|
--- |
|
language: |
|
- en |
|
- multilingual |
|
- ar |
|
- cs |
|
- de |
|
- es |
|
- fr |
|
- it |
|
- ja |
|
- nl |
|
- pt |
|
- ru |
|
size_categories: |
|
- 100K<n<1M |
|
task_categories: |
|
- feature-extraction |
|
- sentence-similarity |
|
pretty_name: News-Commentary |
|
tags: |
|
- sentence-transformers |
|
dataset_info: |
|
- config_name: all |
|
features: |
|
- name: english |
|
dtype: string |
|
- name: non_english |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 364506039 |
|
num_examples: 972552 |
|
download_size: 212877098 |
|
dataset_size: 364506039 |
|
- config_name: en-ar |
|
features: |
|
- name: english |
|
dtype: string |
|
- name: non_english |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 92586042 |
|
num_examples: 160944 |
|
download_size: 49722288 |
|
dataset_size: 92586042 |
|
- config_name: en-cs |
|
features: |
|
- name: english |
|
dtype: string |
|
- name: non_english |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 49880143 |
|
num_examples: 170683 |
|
download_size: 32540459 |
|
dataset_size: 49880143 |
|
- config_name: en-de |
|
features: |
|
- name: english |
|
dtype: string |
|
- name: non_english |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 67264401 |
|
num_examples: 214971 |
|
download_size: 41648198 |
|
dataset_size: 67264401 |
|
- config_name: en-es |
|
features: |
|
- name: english |
|
dtype: string |
|
- name: non_english |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 10885552 |
|
num_examples: 34352 |
|
download_size: 6671353 |
|
dataset_size: 10885552 |
|
- config_name: en-fr |
|
features: |
|
- name: english |
|
dtype: string |
|
- name: non_english |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 34229410 |
|
num_examples: 106040 |
|
download_size: 20771370 |
|
dataset_size: 34229410 |
|
- config_name: en-it |
|
features: |
|
- name: english |
|
dtype: string |
|
- name: non_english |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 14672830 |
|
num_examples: 45791 |
|
download_size: 8938106 |
|
dataset_size: 14672830 |
|
- config_name: en-ja |
|
features: |
|
- name: english |
|
dtype: string |
|
- name: non_english |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 541819 |
|
num_examples: 1253 |
|
download_size: 327264 |
|
dataset_size: 541819 |
|
- config_name: en-nl |
|
features: |
|
- name: english |
|
dtype: string |
|
- name: non_english |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 7209024 |
|
num_examples: 22890 |
|
download_size: 4399324 |
|
dataset_size: 7209024 |
|
- config_name: en-pt |
|
features: |
|
- name: english |
|
dtype: string |
|
- name: non_english |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 9170349 |
|
num_examples: 29077 |
|
download_size: 5684510 |
|
dataset_size: 9170349 |
|
- config_name: en-ru |
|
features: |
|
- name: english |
|
dtype: string |
|
- name: non_english |
|
dtype: string |
|
splits: |
|
- name: train |
|
num_bytes: 77891207 |
|
num_examples: 183413 |
|
download_size: 42240433 |
|
dataset_size: 77891207 |
|
configs: |
|
- config_name: all |
|
data_files: |
|
- split: train |
|
path: all/train-* |
|
- config_name: en-ar |
|
data_files: |
|
- split: train |
|
path: en-ar/train-* |
|
- config_name: en-cs |
|
data_files: |
|
- split: train |
|
path: en-cs/train-* |
|
- config_name: en-de |
|
data_files: |
|
- split: train |
|
path: en-de/train-* |
|
- config_name: en-es |
|
data_files: |
|
- split: train |
|
path: en-es/train-* |
|
- config_name: en-fr |
|
data_files: |
|
- split: train |
|
path: en-fr/train-* |
|
- config_name: en-it |
|
data_files: |
|
- split: train |
|
path: en-it/train-* |
|
- config_name: en-ja |
|
data_files: |
|
- split: train |
|
path: en-ja/train-* |
|
- config_name: en-nl |
|
data_files: |
|
- split: train |
|
path: en-nl/train-* |
|
- config_name: en-pt |
|
data_files: |
|
- split: train |
|
path: en-pt/train-* |
|
- config_name: en-ru |
|
data_files: |
|
- split: train |
|
path: en-ru/train-* |
|
--- |
|
|
|
# Dataset Card for Parallel Sentences - News Commentary |
|
|
|
This dataset contains parallel sentences (i.e. English sentence + the same sentences in another language) for numerous other languages. Most of the sentences originate from the [OPUS website](https://opus.nlpl.eu/). |
|
In particular, this dataset contains the [News-Commentary](https://opus.nlpl.eu/News-Commentary/corpus/version/News-Commentary) dataset. |
|
|
|
## Related Datasets |
|
|
|
The following datasets are also a part of the Parallel Sentences collection: |
|
* [parallel-sentences-europarl](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-europarl) |
|
* [parallel-sentences-global-voices](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-global-voices) |
|
* [parallel-sentences-muse](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-muse) |
|
* [parallel-sentences-jw300](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-jw300) |
|
* [parallel-sentences-news-commentary](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-news-commentary) |
|
* [parallel-sentences-opensubtitles](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-opensubtitles) |
|
* [parallel-sentences-talks](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) |
|
* [parallel-sentences-tatoeba](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-tatoeba) |
|
* [parallel-sentences-wikimatrix](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-wikimatrix) |
|
* [parallel-sentences-wikititles](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-wikititles) |
|
* [parallel-sentences-ccmatrix](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-ccmatrix) |
|
|
|
These datasets can be used to train multilingual sentence embedding models. For more information, see [sbert.net - Multilingual Models](https://www.sbert.net/examples/training/multilingual/README.html). |
|
|
|
## Dataset Subsets |
|
|
|
### `all` subset |
|
|
|
* Columns: "english", "non_english" |
|
* Column types: `str`, `str` |
|
* Examples: |
|
```python |
|
{ |
|
"english": "Pure interests – expressed through lobbying power – were undoubtedly important to several key deregulation measures in the US, whose political system and campaign-finance rules are peculiarly conducive to the power of specific lobbies.", |
|
"non_english": "Заинтересованные группы, действующие посредством лоббирования власти, явились важными действующими лицами при принятии нескольких ключевых мер по отмене регулирующих норм в США, чья политическая система и правила финансирования кампаний особенно поддаются власти отдельных лобби." |
|
} |
|
``` |
|
* Collection strategy: Combining all other subsets from this dataset. |
|
* Deduplified: No |
|
|
|
### `en-...` subsets |
|
|
|
* Columns: "english", "non_english" |
|
* Column types: `str`, `str` |
|
* Examples: |
|
```python |
|
{ |
|
"english": "Last December, many gold bugs were arguing that the price was inevitably headed for $2,000.", |
|
"non_english": "Lo scorso dicembre, molti fanatici dell’oro sostenevano che il suo prezzo era inevitabilmente destinato a raggiungere i 2000 dollari." |
|
} |
|
``` |
|
* Collection strategy: Processing the raw data from [parallel-sentences](https://huggingface.co/datasets/sentence-transformers/parallel-sentences) and formatting it in Parquet, followed by deduplication. |
|
* Deduplified: Yes |
