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conll05.py
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conll05.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 gzip
import tarfile
import numpy as np
from paddle.dataset.common import _check_exists_and_download
from paddle.io import Dataset
__all__ = []
DATA_URL = 'http://paddlemodels.bj.bcebos.com/conll05st/conll05st-tests.tar.gz'
DATA_MD5 = '387719152ae52d60422c016e92a742fc'
WORDDICT_URL = 'http://paddlemodels.bj.bcebos.com/conll05st%2FwordDict.txt'
WORDDICT_MD5 = 'ea7fb7d4c75cc6254716f0177a506baa'
VERBDICT_URL = 'http://paddlemodels.bj.bcebos.com/conll05st%2FverbDict.txt'
VERBDICT_MD5 = '0d2977293bbb6cbefab5b0f97db1e77c'
TRGDICT_URL = 'http://paddlemodels.bj.bcebos.com/conll05st%2FtargetDict.txt'
TRGDICT_MD5 = 'd8c7f03ceb5fc2e5a0fa7503a4353751'
EMB_URL = 'http://paddlemodels.bj.bcebos.com/conll05st%2Femb'
EMB_MD5 = 'bf436eb0faa1f6f9103017f8be57cdb7'
UNK_IDX = 0
class Conll05st(Dataset):
"""
Implementation of `Conll05st <https://www.cs.upc.edu/~srlconll/soft.html>`_
test dataset.
Note: only support download test dataset automatically for that
only test dataset of Conll05st is public.
Args:
data_file(str): path to data tar file, can be set None if
:attr:`download` is True. Default None
word_dict_file(str): path to word dictionary file, can be set None if
:attr:`download` is True. Default None
verb_dict_file(str): path to verb dictionary file, can be set None if
:attr:`download` is True. Default None
target_dict_file(str): path to target dictionary file, can be set None if
:attr:`download` is True. Default None
emb_file(str): path to embedding dictionary file, only used for
:code:`get_embedding` can be set None if :attr:`download` is
True. Default None
download(bool): whether to download dataset automatically if
:attr:`data_file` :attr:`word_dict_file` :attr:`verb_dict_file`
:attr:`target_dict_file` is not set. Default True
Returns:
Dataset: instance of conll05st dataset
Examples:
.. code-block:: python
import paddle
from paddle.text.datasets import Conll05st
class SimpleNet(paddle.nn.Layer):
def __init__(self):
super().__init__()
def forward(self, pred_idx, mark, label):
return paddle.sum(pred_idx), paddle.sum(mark), paddle.sum(label)
conll05st = Conll05st()
for i in range(10):
pred_idx, mark, label= conll05st[i][-3:]
pred_idx = paddle.to_tensor(pred_idx)
mark = paddle.to_tensor(mark)
label = paddle.to_tensor(label)
model = SimpleNet()
pred_idx, mark, label= model(pred_idx, mark, label)
print(pred_idx, mark, label)
"""
def __init__(
self,
data_file=None,
word_dict_file=None,
verb_dict_file=None,
target_dict_file=None,
emb_file=None,
download=True,
):
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, DATA_URL, DATA_MD5, 'conll05st', download
)
self.word_dict_file = word_dict_file
if self.word_dict_file is None:
assert (
download
), "word_dict_file is not set and downloading automatically is disabled"
self.word_dict_file = _check_exists_and_download(
word_dict_file,
WORDDICT_URL,
WORDDICT_MD5,
'conll05st',
download,
)
self.verb_dict_file = verb_dict_file
if self.verb_dict_file is None:
assert (
download
), "verb_dict_file is not set and downloading automatically is disabled"
self.verb_dict_file = _check_exists_and_download(
verb_dict_file,
VERBDICT_URL,
VERBDICT_MD5,
'conll05st',
download,
)
self.target_dict_file = target_dict_file
if self.target_dict_file is None:
assert (
download
), "target_dict_file is not set and downloading automatically is disabled"
self.target_dict_file = _check_exists_and_download(
target_dict_file,
TRGDICT_URL,
TRGDICT_MD5,
'conll05st',
download,
)
self.emb_file = emb_file
if self.emb_file is None:
assert (
download
), "emb_file is not set and downloading automatically is disabled"
self.emb_file = _check_exists_and_download(
emb_file, EMB_URL, EMB_MD5, 'conll05st', download
)
self.word_dict = self._load_dict(self.word_dict_file)
self.predicate_dict = self._load_dict(self.verb_dict_file)
self.label_dict = self._load_label_dict(self.target_dict_file)
# read dataset into memory
self._load_anno()
def _load_label_dict(self, filename):
d = {}
tag_dict = set()
with open(filename, 'r') as f:
for i, line in enumerate(f):
line = line.strip()
if line.startswith("B-"):
tag_dict.add(line[2:])
elif line.startswith("I-"):
tag_dict.add(line[2:])
index = 0
for tag in tag_dict:
d["B-" + tag] = index
index += 1
d["I-" + tag] = index
index += 1
d["O"] = index
return d
def _load_dict(self, filename):
d = {}
with open(filename, 'r') as f:
for i, line in enumerate(f):
d[line.strip()] = i
return d
def _load_anno(self):
tf = tarfile.open(self.data_file)
wf = tf.extractfile(
"conll05st-release/test.wsj/words/test.wsj.words.gz"
)
pf = tf.extractfile(
"conll05st-release/test.wsj/props/test.wsj.props.gz"
)
self.sentences = []
self.predicates = []
self.labels = []
with gzip.GzipFile(fileobj=wf) as words_file, gzip.GzipFile(
fileobj=pf
) as props_file:
sentences = []
labels = []
one_seg = []
for word, label in zip(words_file, props_file):
word = word.strip().decode()
label = label.strip().decode().split()
if len(label) == 0: # end of sentence
for i in range(len(one_seg[0])):
a_kind_lable = [x[i] for x in one_seg]
labels.append(a_kind_lable)
if len(labels) >= 1:
verb_list = []
for x in labels[0]:
if x != '-':
verb_list.append(x)
for i, lbl in enumerate(labels[1:]):
cur_tag = 'O'
is_in_bracket = False
lbl_seq = []
verb_word = ''
for l in lbl:
if l == '*' and not is_in_bracket:
lbl_seq.append('O')
elif l == '*' and is_in_bracket:
lbl_seq.append('I-' + cur_tag)
elif l == '*)':
lbl_seq.append('I-' + cur_tag)
is_in_bracket = False
elif l.find('(') != -1 and l.find(')') != -1:
cur_tag = l[1 : l.find('*')]
lbl_seq.append('B-' + cur_tag)
is_in_bracket = False
elif l.find('(') != -1 and l.find(')') == -1:
cur_tag = l[1 : l.find('*')]
lbl_seq.append('B-' + cur_tag)
is_in_bracket = True
else:
raise RuntimeError(
'Unexpected label: %s' % l
)
self.sentences.append(sentences)
self.predicates.append(verb_list[i])
self.labels.append(lbl_seq)
sentences = []
labels = []
one_seg = []
else:
sentences.append(word)
one_seg.append(label)
pf.close()
wf.close()
tf.close()
def __getitem__(self, idx):
sentence = self.sentences[idx]
predicate = self.predicates[idx]
labels = self.labels[idx]
sen_len = len(sentence)
verb_index = labels.index('B-V')
mark = [0] * len(labels)
if verb_index > 0:
mark[verb_index - 1] = 1
ctx_n1 = sentence[verb_index - 1]
else:
ctx_n1 = 'bos'
if verb_index > 1:
mark[verb_index - 2] = 1
ctx_n2 = sentence[verb_index - 2]
else:
ctx_n2 = 'bos'
mark[verb_index] = 1
ctx_0 = sentence[verb_index]
if verb_index < len(labels) - 1:
mark[verb_index + 1] = 1
ctx_p1 = sentence[verb_index + 1]
else:
ctx_p1 = 'eos'
if verb_index < len(labels) - 2:
mark[verb_index + 2] = 1
ctx_p2 = sentence[verb_index + 2]
else:
ctx_p2 = 'eos'
word_idx = [self.word_dict.get(w, UNK_IDX) for w in sentence]
ctx_n2_idx = [self.word_dict.get(ctx_n2, UNK_IDX)] * sen_len
ctx_n1_idx = [self.word_dict.get(ctx_n1, UNK_IDX)] * sen_len
ctx_0_idx = [self.word_dict.get(ctx_0, UNK_IDX)] * sen_len
ctx_p1_idx = [self.word_dict.get(ctx_p1, UNK_IDX)] * sen_len
ctx_p2_idx = [self.word_dict.get(ctx_p2, UNK_IDX)] * sen_len
pred_idx = [self.predicate_dict.get(predicate)] * sen_len
label_idx = [self.label_dict.get(w) for w in labels]
return (
np.array(word_idx),
np.array(ctx_n2_idx),
np.array(ctx_n1_idx),
np.array(ctx_0_idx),
np.array(ctx_p1_idx),
np.array(ctx_p2_idx),
np.array(pred_idx),
np.array(mark),
np.array(label_idx),
)
def __len__(self):
return len(self.sentences)
def get_dict(self):
"""
Get the word, verb and label dictionary of Wikipedia corpus.
Examples:
.. code-block:: python
from paddle.text.datasets import Conll05st
conll05st = Conll05st()
word_dict, predicate_dict, label_dict = conll05st.get_dict()
"""
return self.word_dict, self.predicate_dict, self.label_dict
def get_embedding(self):
"""
Get the embedding dictionary file.
Examples:
.. code-block:: python
from paddle.text.datasets import Conll05st
conll05st = Conll05st()
emb_file = conll05st.get_embedding()
"""
return self.emb_file