Datasets:

sentence-transformers
/

parallel-sentences-news-commentary

---
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
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- 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
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    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
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    num_examples: 214971
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  dataset_size: 67264401
- config_name: en-es
  features:
  - name: english
    dtype: string
  - name: non_english
    dtype: string
  splits:
  - name: train
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    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
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    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