TF 2.0 is out! Get hands-on practice at TF World, Oct 28-31. Use code TF20 for 20% off select passes. Register now

wmt15_translate

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)

wmt15_translate is configured with tfds.translate.wmt.WmtConfig and has the following configurations predefined (defaults to the first one):

  • cs-en (v0.0.4) (Size: 1.62 GiB): WMT 2015 cs-en translation task dataset.

  • de-en (v0.0.4) (Size: 1.62 GiB): WMT 2015 de-en translation task dataset.

  • fi-en (v0.0.4) (Size: 260.51 MiB): WMT 2015 fi-en translation task dataset.

  • fr-en (v0.0.4) (Size: 6.24 GiB): WMT 2015 fr-en translation task dataset.

  • ru-en (v0.0.4) (Size: 1.02 GiB): WMT 2015 ru-en translation task dataset.

  • cs-en.subwords8k (v0.0.4) (Size: 1.62 GiB): WMT 2015 cs-en translation task dataset with subword encoding.

  • de-en.subwords8k (v0.0.4) (Size: 1.62 GiB): WMT 2015 de-en translation task dataset with subword encoding.

  • fi-en.subwords8k (v0.0.4) (Size: 260.51 MiB): WMT 2015 fi-en translation task dataset with subword encoding.

  • fr-en.subwords8k (v0.0.4) (Size: 6.24 GiB): WMT 2015 fr-en translation task dataset with subword encoding.

  • ru-en.subwords8k (v0.0.4) (Size: 1.02 GiB): WMT 2015 ru-en translation task dataset with subword encoding.

wmt15_translate/cs-en

WMT 2015 cs-en translation task dataset.

Versions:

  • 0.0.4 (default):

Statistics

Split Examples
ALL 62,208,679
TRAIN 62,203,020
VALIDATION 3,003
TEST 2,656

Features

Translation({
    'cs': Text(shape=(), dtype=tf.string),
    'en': Text(shape=(), dtype=tf.string),
})

Urls

Supervised keys (for as_supervised=True)

(u'cs', u'en')

wmt15_translate/de-en

WMT 2015 de-en translation task dataset.

Versions:

  • 0.0.4 (default):

Statistics

Split Examples
ALL 4,528,170
TRAIN 4,522,998
VALIDATION 3,003
TEST 2,169

Features

Translation({
    'de': Text(shape=(), dtype=tf.string),
    'en': Text(shape=(), dtype=tf.string),
})

Urls

Supervised keys (for as_supervised=True)

(u'de', u'en')

wmt15_translate/fi-en

WMT 2015 fi-en translation task dataset.

Versions:

  • 0.0.4 (default):

Statistics

Split Examples
ALL 2,076,264
TRAIN 2,073,394
VALIDATION 1,500
TEST 1,370

Features

Translation({
    'en': Text(shape=(), dtype=tf.string),
    'fi': Text(shape=(), dtype=tf.string),
})

Urls

Supervised keys (for as_supervised=True)

(u'fi', u'en')

wmt15_translate/fr-en

WMT 2015 fr-en translation task dataset.

Versions:

  • 0.0.4 (default):

Statistics

Split Examples
ALL 40,859,301
TRAIN 40,853,298
VALIDATION 4,503
TEST 1,500

Features

Translation({
    'en': Text(shape=(), dtype=tf.string),
    'fr': Text(shape=(), dtype=tf.string),
})

Urls

Supervised keys (for as_supervised=True)

(u'fr', u'en')

wmt15_translate/ru-en

WMT 2015 ru-en translation task dataset.

Versions:

  • 0.0.4 (default):

Statistics

Split Examples
ALL 2,500,902
TRAIN 2,495,081
VALIDATION 3,003
TEST 2,818

Features

Translation({
    'en': Text(shape=(), dtype=tf.string),
    'ru': Text(shape=(), dtype=tf.string),
})

Urls

Supervised keys (for as_supervised=True)

(u'ru', u'en')

wmt15_translate/cs-en.subwords8k

WMT 2015 cs-en translation task dataset with subword encoding.

Versions:

  • 0.0.4 (default):

Statistics

Split Examples
ALL 62,208,679
TRAIN 62,203,020
VALIDATION 3,003
TEST 2,656

Features

Translation({
    'cs': Text(shape=(None,), dtype=tf.int64, encoder=<SubwordTextEncoder vocab_size=8193>),
    'en': Text(shape=(None,), dtype=tf.int64, encoder=<SubwordTextEncoder vocab_size=8155>),
})

Urls

Supervised keys (for as_supervised=True)

(u'cs', u'en')

wmt15_translate/de-en.subwords8k

WMT 2015 de-en translation task dataset with subword encoding.

Versions:

  • 0.0.4 (default):

Statistics

Split Examples
ALL 4,528,170
TRAIN 4,522,998
VALIDATION 3,003
TEST 2,169

Features

Translation({
    'de': Text(shape=(None,), dtype=tf.int64, encoder=<SubwordTextEncoder vocab_size=8270>),
    'en': Text(shape=(None,), dtype=tf.int64, encoder=<SubwordTextEncoder vocab_size=8212>),
})

Urls

Supervised keys (for as_supervised=True)

(u'de', u'en')

wmt15_translate/fi-en.subwords8k

WMT 2015 fi-en translation task dataset with subword encoding.

Versions:

  • 0.0.4 (default):

Statistics

Split Examples
ALL 2,076,264
TRAIN 2,073,394
VALIDATION 1,500
TEST 1,370

Features

Translation({
    'en': Text(shape=(None,), dtype=tf.int64, encoder=<SubwordTextEncoder vocab_size=8217>),
    'fi': Text(shape=(None,), dtype=tf.int64, encoder=<SubwordTextEncoder vocab_size=8113>),
})

Urls

Supervised keys (for as_supervised=True)

(u'fi', u'en')

wmt15_translate/fr-en.subwords8k

WMT 2015 fr-en translation task dataset with subword encoding.

Versions:

  • 0.0.4 (default):

Statistics

Split Examples
ALL 40,859,301
TRAIN 40,853,298
VALIDATION 4,503
TEST 1,500

Features

Translation({
    'en': Text(shape=(None,), dtype=tf.int64, encoder=<SubwordTextEncoder vocab_size=8183>),
    'fr': Text(shape=(None,), dtype=tf.int64, encoder=<SubwordTextEncoder vocab_size=8133>),
})

Urls

Supervised keys (for as_supervised=True)

(u'fr', u'en')

wmt15_translate/ru-en.subwords8k

WMT 2015 ru-en translation task dataset with subword encoding.

Versions:

  • 0.0.4 (default):

Statistics

Split Examples
ALL 2,500,902
TRAIN 2,495,081
VALIDATION 3,003
TEST 2,818

Features

Translation({
    'en': Text(shape=(None,), dtype=tf.int64, encoder=<SubwordTextEncoder vocab_size=8194>),
    'ru': Text(shape=(None,), dtype=tf.int64, encoder=<SubwordTextEncoder vocab_size=8180>),
})

Urls

Supervised keys (for as_supervised=True)

(u'ru', u'en')

Citation

@InProceedings{bojar-EtAl:2015:WMT,
  author    = {Bojar, Ond
{r}ej  and  Chatterjee, Rajen  and  Federmann, Christian  and  Haddow, Barry  and  Huck, Matthias  and  Hokamp, Chris  and  Koehn, Philipp  and  Logacheva, Varvara  and  Monz, Christof  and  Negri, Matteo  and  Post, Matt  and  Scarton, Carolina  and  Specia, Lucia  and  Turchi, Marco},
  title     = {Findings of the 2015 Workshop on Statistical Machine Translation},
  booktitle = {Proceedings of the Tenth Workshop on Statistical Machine Translation},
  month     = {September},
  year      = {2015},
  address   = {Lisbon, Portugal},
  publisher = {Association for Computational Linguistics},
  pages     = {1--46},
  url       = {http://aclweb.org/anthology/W15-3001}
}