dataset Description
Disclaimer. Loading of dataset is slow, thus it may not be feasible when loading at scale. I'd suggest to use the other OPUS datasets on Huggingface which loads a specific corpus.

Loads OPUS as HuggingFace dataset. OPUS is an open parallel corpus covering 700+ languages and 1100+ datasets.

Given a 'src' and 'tgt' language, this repository can load *all* available parallel corpus. To my knowledge, other OPUS datasets on Huggingface loads a specific corpus


Requirements. 



Example Usage.



Disclaimer.

This repository is still in active development. Do make a PR if there're any issues! ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages

Available languages can be viewed on the OPUS API ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits

Merged all data into train split. ## 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 ### Contributions

Thanks to @larrylawl for adding this dataset.
TAGS
#task_categories-translation #annotations_creators-expert-generated #annotations_creators-found #language_creators-found #language_creators-expert-generated #multilinguality-translation #parallel-corpus #region-us 
 # Dataset Card for [opus] ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - 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
  - Contributions ## Dataset Description
Disclaimer. Loading of dataset is slow, thus it may not be feasible when loading at scale. I'd suggest to use the other OPUS datasets on Huggingface which loads a specific corpus.

Loads OPUS as HuggingFace dataset. OPUS is an open parallel corpus covering 700+ languages and 1100+ datasets.

Given a 'src' and 'tgt' language, this repository can load *all* available parallel corpus. To my knowledge, other OPUS datasets on Huggingface loads a specific corpus


Requirements. 



Example Usage.



Disclaimer.

This repository is still in active development. Do make a PR if there're any issues! ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages

Available languages can be viewed on the OPUS API ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits

Merged all data into train split. ## 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 ### Contributions

Thanks to @larrylawl for adding this dataset.
passage: TAGS
#task_categories-translation #annotations_creators-expert-generated #annotations_creators-found #language_creators-found #language_creators-expert-generated #multilinguality-translation #parallel-corpus #region-us 
# Dataset Card for [opus]## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - 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
  - Contributions## Dataset Description
Disclaimer. Loading of dataset is slow, thus it may not be feasible when loading at scale. I'd suggest to use the other OPUS datasets on Huggingface which loads a specific corpus.

Loads OPUS as HuggingFace dataset. OPUS is an open parallel corpus covering 700+ languages and 1100+ datasets.

Given a 'src' and 'tgt' language, this repository can load *all* available parallel corpus. To my knowledge, other OPUS datasets on Huggingface loads a specific corpus


Requirements. 



Example Usage.



Disclaimer.

This repository is still in active development. Do make a PR if there're any issues!### Dataset Summary### Supported Tasks and Leaderboards### Languages

Available languages can be viewed on the OPUS API## Dataset Structure### Data Instances### Data Fields### Data Splits

Merged all data into train split.## 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