- Description:
Translate dataset based on the data from statmt.org.
Versions exists for the different years using a combination of multiple data
sources. The base wmt_translate
allows you to create your own config to choose
your own data/language pair by creating a custom tfds.translate.wmt.WmtConfig
.
config = tfds.translate.wmt.WmtConfig(
version="0.0.1",
language_pair=("fr", "de"),
subsets={
tfds.Split.TRAIN: ["commoncrawl_frde"],
tfds.Split.VALIDATION: ["euelections_dev2019"],
},
)
builder = tfds.builder("wmt_translate", config=config)
Source code:
tfds.translate.Wmt16Translate
Versions:
1.0.0
(default): No release notes.
Dataset size:
Unknown size
Manual download instructions: This dataset requires you to download the source data manually into
download_config.manual_dir
(defaults to~/tensorflow_datasets/downloads/manual/
):
Some of the wmt configs here, require a manual download. Please look into wmt.py to see the exact path (and file name) that has to be downloaded.Auto-cached (documentation): Unknown
Citation:
@InProceedings{bojar-EtAl:2016:WMT1,
author = {Bojar, Ond
{r}ej and Chatterjee, Rajen and Federmann, Christian and Graham, Yvette and Haddow, Barry and Huck, Matthias and Jimeno Yepes, Antonio and Koehn, Philipp and Logacheva, Varvara and Monz, Christof and Negri, Matteo and Neveol, Aurelie and Neves, Mariana and Popel, Martin and Post, Matt and Rubino, Raphael and Scarton, Carolina and Specia, Lucia and Turchi, Marco and Verspoor, Karin and Zampieri, Marcos},
title = {Findings of the 2016 Conference on Machine Translation},
booktitle = {Proceedings of the First Conference on Machine Translation},
month = {August},
year = {2016},
address = {Berlin, Germany},
publisher = {Association for Computational Linguistics},
pages = {131--198},
url = {http://www.aclweb.org/anthology/W/W16/W16-2301}
}
- Figure (tfds.show_examples): Not supported.
wmt16_translate/cs-en (default config)
Config description: WMT 2016 cs-en translation task dataset.
Download size:
1.57 GiB
Splits:
Split | Examples |
---|---|
'test' |
2,999 |
'train' |
52,335,651 |
'validation' |
2,656 |
- Features:
Translation({
'cs': Text(shape=(), dtype=tf.string),
'en': Text(shape=(), dtype=tf.string),
})
Supervised keys (See
as_supervised
doc):('cs', 'en')
Examples (tfds.as_dataframe):
wmt16_translate/de-en
Config description: WMT 2016 de-en translation task dataset.
Download size:
1.57 GiB
Splits:
Split | Examples |
---|---|
'test' |
2,999 |
'train' |
4,548,885 |
'validation' |
2,169 |
- Features:
Translation({
'de': Text(shape=(), dtype=tf.string),
'en': Text(shape=(), dtype=tf.string),
})
Supervised keys (See
as_supervised
doc):('de', 'en')
Examples (tfds.as_dataframe):
wmt16_translate/fi-en
Config description: WMT 2016 fi-en translation task dataset.
Download size:
260.51 MiB
Splits:
Split | Examples |
---|---|
'test' |
6,000 |
'train' |
2,073,394 |
'validation' |
1,370 |
- Features:
Translation({
'en': Text(shape=(), dtype=tf.string),
'fi': Text(shape=(), dtype=tf.string),
})
Supervised keys (See
as_supervised
doc):('fi', 'en')
Examples (tfds.as_dataframe):
wmt16_translate/ro-en
Config description: WMT 2016 ro-en translation task dataset.
Download size:
273.83 MiB
Splits:
Split | Examples |
---|---|
'test' |
1,999 |
'train' |
610,320 |
'validation' |
1,999 |
- Features:
Translation({
'en': Text(shape=(), dtype=tf.string),
'ro': Text(shape=(), dtype=tf.string),
})
Supervised keys (See
as_supervised
doc):('ro', 'en')
Examples (tfds.as_dataframe):
wmt16_translate/ru-en
Config description: WMT 2016 ru-en translation task dataset.
Download size:
993.38 MiB
Splits:
Split | Examples |
---|---|
'test' |
2,998 |
'train' |
2,516,162 |
'validation' |
2,818 |
- Features:
Translation({
'en': Text(shape=(), dtype=tf.string),
'ru': Text(shape=(), dtype=tf.string),
})
Supervised keys (See
as_supervised
doc):('ru', 'en')
Examples (tfds.as_dataframe):
wmt16_translate/tr-en
Config description: WMT 2016 tr-en translation task dataset.
Download size:
59.32 MiB
Splits:
Split | Examples |
---|---|
'test' |
3,000 |
'train' |
205,756 |
'validation' |
1,001 |
- Features:
Translation({
'en': Text(shape=(), dtype=tf.string),
'tr': Text(shape=(), dtype=tf.string),
})
Supervised keys (See
as_supervised
doc):('tr', 'en')
Examples (tfds.as_dataframe):