Datasets:
File size: 6,121 Bytes
1e62901 74091a0 1e62901 f38e131 58f6593 1e62901 58f6593 e2a5e5f 07741d6 bd90569 95c3626 3af0d6b 438d524 e1d4efe 8392615 1e62901 f38e131 1e62901 58f6593 e2a5e5f 07741d6 bd90569 95c3626 3af0d6b 438d524 e1d4efe 8392615 1e62901 74091a0 e75ea50 74091a0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 |
---
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)
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
```
* Collection strategy: Combining all other subsets from this dataset.
* Deduplified: No
### `en-...` subsets
* Columns: "english", "non_english"
* Column types: `str`, `str`
* Examples:
```python
```
* 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 |