forked from PaddlePaddle/Paddle
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathwmt14.py
More file actions
168 lines (138 loc) · 5.41 KB
/
Copy pathwmt14.py
File metadata and controls
168 lines (138 loc) · 5.41 KB
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
# Copyright (c) 2016 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.
"""
WMT14 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://paddlepaddle.bj.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz and
parse training set and test set into paddle reader creators.
"""
from __future__ import print_function
import six
import tarfile
import gzip
import paddle.dataset.common
import paddle.compat as cpt
__all__ = [
'train',
'test',
'get_dict',
]
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'
# BLEU of this trained model is 26.92
URL_MODEL = 'http://paddlemodels.bj.bcebos.com/wmt%2Fwmt14.tgz'
MD5_MODEL = '0cb4a5366189b6acba876491c8724fa3'
START = "<s>"
END = "<e>"
UNK = "<unk>"
UNK_IDX = 2
def __read_to_dict(tar_file, dict_size):
def __to_dict(fd, size):
out_dict = dict()
for line_count, line in enumerate(fd):
if line_count < size:
out_dict[cpt.to_text(line.strip())] = line_count
else:
break
return out_dict
with tarfile.open(tar_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
src_dict = __to_dict(f.extractfile(names[0]), dict_size)
names = [
each_item.name for each_item in f
if each_item.name.endswith("trg.dict")
]
assert len(names) == 1
trg_dict = __to_dict(f.extractfile(names[0]), dict_size)
return src_dict, trg_dict
def reader_creator(tar_file, file_name, dict_size):
def reader():
src_dict, trg_dict = __read_to_dict(tar_file, dict_size)
with tarfile.open(tar_file, mode='r') as f:
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 = cpt.to_text(line)
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 = [
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 = [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 + [trg_dict[END]]
trg_ids = [trg_dict[START]] + trg_ids
yield src_ids, trg_ids, trg_ids_next
return reader
def train(dict_size):
"""
WMT14 training set creator.
It returns a reader creator, each sample in the reader is source language
word ID sequence, target language word ID sequence and next word ID
sequence.
:return: Training reader creator
:rtype: callable
"""
return reader_creator(
paddle.dataset.common.download(URL_TRAIN, 'wmt14', MD5_TRAIN),
'train/train', dict_size)
def test(dict_size):
"""
WMT14 test set creator.
It returns a reader creator, each sample in the reader is source language
word ID sequence, target language word ID sequence and next word ID
sequence.
:return: Test reader creator
:rtype: callable
"""
return reader_creator(
paddle.dataset.common.download(URL_TRAIN, 'wmt14', MD5_TRAIN),
'test/test', dict_size)
def gen(dict_size):
return reader_creator(
paddle.dataset.common.download(URL_TRAIN, 'wmt14', MD5_TRAIN),
'gen/gen', dict_size)
def get_dict(dict_size, reverse=True):
# if reverse = False, return dict = {'a':'001', 'b':'002', ...}
# else reverse = true, return dict = {'001':'a', '002':'b', ...}
tar_file = paddle.dataset.common.download(URL_TRAIN, 'wmt14', MD5_TRAIN)
src_dict, trg_dict = __read_to_dict(tar_file, dict_size)
if reverse:
src_dict = {v: k for k, v in six.iteritems(src_dict)}
trg_dict = {v: k for k, v in six.iteritems(trg_dict)}
return src_dict, trg_dict
def fetch():
paddle.dataset.common.download(URL_TRAIN, 'wmt14', MD5_TRAIN)
paddle.dataset.common.download(URL_MODEL, 'wmt14', MD5_MODEL)