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wmt14.py
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wmt14.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tarfile
import numpy as np
from paddle.dataset.common import _check_exists_and_download
from paddle.io import Dataset
__all__ = []
URL_DEV_TEST = (
'http://www-lium.univ-lemans.fr/~schwenk/'
'cslm_joint_paper/data/dev+test.tgz'
)
MD5_DEV_TEST = '7d7897317ddd8ba0ae5c5fa7248d3ff5'
# this is a small set of data for test. The original data is too large and
# will be add later.
URL_TRAIN = 'http://paddlemodels.bj.bcebos.com/wmt/wmt14.tgz'
MD5_TRAIN = '0791583d57d5beb693b9414c5b36798c'
START = "<s>"
END = "<e>"
UNK = "<unk>"
UNK_IDX = 2
class WMT14(Dataset):
"""
Implementation of `WMT14 <http://www.statmt.org/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 .
Args:
data_file(str): path to data tar file, can be set None if
:attr:`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
:attr:`data_file` is not set. Default True
Returns:
Dataset: Instance of WMT14 dataset
- src_ids (np.array) - The sequence of token ids of source language.
- trg_ids (np.array) - The sequence of token ids of target language.
- trg_ids_next (np.array) - The next sequence of token ids of target language.
Examples:
.. code-block:: python
import paddle
from paddle.text.datasets import WMT14
class SimpleNet(paddle.nn.Layer):
def __init__(self):
super().__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())
"""
def __init__(
self, data_file=None, mode='train', dict_size=-1, download=True
):
assert mode.lower() in [
'train',
'test',
'gen',
], f"mode should be 'train', 'test' or 'gen', but got {mode}"
self.mode = mode.lower()
self.data_file = data_file
if self.data_file is None:
assert (
download
), "data_file is not set and downloading automatically is disabled"
self.data_file = _check_exists_and_download(
data_file, URL_TRAIN, MD5_TRAIN, 'wmt14', download
)
# read dataset into memory
assert dict_size > 0, "dict_size should be set as positive number"
self.dict_size = dict_size
self._load_data()
def _load_data(self):
def __to_dict(fd, size):
out_dict = {}
for line_count, line in enumerate(fd):
if line_count < size:
out_dict[line.strip().decode()] = line_count
else:
break
return out_dict
self.src_ids = []
self.trg_ids = []
self.trg_ids_next = []
with tarfile.open(self.data_file, mode='r') as f:
names = [
each_item.name
for each_item in f
if each_item.name.endswith("src.dict")
]
assert len(names) == 1
self.src_dict = __to_dict(f.extractfile(names[0]), self.dict_size)
names = [
each_item.name
for each_item in f
if each_item.name.endswith("trg.dict")
]
assert len(names) == 1
self.trg_dict = __to_dict(f.extractfile(names[0]), self.dict_size)
file_name = f"{self.mode}/{self.mode}"
names = [
each_item.name
for each_item in f
if each_item.name.endswith(file_name)
]
for name in names:
for line in f.extractfile(name):
line = line.decode()
line_split = line.strip().split('\t')
if len(line_split) != 2:
continue
src_seq = line_split[0] # one source sequence
src_words = src_seq.split()
src_ids = [
self.src_dict.get(w, UNK_IDX)
for w in [START] + src_words + [END]
]
trg_seq = line_split[1] # one target sequence
trg_words = trg_seq.split()
trg_ids = [self.trg_dict.get(w, UNK_IDX) for w in trg_words]
# remove sequence whose length > 80 in training mode
if len(src_ids) > 80 or len(trg_ids) > 80:
continue
trg_ids_next = trg_ids + [self.trg_dict[END]]
trg_ids = [self.trg_dict[START]] + trg_ids
self.src_ids.append(src_ids)
self.trg_ids.append(trg_ids)
self.trg_ids_next.append(trg_ids_next)
def __getitem__(self, idx):
return (
np.array(self.src_ids[idx]),
np.array(self.trg_ids[idx]),
np.array(self.trg_ids_next[idx]),
)
def __len__(self):
return len(self.src_ids)
def get_dict(self, reverse=False):
"""
Get the source and target dictionary.
Args:
reverse (bool): wether to reverse key and value in dictionary,
i.e. key: value to value: key.
Returns:
Two dictionaries, the source and target dictionary.
Examples:
.. code-block:: python
from paddle.text.datasets import WMT14
wmt14 = WMT14(mode='train', dict_size=50)
src_dict, trg_dict = wmt14.get_dict()
"""
src_dict, trg_dict = self.src_dict, self.trg_dict
if reverse:
src_dict = {v: k for k, v in src_dict.items()}
trg_dict = {v: k for k, v in trg_dict.items()}
return src_dict, trg_dict