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# Copyright 2018 The TensorFlow 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.
# ==============================================================================
"""
Preprocesses pretrained word embeddings, creates dev sets for tasks without a
provided one, and figures out the set of output classes for each task.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import random
from base import configure
from base import embeddings
from base import utils
from task_specific.word_level import word_level_data
def main(data_dir='./data'):
random.seed(0)
utils.log("BUILDING WORD VOCABULARY/EMBEDDINGS")
for pretrained in ['glove.6B.300d.txt']:
config = configure.Config(data_dir=data_dir,
for_preprocessing=True,
pretrained_embeddings=pretrained,
word_embedding_size=300)
embeddings.PretrainedEmbeddingLoader(config).build()
utils.log("CONSTRUCTING DEV SETS")
for task_name in ["chunk"]:
# chunking does not come with a provided dev split, so create one by
# selecting a random subset of the data
config = configure.Config(data_dir=data_dir,
for_preprocessing=True)
task_data_dir = os.path.join(config.raw_data_topdir, task_name) + '/'
train_sentences = word_level_data.TaggedDataLoader(
config, task_name, False).get_labeled_sentences("train")
random.shuffle(train_sentences)
write_sentences(task_data_dir + 'train_subset.txt', train_sentences[1500:])
write_sentences(task_data_dir + 'dev.txt', train_sentences[:1500])
utils.log("WRITING LABEL MAPPINGS")
for task_name in ["chunk"]:
for i, label_encoding in enumerate(["BIOES"]):
config = configure.Config(data_dir=data_dir,
for_preprocessing=True,
label_encoding=label_encoding)
token_level = task_name in ["ccg", "pos", "depparse"]
loader = word_level_data.TaggedDataLoader(config, task_name, token_level)
if token_level:
if i != 0:
continue
utils.log("WRITING LABEL MAPPING FOR", task_name.upper())
else:
utils.log(" Writing label mapping for", task_name.upper(),
label_encoding)
utils.log(" ", len(loader.label_mapping), "classes")
utils.write_cpickle(loader.label_mapping,
loader.label_mapping_path)
def write_sentences(fname, sentences):
with open(fname, 'w') as f:
for words, tags in sentences:
for word, tag in zip(words, tags):
f.write(word + " " + tag + "\n")
f.write("\n")
if __name__ == '__main__':
main()
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