def load(self):
"""Loads vocabulary, processing functions and embeddings
Supposes that build_data.py has been run successfully and that
the corresponding files have been created (vocab and trimmed GloVe
vectors)
"""
# 1. vocabulary
self.vocab_words = load_vocab(self.filename_words)
self.vocab_tags = load_vocab(self.filename_tags)
self.vocab_chars = load_vocab(self.filename_chars)
self.nwords = len(self.vocab_words)
self.nchars = len(self.vocab_chars)
self.ntags = len(self.vocab_tags)
# 2. get processing functions that map str -> id
self.processing_word = get_processing_word(self.vocab_words,
self.vocab_chars,
lowercase=True,
chars=self.use_chars)
self.processing_tag = get_processing_word(self.vocab_tags,
lowercase=False,
allow_unk=False)
# 3. get pre-trained embeddings
self.embeddings = (get_trimmed_glove_vectors(self.filename_trimmed)
if self.use_pretrained else None)
def main():
# get config and processing of words
config = Config(load=False)
processing_word = get_processing_word(lowercase=True)
processing_pos = get_processing_word()
processing_chunk = get_processing_word()
# Generators
dev = CoNLLDataset(config.filename_dev, processing_word, processing_pos,
processing_chunk)
test = CoNLLDataset(config.filename_test, processing_word, processing_pos,
processing_chunk)
train = CoNLLDataset(config.filename_train, processing_word,
processing_pos, processing_chunk)
# Build Word and Tag vocab
vocab_words, vocab_tags, vocab_poses, vocab_chunks = get_vocabs(
[train, dev, test])
vocab_glove = get_glove_vocab(config.filename_glove)
vocab = [i for i in vocab_words if i in vocab_glove]
vocab.append(UNK)
vocab.append(NUM)
vocab.append("$pad$")
vocab_poses.append("$pad$")
vocab_chunks.append("$pad$")
vocab_tags.append("$pad$")
# Save vocab
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
write_vocab(vocab_poses, config.filename_poses)
write_vocab(vocab_chunks, config.filename_chunks)
# Trim GloVe Vectors
vocab = load_vocab(config.filename_words)
print(len(vocab))
export_trimmed_glove_vectors(vocab, config.filename_glove,
config.filename_trimmed, config.dim_word)
vocab = load_vocab(config.filename_poses)
export_trimed_ont_hot_vectors(vocab, config.filename_pos_trimmed)
vocab = load_vocab(config.filename_chunks)
export_trimed_ont_hot_vectors(vocab, config.filename_chunk_trimmed)
# Build and save char vocab
train = CoNLLDataset(config.filename_train)
vocab_chars = get_char_vocab(train)
vocab_chars.append("$pad$")
write_vocab(vocab_chars, config.filename_chars)
def main():
"""Procedure to build data
You MUST RUN this procedure. It iterates over the whole dataset (train,
dev and test) and extract the vocabularies in terms of words, tags, and
characters. Having built the vocabularies it writes them in a file. The
writing of vocabulary in a file assigns an id (the line #) to each word.
It then extract the relevant GloVe vectors and stores them in a np array
such that the i-th entry corresponds to the i-th word in the vocabulary.
Args:
config: (instance of Config) has attributes like hyper-params...
"""
# get config and processing of words
config = Config(load=False)
processing_word = get_processing_word(lowercase=True) # 把字符全部小写，数字替换成NUM
# Generators
to_be_add = CoNLLDataset1(config.filename_test,
processing_word) # 返回一句话（words），和标签tags
# Build Word and Tag vocab
vocab_words, _ = get_vocabs([to_be_add])
vocab_glove = get_glove_vocab(config.filename_glove) # glove词表
words_have_vec = vocab_words & vocab_glove
vocab_words_and_entity = entity2vocab(datasets=[to_be_add],
vocab=words_have_vec)
vocab_in_file = set(load_vocab(config.filename_words))
vocab_words_to_be_add = vocab_words_and_entity - vocab_in_file
if len(vocab_words_to_be_add) != 0:
with open(config.filename_words, 'a') as f:
for i, vocab_word in enumerate(vocab_words_to_be_add):
f.write('\n{}'.format(vocab_word))
# Trim GloVe Vectors
vocab = load_vocab(config.filename_words) # 得到dict类型的vocab：{word:index}
# 针对vocab，生成numpy的embedding文件，包含一个矩阵，对应词嵌入
export_trimmed_glove_vectors(vocab, config.filename_glove,
config.filename_trimmed, config.dim_word)
def main():
# get config and processing of words
config = Config(load=False)
processing_word = get_processing_word(lowercase=True)
# Generators
dev = CoNLLDataset(config.filename_dev, processing_word)
test = CoNLLDataset(config.filename_test, processing_word)
train = CoNLLDataset(config.filename_train, processing_word)
# Build Word and Tag vocab
vocab_words, vocab_tags = get_vocabs([train, dev, test])
vocab_glove = get_glove_vocab(config.filename_glove)
vocab = vocab_words & vocab_glove
vocab.add(UNK)
vocab.add(NUM)
# Save vocab
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
# Trim GloVe Vectors
vocab = load_vocab(config.filename_words)
export_trimmed_glove_vectors(vocab, config.filename_glove,
config.filename_trimmed, config.dim_word)
# Build and save char vocab
train = CoNLLDataset(config.filename_train)
vocab_chars = get_char_vocab(train)
write_vocab(vocab_chars, config.filename_chars)
def main():
"""Procedure to build data
You MUST RUN this procedure. It iterates over the whole dataset (train,
dev and test) and extract the vocabularies in terms of words, tags, and
characters. Having built the vocabularies it writes them in a file. The
writing of vocabulary in a file assigns an id (the line #) to each word.
It then extract the relevant GloVe vectors and stores them in a np array
such that the i-th entry corresponds to the i-th word in the vocabulary.
Args:
config: (instance of Config) has attributes like hyper-params...
"""
# get config and processing of words
config = Config(load=False)
processing_word = get_processing_word(lowercase=True) # 把字符全部小写，数字替换成NUM
# Generators
dev = CoNLLDataset(config.filename_dev,
processing_word) # 创建一个生成器对象，每一次迭代产生tuple （words，tags）
test = CoNLLDataset(config.filename_test,
processing_word) # 返回一句话（words），和标签tags
train = CoNLLDataset(config.filename_train, processing_word)
#进一步处理数据
# Build Word and Tag vocab
vocab_words, vocab_tags = get_vocabs([train, dev, test]) # word词表， tags表
print(len(vocab_words))
vocab_glove = get_glove_vocab(config.filename_glove) # glove词表
vocab = vocab_words & vocab_glove # & 求交集 set，都是集合
vocab.add(UNK)
vocab.add(NUM) # 手动添加
print("len of vocab without entity: ", len(vocab))
# vocab_entity = entity2vocab(datasets=[train, dev, test])
# vocab.update(vocab_entity)
# vocab = entity2vocab(datasets=[train, dev], vocab=vocab)
# Save vocab
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
# Trim GloVe Vectors
vocab = load_vocab(config.filename_words) # 得到dict类型的vocab：{word:index}
# 针对vocab，生成numpy的embedding文件，包含一个矩阵，对应词嵌入
export_trimmed_glove_vectors(vocab, config.filename_glove,
config.filename_trimmed, config.dim_word)
# Build and save char vocab 生成字母表, 这里没用到小写化的东西。只有文件本身。
train = CoNLLDataset(config.filename_train)
vocab_chars = get_char_vocab(train)
write_vocab(vocab_chars, config.filename_chars)
def main():
"""Procedure to build data
You MUST RUN this procedure. It iterates over the whole dataset (train,
dev and test) and extract the vocabularies in terms of words, tags, and
characters. Having built the vocabularies it writes them in a file. The
writing of vocabulary in a file assigns an id (the line #) to each word.
It then extract the relevant GloVe vectors and stores them in a np array
such that the i-th entry corresponds to the i-th word in the vocabulary.
Args:
config: (instance of Config) has attributes like hyper-params...
"""
# get config and processing of words
config = Config(load=False)
processing_word = get_processing_word(lowercase=True)
# Generators
dev = CoNLLDataset(config.filename_dev, processing_word, task=config.task)
test = CoNLLDataset(config.filename_test,
processing_word,
task=config.task)
train = CoNLLDataset(config.filename_train,
processing_word,
task=config.task)
# Build Word and Tag vocab
vocab_words, vocab_tags = get_vocabs([train, dev, test])
vocab_glove = get_glove_vocab(config.filename_glove)
#TODO get word2vec vocab too
vocab = vocab_words & vocab_glove
vocab.add(UNK)
vocab.add(NUM)
# Save word and tag vocab
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
# write and trim GloVe and word2vec Vectors
vocab = load_vocab(config.filename_words)
write_word2vec_to_txtfile(config.path_to_word2vec_bin_file,
config.filename_word2vec)
export_trimmed_word2vec_vectors(vocab, config.filename_word2vec,
config.trimmed_word2vec_filename,
config.dim_word)
export_trimmed_glove_vectors(vocab, config.filename_glove,
config.trimmed_glove_filename,
config.dim_word)
# Build and save char vocab
train = CoNLLDataset(config.filename_train)
vocab_chars = get_char_vocab(train)
write_vocab(vocab_chars, config.filename_chars)
def main():
"""Procedure to build data
You MUST RUN this procedure. It iterates over the whole dataset (train,
dev and test) and extract the vocabularies in terms of words and
characters. Having built the vocabularies it writes them in a file. The
writing of vocabulary in a file assigns an id (the line #) to each word.
It then extract the relevant GloVe vectors and stores them in a np array
such that the i-th entry corresponds to the i-th word in the vocabulary.
Args:
config: (instance of Config) has attributes like hyper-params...
"""
# get config and processing of words
config = Config(load=False)
pw_function = get_processing_word(lowercase=True)
# Generators
dev = Dataset(config.filename_dev, processing_word=pw_function)
test = Dataset(config.filename_test, processing_word=pw_function)
train = Dataset(config.filename_train, processing_word=pw_function)
# Build Words
vocab_words = get_vocabs([train, dev, test])
vocab_glove = get_glove_vocab(config.filename_glove)
vocab = vocab_words & vocab_glove
vocab = list(vocab)
pronouns_in_vocab = move_pronouns(vocab)
write_vocab(pronouns_in_vocab, config.filename_pronouns)
# add START, STOP, PAD, UNK and NUM tokens into the list
add_special_tokens(vocab)
assert PAD_TOKEN == vocab[0]
assert UNKNOWN_TOKEN in vocab
# Save vocab
write_vocab(vocab, config.filename_words)
# Trim GloVe Vectors
vocab, _ = load_vocab(config.filename_words)
export_trimmed_glove_vectors(vocab, config.filename_glove,
config.filename_trimmed, config.dim_word)
# Build and save char vocab
train = Dataset(config.filename_train)
vocab_chars = get_char_vocab(train)
vocab_chars = list(vocab_chars)
vocab_chars.insert(0, PAD_TOKEN)
write_vocab(vocab_chars, config.filename_chars)
def main():
"""Procedure to build data
You MUST RUN this procedure. It iterates over the whole dataset (train,
dev and test) and extract the vocabularies in terms of words, tags, and
characters. Having built the vocabularies it writes them in a file. The
writing of vocabulary in a file assigns an id (the line #) to each word.
It then extract the relevant GloVe vectors and stores them in a np array
such that the i-th entry corresponds to the i-th word in the vocabulary.
Args:
config: (instance of Config) has attributes like hyper-params...
"""
# get config and processing of words
config = Config(load=False)
if config.task == 'pos':
print("USING POS")
config.filename_train = "data/train.pos" # test
config.filename_dev = "data/dev.pos"
config.filename_test = "data/test.pos"
else:
print("USING NER")
processing_word = get_processing_word(lowercase=True)
# Generators
dev = CoNLLDataset(config.filename_dev, processing_word)
test = CoNLLDataset(config.filename_test, processing_word)
train = CoNLLDataset(config.filename_train, processing_word)
# Build Word and Tag vocab
vocab_words, vocab_tags = get_vocabs([train, dev, test])
vocab_glove = get_glove_vocab(config.filename_glove)
vocab = vocab_words & vocab_glove
vocab.add(UNK)
vocab.add(NUM)
# Save vocab
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
# Trim GloVe Vectors
vocab = load_vocab(config.filename_words)
export_trimmed_glove_vectors(vocab, config.filename_glove,
config.filename_trimmed, config.dim_word)
# Build and save char vocab
train = CoNLLDataset(config.filename_train)
vocab_chars = get_char_vocab(train)
write_vocab(vocab_chars, config.filename_chars)
def main():
"""Procedure to build data
You MUST RUN this procedure. It iterates over the whole dataset (train,
dev and test) and extract the vocabularies in terms of words, tags, and
characters. Having built the vocabularies it writes them in a file. The
writing of vocabulary in a file assigns an id (the line #) to each word.
It then extract the relevant GloVe vectors and stores them in a np array
such that the i-th entry corresponds to the i-th word in the vocabulary.
Args:
config: (instance of Config) has attributes like hyper-params...
"""
# get config and processing of words
config = Config(load=False)
processing_word = get_processing_word(lowercase=True)
# Generators
dev = CoNLLDataset(config.filename_dev, processing_word)
test = CoNLLDataset(config.filename_test, processing_word)
train = CoNLLDataset(config.filename_train, processing_word)
# Build Word and Tag vocab
vocab_words, vocab_tags = get_vocabs([train, dev, test])
vocab_glove = get_glove_vocab(config.filename_glove)
# 与glove中的词集合求交,只保留有向量的那些词
vocab = vocab_words & vocab_glove
vocab.add(UNK)
vocab.add(NUM)
# Save vocab, vocab: set()
print("write vocab set to file: " + config.filename_words)
write_vocab(vocab, config.filename_words)
print("write vocab tags set to file: " + config.filename_tags)
write_vocab(vocab_tags, config.filename_tags)
# Trim GloVe Vectors, 只加载那些在词集合中出现过的词向量
vocab_to_index_dict = load_vocab(config.filename_words)
# vocab: dict, vocab[word] = word_index
print("export trimmed vocab embedding to file: " + config.filename_trimmed)
export_trimmed_glove_vectors(vocab_to_index_dict, config.filename_glove,
config.filename_trimmed, config.dim_word)
# Build and save char vocab
train = CoNLLDataset(config.filename_train)
vocab_chars = get_char_vocab(train)
print("save char set to file:" + config.filename_chars)
write_vocab(vocab_chars, config.filename_chars)
def main():
"""Procedure to build data
You MUST RUN this procedure. It iterates over the whole dataset (train,
dev and test) and extract the vocabularies in terms of words, tags, and
characters. Having built the vocabularies it writes them in a file. The
writing of vocabulary in a file assigns an id (the line #) to each word.
It then extract the relevant GloVe vectors and stores them in a np array
such that the i-th entry corresponds to the i-th word in the vocabulary.
Args:
config: (instance of Config) has attributes like hyper-params...
"""
# get config and processing of words
config = Config(load=False)
processing_word = get_processing_word(lowercase=True)
# Generators
dev = CoNLLDataset(config.filename_dev, processing_word)
test = CoNLLDataset(config.filename_test, processing_word)
train = CoNLLDataset(config.filename_train, processing_word)
# Build Word and Tag vocab (only from train!)
vocab_words, vocab_freqs, vocab_tags = get_vocabs([train]) #, dev, test])
vocab_glove = get_glove_vocab(config.filename_glove)
vocab = vocab_words & vocab_glove
#vocab = make_unks(vocab, vocab_freqs, config.p_unk)
#vocab.add(UNK)
vocab.add(NUM)
vocab = [UNK] + list(vocab)
# Save vocab
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
# get singletons
singletons = [k for k, v in vocab_freqs.items() if v == 1]
write_vocab(singletons, config.filename_singletons)
# Trim GloVe Vectors
vocab = load_vocab(config.filename_words)
export_trimmed_glove_vectors(vocab, config.filename_glove,
config.filename_trimmed, config.dim_word)
# Build and save char vocab
train = CoNLLDataset(config.filename_train)
vocab_chars = get_char_vocab(train)
write_vocab(vocab_chars, config.filename_chars)
def main():
# get config and processing of words
config = Config(load=False)
# should be source_x.txt
# or ontonotes-nw if you like
config.filename_train = "../datasets/ritter2011/train"
config.filename_dev = "../datasets/ritter2011/dev"
config.filename_test = "../datasets/ritter2011/test"
config.filename_chars = config.filename_chars.replace("source", "target")
config.filename_glove = config.filename_glove.replace("source", "target")
config.filename_tags = config.filename_tags.replace("source", "target")
config.filename_words = config.filename_words.replace("source", "target")
config.dir_model = config.dir_model.replace("source", "target")
config.dir_output = config.dir_output.replace("source", "target")
config.path_log = config.path_log.replace("source", "target")
processing_word = get_processing_word(lowercase=True)
# Generators
dev = NERDataset(config.filename_dev, processing_word)
test = NERDataset(config.filename_test, processing_word)
train = NERDataset(config.filename_train, processing_word)
# Build Word and Tag vocab
vocab_words, vocab_tags = get_vocabs([train, dev, test])
vocab_glove = get_glove_vocab(config.filename_glove)
vocab = vocab_words & vocab_glove
vocab.add(UNK)
vocab.add(NUM)
vocab_tags.add(UNK)
# Save vocab
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
# Trim Word Vectors
vocab = load_vocab(config.filename_words)
export_trimmed_glove_vectors(vocab, config.filename_glove,
config.filename_trimmed, config.dim_word)
# Build and save char vocab
train = NERDataset(config.filename_train)
vocab_chars = get_char_vocab(train)
write_vocab(vocab_chars, config.filename_chars)
def main():
"""Procedure to build data
You MUST RUN this procedure. It iterates over the whole dataset (train,
dev and test) and extract the vocabularies in terms of words, tags, and
characters. Having built the vocabularies it writes them in a file. The
writing of vocabulary in a file assigns an id (the line #) to each word.
It then extract the relevant GloVe vectors and stores them in a np array
such that the i-th entry corresponds to the i-th word in the vocabulary.
Args:
config: (instance of Config) has attributes like hyper-params...
"""
# get config and processing of words
config = Config(load=False)
processing_word = get_processing_word(lowercase=True)
# Generators
dev = CoNLLDataset(config.filename_dev, processing_word)
test = CoNLLDataset(config.filename_test, processing_word)
train = CoNLLDataset(config.filename_train, processing_word)
# Build Word and Tag vocab
vocab_words, vocab_tags = get_vocabs([train, dev, test])
vocab_glove = get_glove_vocab(config.filename_glove)
vocab = vocab_words & vocab_glove
vocab.add(UNK)
vocab.add(NUM)
# Save vocab
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
# Trim GloVe Vectors
vocab = load_vocab(config.filename_words)
export_trimmed_glove_vectors(vocab, config.filename_glove,
config.filename_trimmed, config.dim_word)
# Build and save char vocab
train = CoNLLDataset(config.filename_train)
vocab_chars = get_char_vocab(train)
write_vocab(vocab_chars, config.filename_chars)
def main():
"""Procedure to build data
You MUST RUN this procedure. It iterates over the whole dataset (train,
dev and test) and extract the vocabularies in terms of words, tags, and
characters. Having built the vocabularies it writes them in a file. The
writing of vocabulary in a file assigns an id (the line #) to each word.
It then extract the relevant GloVe vectors and stores them in a np array
such that the i-th entry corresponds to the i-th word in the vocabulary.
Args:
config: (instance of Config) has attributes like hyper-params...
"""
# get config and processing of words
config = Config(parser, load=False)
processing_word = get_processing_word(lowercase=True)
# Generators
dev = Dataset(config.filename_dev, processing_word)
test = Dataset(config.filename_test, processing_word)
train = Dataset(config.filename_train, processing_word)
# Build Word and Tag vocab
vocab_words, vocab_tags = get_vocabs([train, dev, test])
# vocab_glove = get_wordvec_vocab(config.filename_wordvec)
# vocab = vocab_words & vocab_glove
vocab = list(vocab_words)
vocab.insert(0, UNK)
vocab.append(NUM)
# Save vocab
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
print('Wrote vocab')
# Trim GloVe Vectors
vocab = load_vocab(config.filename_words)
export_trimmed_wordvec_vectors(vocab, config.filename_wordvec,
config.filename_wordvec_trimmed)
print('trimmed vocab')
def main():
config = Config(load=False)
processing_word = data_utils.get_processing_word(lowercase=True)
#Datasets
test = Dataset(config.filename_test, processing_word=processing_word)
dev = Dataset(config.filename_dev, processing_word=processing_word)
train = Dataset(config.filename_train, processing_word=processing_word)
# Vocab Generators
vocab_words, vocab_tags = get_vocabs([train, dev, test])
vocab_fasttext = get_fasttext_vocab(config.filename_fasttext)
#Build Word and Tag Vocab
if config.use_fasttext_oov_vector_gen:
vocab = vocab_words
else:
vocab = vocab_words & vocab_fasttext
vocab.add(UNK)
vocab.add(NUM)
oov_words = vocab_words - vocab_fasttext
generate_fasttext_oov_vectors(oov_words, config.filename_oov_words,
config.filename_oov_result_vectors)
# Save vocab
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
#Trim and (insert new) Fasttext vectors
word_to_idx, idx_to_word = load_vocab(config.filename_words)
export_trimmed_fasttext_vectors(word_to_idx, idx_to_word,
config.filename_fasttext,
config.filename_fasttext_trimmed,
config.dim_word,
config.filename_oov_result_vectors,
config.use_fasttext_oov_vector_gen)
# Build and save char vocab
train = Dataset(config.filename_train)
vocab_chars = get_char_vocab(train)
write_vocab(vocab_chars, config.filename_chars)
def main():
# get config and processing of words
config = Config(load=False)
# should be source_x.txt
# or ontonotes-nw if you like
config.filename_train = "../datasets/ontonotes-nw/train"
config.filename_dev = "../datasets/ontonotes-nw/dev"
config.filename_test = "../datasets/ontonotes-nw/test"
processing_word = get_processing_word(lowercase=True)
# Generators
dev = NERDataset(config.filename_dev, processing_word)
test = NERDataset(config.filename_test, processing_word)
train = NERDataset(config.filename_train, processing_word)
#for word, tag in train:
#print("word:{}".format(word))
#print ("tag:{}".format(tag))
# Build Word and Tag vocab
vocab_words, vocab_tags = get_vocabs([train, dev, test])
vocab_glove = get_glove_vocab(config.filename_glove)
vocab = vocab_words & vocab_glove
vocab.add(UNK)
vocab.add(NUM)
vocab_tags.add(UNK)
# Save vocab
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
# Trim Word Vectors
vocab = load_vocab(config.filename_words)
export_trimmed_glove_vectors(vocab, config.filename_glove,
config.filename_trimmed, config.dim_word)
# Build and save char vocab
train = NERDataset(config.filename_train)
vocab_chars = get_char_vocab(train)
write_vocab(vocab_chars, config.filename_chars)
def main():
# get config and processing of words
config = Config(load=False)
# or ontonotes-nw if you like
assert sys.argv[1] in datasets, "the source argument should be in {c/o/r/w}"
source_dataset = sys.argv[1]
source_vocab_words, source_vocab_tags = get_vocabs_from_dataset(source_dataset)
print("Source word vocab size:", len(source_vocab_words))
assert sys.argv[2] in datasets, "the target argument should be in {c/o/r/w}"
target_dataset = sys.argv[2]
target_vocab_words, _ = get_vocabs_from_dataset(target_dataset)
print("Target word vocab size:", len(target_vocab_words))
# Build Word and Tag vocab
config.filename_words = "../datasets/%s/words.txt"%datasets[source_dataset]
config.filename_tags = "../datasets/%s/tags.txt"%datasets[source_dataset]
config.filename_chars = "../datasets/%s/chars.txt"%datasets[source_dataset]
print("Source+Target word vocab size:", len((source_vocab_words | target_vocab_words)))
vocab_glove = get_glove_vocab(config.filename_glove)
vocab = (source_vocab_words | target_vocab_words) & vocab_glove
vocab.add(UNK)
vocab.add(NUM)
print("Final word vocab size:", len(vocab))
# Save vocab
write_vocab(vocab, config.filename_words)
write_vocab(source_vocab_tags, config.filename_tags)
# Build and save char vocab
vocab_chars = get_char_vocab((source_vocab_words | target_vocab_words))
print("Final char vocab size:", len(vocab_chars))
write_vocab(vocab_chars, config.filename_chars)
# Trim Word Vectors
vocab = load_vocab(config.filename_words)
config.filename_trimmed = config.filename_trimmed.replace("dataset_name",datasets[source_dataset])
export_trimmed_glove_vectors(vocab, config.filename_glove,
config.filename_trimmed, config.dim_word)
def main():
"""Procedure to build data
You MUST RUN this procedure. It iterates over the whole dataset (train, dev
and test) and extract the vocabularies in terms of words, tags. Having built
the vocabularies it writes them in a file. The writing of vocabulary in a
file assigns an id (the line #) to each word. It then extract the relevant
polyglot vectors and stores them in a np array such that the i-th entry
corresponds to the i-th word in the vocabulary.
Args:
config: (instance of Config) has attributes like hyper-params...
"""
# get config and processing of words
config = Config(load=False)
processing_word = get_processing_word()
# Generators
dev = getDataset(config.filename_dev, processing_word)
test = getDataset(config.filename_test, processing_word)
train = getDataset(config.filename_train, processing_word)
# Build Word and Tag vocab
vocab_words, vocab_tags = get_vocabs([train, dev, test])
vocab_poly = get_polyglot_vocab(config.filename_polyglot)
# Get common vocab
vocab = vocab_words & vocab_poly
vocab.add(UNK)
# Save vocab
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
# Trim Polygloe Vectors
vocab = load_vocab(config.filename_words)
export_trimmed_polyglot_vectors(vocab, config.filename_polyglot, \
config.filename_trimmed, config.dim)
def main():
"""Procedure to build data
You MUST RUN this procedure. It iterates over the whole dataset (train,
dev and test) and extract the vocabularies in terms of words, tags, and
characters. Having built the vocabularies it writes them in a file. The
writing of vocabulary in a file assigns an id (the line #) to each word.
It then extract the relevant GloVe vectors and stores them in a np array
such that the i-th entry corresponds to the i-th word in the vocabulary.
Args:
config: (instance of Config) has attributes like hyper-params...
"""
# get config and processing of words
config = Config(load=False)
# Build word and tag vocabs
vocab_words, vocab_tags = CoNLLDataset(
[config.filename_dev, config.filename_train, config.filename_test],
processing_word(lowercase=True)).get_word_tag_vocabs()
vocab_glove = get_glove_vocab(config.filename_glove)
vocab = vocab_words & vocab_glove | {UNK, NUM}
# Save vocab
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
# Trim GloVe Vectors
vocab = load_vocab(config.filename_words)
export_trimmed_glove_vectors(vocab, config.filename_glove,
config.filename_trimmed, config.dim_word)
# Build and save char vocab
vocab_chars = CoNLLDataset(config.filename_train).get_char_vocab()
write_vocab(vocab_chars, config.filename_chars)
def main():
"""Procedure to build data
This procedure iterates over the SemEval dataset and builds a vocabulary
of words and tags, then writes them to a file. Each word is labelled by
an ID. The GloVe vectors of the words are then extracted and stored
in a numpy array. The word id is used to index into that numpy array.
"""
# get config and processing of words
config = Config(load=False)
processing_word = get_processing_word(lowercase=True)
# Generators for the dev, test and training files
dev = GloveDataset(config.filename_dev, processing_word)
test = GloveDataset(config.filename_test, processing_word)
train = GloveDataset(config.filename_train, processing_word)
# Build Word and Tag vocab
vocab_words, vocab_tags = get_vocabs([train, dev, test])
vocab_glove = get_glove_vocab(config.filename_glove)
#find the intersection between the vocabs from the chosen dataset and GloVe
vocab = vocab_words & vocab_glove
#adds the unknown and numeric value to the vocab
vocab.add(UNK)
vocab.add(NUM)
# Save vocab
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
# export the trimmed glove vectors in a compressed file.
vocab = load_vocab(config.filename_words)
export_trimmed_glove_vectors(vocab, config.filename_glove,
config.filename_trimmed, config.dim_word)
def main():
"""Procedure to build data
You MUST RUN this procedure. It iterates over the whole dataset (train,
dev and test) and extract the vocabularies in terms of words, tags, and
characters. Having built the vocabularies it writes them in a file. The
writing of vocabulary in a file assigns an id (the line #) to each word.
It then extract the relevant GloVe vectors and stores them in a np array
such that the i-th entry corresponds to the i-th word in the vocabulary.
Args:
config: (instance of Config) has attributes like hyper-params...
"""
# get config and processing of words
config = Config(load=False)
processing_word = get_processing_word(lowercase=True)
# Generators
dev = FKDataset(config.filename_dev, processing_word)
test1 = FKDataset(config.filename_test1, processing_word)
test2 = FKDataset(config.filename_test2, processing_word)
train = FKDataset(config.filename_train, processing_word)
# Build Word and Tag vocab
vocab_words, vocab_tags = get_vocabs([train, dev, test1,test2])
vocab_glove = get_glove_vocab(config.filename_glove)
#print ("Inside build data and prinitng vocab_tags")
vocab_tags_task1 =[]
vocab_tags_task2 =[]
for items in vocab_tags:
if "_dress" in items:
vocab_tags_task1.append(items)
if "_jean" in items:
vocab_tags_task2.append(items)
vocab_tags_task1.append('O')
vocab_tags_task2.append('O')
vocab = vocab_words & vocab_glove
vocab.add(UNK)
vocab.add(NUM)
# Save vocab
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
# Trim GloVe Vectors
vocab = load_vocab(config.filename_words)
export_trimmed_glove_vectors(vocab, config.filename_glove,
config.filename_trimmed, config.dim_word)
# Build and save char vocab
train = FKDataset(config.filename_train)
vocab_chars = get_char_vocab(train)
write_vocab(vocab_chars, config.filename_chars)
filename_chars = "working_dir/chars.txt"
# build data (just to test model)
build_data(
filename_dev,
filename_test,
filename_train, [300],
filename_words,
filename_words_ext,
filename_tags,
filename_chars,
filename_word="../pretrained_vectors/vecs_{}.txt",
filename_word_vec_trimmed="../pretrained_vectors/vecs_{}.trimmed.npz",
which_tags=which_tags)
vocab_words = load_vocab(filename_words)
vocab_tags = load_vocab(filename_tags)
vocab_chars = load_vocab(filename_chars)
nwords = len(vocab_words)
nchars = len(vocab_chars)
ntags = len(vocab_tags)
# load data
processing_word = get_processing_word(vocab_words,
vocab_chars,
lowercase=True,
chars=use_chars)
processing_tag = get_processing_word(vocab_tags,
lowercase=False,
allow_unk=False)
X_dev, y_dev = coNLLDataset_full(filename_dev, processing_word,
def main():
"""Procedure to build data
You MUST RUN this procedure. It iterates over the whole dataset (train,
dev and test) and extract the vocabularies in terms of words, tags, and
characters. Having built the vocabularies it writes them in a file. The
writing of vocabulary in a file assigns an id (the line #) to each word.
It then extract the relevant GloVe vectors and stores them in a np array
such that the i-th entry corresponds to the i-th word in the vocabulary.
Args:
config: (instance of Config) has attributes like hyper-params...
"""
if len(sys.argv)<2:
sys.stderr.write("Too few arguments have been specified\n")
sys.stderr.write("python "+sys.argv[0]+" config [additional vocabulary in conll format]\n")
sys.exit(0)
# get config and processing of words
config_file = sys.argv[1]
config = Config(config_file,load=False)
processing_word = get_processing_word(config)
# processing_word = get_processing_word(lowercase=config.lowercase)
# Generators
dev = CoNLLDataset(config.filename_dev, processing_word)
test = CoNLLDataset(config.filename_test, processing_word)
train = CoNLLDataset(config.filename_train, processing_word)
# Build Word and Tag vocab
vocab_words, vocab_tags = get_vocabs([train, dev, test])
#add additional tags/vocabulary where the data is applied to!
if len(sys.argv)>2:
for i in range(2,len(sys.argv)):
wo,tg = get_vocabs([CoNLLDataset(sys.argv[i],processing_word)])
vocab_words |= wo
vocab_tags |= tg
#if config.use_pretrained:
# vocab_glove = get_vocab(config.filename_embeddings)
#if config.use_pretrained:
# vocab = vocab_words & vocab_glove
#else:
vocab = vocab_words
vocab.add(UNK)
vocab.add(NUM)
# Save vocab
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
# Trim GloVe Vectors
vocab = load_vocab(config.filename_words)
if config.use_pretrained:
export_trimmed_embedding_vectors(vocab, config.filename_embeddings,
config.filename_embeddings_trimmed, config.dim_word, config.embedding_type)
# Build and save char vocab
train = CoNLLDataset(config.filename_train)
vocab_chars = get_char_vocab(train)
write_vocab(vocab_chars, config.filename_chars)
def main():
"""Procedure to build data
You MUST RUN this procedure. It iterates over the whole dataset (train,
dev and test) and extract the vocabularies in terms of words, tags, and
characters. Having built the vocabularies it writes them in a file. The
writing of vocabulary in a file assigns an id (the line #) to each word.
It then extract the relevant GloVe vectors and stores them in a np array
such that the i-th entry corresponds to the i-th word in the vocabulary.
Args:
config: (instance of Config) has attributes like hyper-params...
"""
# get config and processing of words
config = Config(load=False)
processing_word = get_processing_word(lowercase=True)
logger = config.logger
#------------------------------------------------------------------
# Generators
# ------------------------------------------------------------------
dev = CoNLLDataset(config.filename_dev, processing_word)
test = CoNLLDataset(config.filename_test, processing_word)
train = CoNLLDataset(config.filename_train, processing_word)
sick = CoNLLDataset(config.filename_sick, processing_word)
# ------------------------------------------------------------------
# Build Word and Tag vocab
# ------------------------------------------------------------------
vocab_words, vocab_tags = get_vocabs([train, dev, test, sick])
vocab_glove = get_glove_vocab(config.filename_glove)
vocab = vocab_words & vocab_glove
vocab.add(UNK)
vocab.add(NUM)
# ------------------------------------------------------------------
# Save vocab
# ------------------------------------------------------------------
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
# ------------------------------------------------------------------
# Trim GloVe Vectors
# ------------------------------------------------------------------
vocab, _ = load_vocab(config.filename_words)
export_trimmed_glove_vectors(vocab, config.filename_glove,
config.filename_trimmed, config.dim_word)
# ------------------------------------------------------------------
# Build and save char vocab
# ------------------------------------------------------------------
train = CoNLLDataset(config.filename_train)
vocab_chars = get_char_vocab(train)
write_vocab(vocab_chars, config.filename_chars)
# ------------------------------------------------------------------
#split train files
# ------------------------------------------------------------------
logger.info('\n Splitting the train file into {} splits ...'.format(
config.num_splits))
split_train(config)
logger.info('Saved the train splits in {}'.format('ner/data/'))
def main():
"""Procedure to build data
You MUST RUN this procedure. It iterates over the whole dataset (train,
dev and test) and extract the vocabularies in terms of words, tags, and
characters. Having built the vocabularies it writes them in a file. The
writing of vocabulary in a file assigns an id (the line #) to each word.
It then extract the relevant GloVe vectors and stores them in a np array
such that the i-th entry corresponds to the i-th word in the vocabulary.
Args:
config: (instance of Config) has attributes like hyper-params...
"""
# get config and processing of words
dir_output = "./results/" + sys.argv[4] + "/"
config = Config(dir_output, load=False)
processing_word = get_processing_word(lowercase=True)
# Generators
#dev = CoNLLDataset(config.filename_dev, processing_word)
#test = CoNLLDataset(config.filename_test, processing_word)
#train = CoNLLDataset(config.filename_train, processing_word)
dev = CoNLLDataset(sys.argv[1], processing_word)
test = CoNLLDataset(sys.argv[2], processing_word)
train = CoNLLDataset(sys.argv[3], processing_word)
config.filename_dev = sys.argv[1]
config.filename_test = sys.argv[2]
config.filename_train = sys.argv[3]
config.filename_pred = sys.argv[2].replace(".txt", ".pred")
config.filename_words = "./data/words_" + sys.argv[4] + ".txt"
config.filename_chars = "./data/chars_" + sys.argv[4] + ".txt"
config.filename_tags = "./data/tags_" + sys.argv[4] + ".txt"
# Build Word and Tag vocab
vocab_words, vocab_tags = get_vocabs([train, dev, test])
vocab_glove = get_glove_vocab(config.filename_glove)
vocab = vocab_words & vocab_glove
vocab.add(UNK)
vocab.add(NUM)
vocab.add(LG)
vocab.add(ENT)
# Save vocab
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
# Trim GloVe Vectors
vocab = load_vocab(config.filename_words)
export_trimmed_glove_vectors(vocab, config.filename_glove,
config.filename_trimmed, config.dim_word)
# Build and save char vocab
train = CoNLLDataset(config.filename_train)
vocab_chars = get_char_vocab(train)
write_vocab(vocab_chars, config.filename_chars)
def main():
"""Procedure to build data
You MUST RUN this procedure. It iterates over the whole dataset (train,
dev and test) and extract the vocabularies in terms of words, tags, and
characters. Having built the vocabularies it writes them in a file. The
writing of vocabulary in a file assigns an id (the line #) to each word.
It then extract the relevant word2vec vectors and stores them in a np array
such that the i-th entry corresponds to the i-th word in the vocabulary.
Args:
config: (instance of Config) has attributes like hyper-params...
"""
# get config and processing of words
config = Config(load=False)
processing_word = get_processing_word(lowercase=False)
# Generators
dev = CoNLLDataset(config.filename_dev, processing_word)
test = CoNLLDataset(config.filename_test, processing_word)
train = CoNLLDataset(config.filename_train, processing_word)
train2 = CoNLLDataset(config.filename_train2, processing_word)
# Build Word and Tag vocab
vocab_words, vocab_tags = get_vocabs([train, dev, test, train2])
vocab = vocab_words
if "w2v" in config.use_pretrained:
vocab_word2vec = get_word_vec_vocab(config.filename_word2vec)
vocab = vocab_words & vocab_word2vec if config.use_pretrained == "w2v" else vocab_words
if config.replace_digits:
vocab.add(NUM)
vocab.add(UNK)
# Save vocab
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
# Trim FastText vectors
if "ft" in config.use_pretrained:
abs_f_words = os.path.abspath(config.filename_words)
abs_f_vec = os.path.abspath(config.filename_fasttext)
cmd = config.get_ft_vectors_cmd.format(abs_f_words, abs_f_vec)
subprocess.check_call(cmd, shell=True)
vocab = load_vocab(config.filename_words)
export_trimmed_word_vectors(vocab, config.filename_fasttext,
config.filename_trimmed_ft,
config.dim_word)
if "s2v" in config.use_pretrained:
abs_s_words = os.path.abspath(config.filename_words)
abs_s_vec = os.path.abspath(config.filename_fasttext)
cmd = config.get_sent2vec_vectors_cmd.format(abs_s_words, abs_s_vec)
subprocess.check_call(cmd, shell=True)
vocab = load_vocab(config.filename_words)
export_trimmed_word_vectors(vocab, config.filename_sent2vec,
config.filename_trimmed_s2v,
config.dim_sent)
# Trim Morph2Vec vectors
if "m2v" in config.use_pretrained:
vocab = load_vocab(config.filename_words)
export_trimmed_word_vectors(vocab,
config.filename_morph2vec,
config.filename_trimmed_m2v,
config.dim_morph,
partial_match=True)
# Trim word2vec Vectors
if "w2v" in config.use_pretrained:
vocab = load_vocab(config.filename_words)
export_trimmed_word_vectors(vocab, config.filename_word2vec,
config.filename_trimmed_w2v,
config.dim_word)
# Build and save char vocab
train = CoNLLDataset(config.filename_train)
vocab_chars = get_char_vocab(train, config.use_ortho_char)
write_vocab(vocab_chars, config.filename_chars)
def generate_model_data(data_prefix=None):
"""Procedure to build data
You MUST RUN this procedure. It iterates over the whole dataset (train,
dev and test) and extract the vocabularies in terms of words, tags, and
characters. Having built the vocabularies it writes them in a file. The
writing of vocabulary in a file assigns an id (the line #) to each word.
It then extract the relevant GloVe vectors and stores them in a np array
such that the i-th entry corresponds to the i-th word in the vocabulary.
Args:
config: (instance of Config) has attributes like hyper-params...
"""
# get config and processing of words
# loads PubMeda articles
config = Config(load=False)
print('Config')
processing_word = get_processing_word(lowercase=True)
print('Processing_word')
# Generators
if data_prefix:
cwd = os.getcwd()
config.filename_dev = os.path.join(
cwd, 'data',
data_prefix + '_' + os.path.basename(config.filename_dev))
config.filename_test = os.path.join(
cwd, 'data',
data_prefix + '_' + os.path.basename(config.filename_test))
config.filename_train = os.path.join(
cwd, 'data',
data_prefix + '_' + os.path.basename(config.filename_train))
if not os.path.isfile(config.filename_dev):
print('Preprocessing tokens and labels to generate input data files')
preprocess_data()
dev = CoNLLDataset(config.filename_dev, processing_word)
test = CoNLLDataset(config.filename_test, processing_word)
train = CoNLLDataset(config.filename_train, processing_word)
print('Loaded dev, test, train')
# Build Word and Tag vocab
vocab_words, vocab_tags = get_vocabs([train, dev, test])
print('Loading vocab_words')
vocab_glove = get_glove_vocab(config.filename_glove)
vocab = vocab_words & vocab_glove
vocab.add(UNK)
vocab.add(NUM)
# Save vocab
write_vocab(vocab, config.filename_words)
write_vocab(vocab_tags, config.filename_tags)
# Trim GloVe Vectors
vocab = load_vocab(config.filename_words)
export_trimmed_glove_vectors(vocab, config.filename_glove,
config.filename_trimmed, config.dim_word)
# Build and save char vocab
train = CoNLLDataset(config.filename_train)
vocab_chars = get_char_vocab(train)
write_vocab(vocab_chars, config.filename_chars)
def main():
"""Procedure to build data
You MUST RUN this procedure. It iterates over the whole dataset (train,
dev and test) and extract the vocabularies in terms of words, tags, and
characters. Having built the vocabularies it writes them in a file. The
writing of vocabulary in a file assigns an id (the line #) to each word.
It then extract the relevant GloVe vectors and stores them in a np array
such that the i-th entry corresponds to the i-th word in the vocabulary.
Args:
config: (instance of Config) has attributes like hyper-params...
"""
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default='conll2003')
parser.add_argument('--train_lang', type=str, default='en')
parser.add_argument('--dev_lang', type=str, default='en')
parser.add_argument('--test_lang', type=str, default='en')
parser.add_argument('--src_glove',
type=str,
default='data/glove.42B.300d.txt')
parser.add_argument('--tgt_glove', type=str, default=None)
parser.add_argument('--emb_dim', type=int, default=300)
parser.add_argument('--trimmed_glove',
type=str,
default='glove_trimmed.npz')
#parser.add_argument('--init_char', type=str, default=0)
#parser.add_argument('--trimmed_char', type=str, default='char_trimmed.npz')
args = parser.parse_args()
# get config and processing of words
#config = Config(emb_dim=512, load=False, dataset='ner_nl_es', use_muse=True)
processing_word = get_processing_word(lowercase=True)
#src_lang = 'nl'
#tgt_lang = 'es'
data_dir = args.dataset
# Generators
dev = CoNLLDataset(os.path.join(data_dir, 'dev.txt'),
processing_word=processing_word,
lang=args.dev_lang)
test = CoNLLDataset(os.path.join(data_dir, 'test.txt'),
processing_word=processing_word,
lang=args.test_lang)
train = CoNLLDataset(os.path.join(data_dir, 'train.txt'),
processing_word=processing_word,
lang=args.train_lang)
# Build Word and Tag vocab
vocab_words, vocab_tags = get_vocabs([train, dev, test])
vocab_glove = get_glove_vocab(args.src_glove, lang=args.train_lang)
if args.tgt_glove:
vocab_glove_tgt = get_glove_vocab(args.tgt_glove, lang=args.test_lang)
vocab = vocab_words & (vocab_glove | vocab_glove_tgt)
else:
vocab = vocab_words & vocab_glove
#vocab = vocab_words
vocab.add(UNK)
vocab.add(NUM)
# Save vocab
write_vocab(vocab, os.path.join(data_dir, 'words.txt'))
write_vocab(vocab_tags, os.path.join(data_dir, 'tags.txt'))
# Trim GloVe Vectors
vocab = load_vocab(os.path.join(data_dir, 'words.txt'))
if args.tgt_glove:
gloves = {
args.train_lang: args.src_glove,
args.test_lang: args.tgt_glove
}
else:
gloves = {args.train_lang: args.src_glove}
export_trimmed_glove_vectors_multiple(
vocab, gloves, os.path.join(data_dir, args.trimmed_glove),
args.emb_dim)
# Build and save char vocab
train = CoNLLDataset(os.path.join(data_dir, 'train.txt'))
test = CoNLLDataset(os.path.join(data_dir, 'test.txt'))
dev = CoNLLDataset(os.path.join(data_dir, 'dev.txt'))
vocab_chars = get_char_vocab([train, test, dev])
write_vocab(vocab_chars, os.path.join(data_dir, 'chars.txt'))
