class paddle.text.datasets. WMT14 ( data_file=None, mode='train', dict_size=- 1, download=True ) [source]

Implementation of WMT14 test dataset. The original WMT14 dataset is too large and a small set of data for set is provided. This module will download dataset from http://paddlemodels.bj.bcebos.com/wmt/wmt14.tgz .

  • data_file (str) – path to data tar file, can be set None if download is True. Default None

  • mode (str) – ‘train’, ‘test’ or ‘gen’. Default ‘train’

  • dict_size (int) – word dictionary size. Default -1.

  • download (bool) – whether to download dataset automatically if data_file is not set. Default True


instance of WMT14 dataset

Return type



import paddle
from paddle.text.datasets import WMT14

class SimpleNet(paddle.nn.Layer):
    def __init__(self):
        super(SimpleNet, self).__init__()

    def forward(self, src_ids, trg_ids, trg_ids_next):
        return paddle.sum(src_ids), paddle.sum(trg_ids), paddle.sum(trg_ids_next)

wmt14 = WMT14(mode='train', dict_size=50)

for i in range(10):
    src_ids, trg_ids, trg_ids_next = wmt14[i]
    src_ids = paddle.to_tensor(src_ids)
    trg_ids = paddle.to_tensor(trg_ids)
    trg_ids_next = paddle.to_tensor(trg_ids_next)

    model = SimpleNet()
    src_ids, trg_ids, trg_ids_next = model(src_ids, trg_ids, trg_ids_next)
    print(src_ids.numpy(), trg_ids.numpy(), trg_ids_next.numpy())
get_dict ( reverse=False )

Get the source and target dictionary.


reverse (bool) – wether to reverse key and value in dictionary, i.e. key: value to value: key.


Two dictionaries, the source and target dictionary.


from paddle.text.datasets import WMT14
wmt14 = WMT14(mode='train', dict_size=50)
src_dict, trg_dict = wmt14.get_dict()