def __token_embedding_dict(self, pretrained_vocabulary: PretrainedVocabulary):
token_embedding_dict = dict()
unk_index = pretrained_vocabulary.index(pretrained_vocabulary.unk)
for index in range(pretrained_vocabulary.embedding_matrix.size(0)):
if index in {pretrained_vocabulary.padding_index, unk_index}:
# 对 padding_index 和 unk_index 来说，需要略过，因为是在后面合并后的 vocabulary 中，会重新填充
continue
token = pretrained_vocabulary.token(index)
embedding = pretrained_vocabulary.embedding_matrix[index, :]
token_embedding_dict[token] = embedding
return token_embedding_dict
def test_save_and_load(pretrained_vocabulary):
"""
测试 保存和载入
"""
saved_dir = os.path.join(ROOT_PATH,
"data/easytext/tests/pretrained_vocabulary")
pretrained_vocabulary.save_to_file(saved_dir)
loaded_vocab = PretrainedVocabulary.from_file(saved_dir)
test_pretrained_vocabulary(loaded_vocab)
def __init__(
self, is_training: bool, dataset: Dataset, gaz_vocabulary_dir: str,
gaz_pretrained_word_embedding_loader: PretrainedWordEmbeddingLoader
):
"""
构建 Gaz 词汇表
:param is_training: 当前是否 Training 状态
:param dataset: 数据集
:param gaz_vocabulary_dir: gaz 词汇表存放目录
:param gaz_pretrained_word_embedding_loader: gaz 预训练 word embedding 载入器
"""
super().__init__(is_training=is_training)
# gazetter 理论上来说，应该支持持久化的，这里并没有做
gazetteer = Gazetteer(gaz_pretrained_word_embedding_loader=
gaz_pretrained_word_embedding_loader)
if is_training:
gaz_vocabulary_collate = GazVocabularyCollate(gazetteer=gazetteer)
data_loader = DataLoader(dataset=dataset,
batch_size=100,
shuffle=False,
num_workers=0,
collate_fn=gaz_vocabulary_collate)
gaz_words = list()
for batch_gaz_words in data_loader:
gaz_words.extend(batch_gaz_words)
gaz_vocabulary = Vocabulary(tokens=gaz_words,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True)
gaz_vocabulary = PretrainedVocabulary(
vocabulary=gaz_vocabulary,
pretrained_word_embedding_loader=
gaz_pretrained_word_embedding_loader)
gaz_vocabulary.save_to_file(gaz_vocabulary_dir)
else:
gaz_vocabulary = Vocabulary.from_file(gaz_vocabulary_dir)
self.gaz_vocabulary = gaz_vocabulary
self.gazetteer = gazetteer
def build_vocabulary(self):
training_dataset_file_path = self.config["training_dataset_file_path"]
dataset = ACSASemEvalDataset(
dataset_file_path=training_dataset_file_path)
collate_fn = VocabularyCollate()
data_loader = DataLoader(dataset=dataset,
batch_size=10,
shuffle=False,
num_workers=0,
collate_fn=collate_fn)
tokens = list()
categories = list()
labels = list()
for collate_dict in data_loader:
tokens.append(collate_dict["tokens"])
categories.append(collate_dict["categories"])
labels.append(collate_dict["labels"])
token_vocabulary = Vocabulary(tokens=tokens,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True)
if not self.config["debug"]:
pretrained_file_path = self.config["pretrained_file_path"]
pretrained_loader = GloveLoader(
embedding_dim=300, pretrained_file_path=pretrained_file_path)
pretrained_token_vocabulary = PretrainedVocabulary(
vocabulary=token_vocabulary,
pretrained_word_embedding_loader=pretrained_loader)
token_vocabulary = pretrained_token_vocabulary
category_vocabulary = LabelVocabulary(labels=categories, padding=None)
label_vocabulary = LabelVocabulary(labels=labels, padding=None)
return {
"token_vocabulary": token_vocabulary,
"category_vocabulary": category_vocabulary,
"label_vocabulary": label_vocabulary
}
def pretrained_vocabulary():
"""
生成 预训练词汇表
"""
pretrained_file_path = "data/easytext/tests/pretrained/word_embedding_sample.3d.txt"
pretrained_file_path = os.path.join(ROOT_PATH, pretrained_file_path)
glove_loader = GloveLoader(embedding_dim=3,
pretrained_file_path=pretrained_file_path)
tokens = [["我"], ["美丽"]]
vocab = Vocabulary(tokens=tokens,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True)
pretrained_vocab = PretrainedVocabulary(
vocabulary=vocab, pretrained_word_embedding_loader=glove_loader)
return pretrained_vocab
def build_vocabulary(self, dataset: Dataset):
data_loader = DataLoader(dataset=dataset,
batch_size=100,
shuffle=False,
num_workers=0,
collate_fn=VocabularyCollate())
batch_tokens = list()
batch_sequence_labels = list()
for collate_dict in data_loader:
batch_tokens.extend(collate_dict["tokens"])
batch_sequence_labels.extend(collate_dict["sequence_labels"])
token_vocabulary = Vocabulary(tokens=batch_tokens,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True)
model_name = self.config["model_name"]
if model_name in {ModelName.NER_V2, ModelName.NER_V3}:
pretrained_word_embedding_file_path = self.config[
"pretrained_word_embedding_file_path"]
glove_loader = GloveLoader(
embedding_dim=100,
pretrained_file_path=pretrained_word_embedding_file_path)
token_vocabulary = PretrainedVocabulary(
vocabulary=token_vocabulary,
pretrained_word_embedding_loader=glove_loader)
label_vocabulary = LabelVocabulary(labels=batch_sequence_labels,
padding=LabelVocabulary.PADDING)
return {
"token_vocabulary": token_vocabulary,
"label_vocabulary": label_vocabulary
}
def __init__(self,
is_training: bool,
dataset: Dataset,
vocabulary_collate,
token_vocabulary_dir: str,
label_vocabulary_dir: str,
is_build_token_vocabulary: bool,
pretrained_word_embedding_loader: PretrainedWordEmbeddingLoader):
"""
词汇表构建器
:param is_training: 因为在 train 和 非 train, 词汇表的构建行为有所不同;
如果是 train, 则一般需要重新构建; 而对于 非train, 使用先前构建好的即可。
:param dataset: 数据集
:param vocabulary_collate: 词汇表 collate
:param token_vocabulary_dir: token vocabulary 存放目录
:param label_vocabulary_dir: label vocabulary 存放目录
:param is_build_token_vocabulary: 是否构建 token vocabulary, 因为在使用 Bert 或者 其他模型作为预训练的 embedding,
则没有必要构建 token vocabulary.
:param pretrained_word_embedding_loader: 预训练词汇表
"""
super().__init__(is_training=is_training)
token_vocabulary = None
label_vocabulary = None
if is_training:
data_loader = DataLoader(dataset=dataset,
batch_size=100,
shuffle=False,
num_workers=0,
collate_fn=vocabulary_collate)
batch_tokens = list()
batch_sequence_labels = list()
for collate_dict in data_loader:
batch_tokens.extend(collate_dict["tokens"])
batch_sequence_labels.extend(collate_dict["sequence_labels"])
if is_build_token_vocabulary:
token_vocabulary = Vocabulary(tokens=batch_tokens,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True)
if pretrained_word_embedding_loader is not None:
token_vocabulary = \
PretrainedVocabulary(vocabulary=token_vocabulary,
pretrained_word_embedding_loader=pretrained_word_embedding_loader)
if token_vocabulary_dir:
token_vocabulary.save_to_file(token_vocabulary_dir)
label_vocabulary = LabelVocabulary(labels=batch_sequence_labels,
padding=LabelVocabulary.PADDING)
if label_vocabulary_dir:
label_vocabulary.save_to_file(label_vocabulary_dir)
else:
if is_build_token_vocabulary and token_vocabulary_dir:
token_vocabulary = Vocabulary.from_file(token_vocabulary_dir)
if label_vocabulary_dir:
label_vocabulary = LabelVocabulary.from_file(label_vocabulary_dir)
self.token_vocabulary = token_vocabulary
self.label_vocabulary = label_vocabulary
def test_flat_model_collate(lattice_ner_demo_dataset,
character_pretrained_embedding_loader,
gaz_pretrained_embedding_loader):
"""
测试 flat model collate
:return:
"""
# 仅仅取前两个作为测试
batch_instances = lattice_ner_demo_dataset[0:2]
vocabulary_collate = VocabularyCollate()
collate_result = vocabulary_collate(batch_instances)
characters = collate_result["tokens"]
sequence_label = collate_result["sequence_labels"]
character_vocabulary = Vocabulary(tokens=characters,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True)
character_vocabulary = PretrainedVocabulary(
vocabulary=character_vocabulary,
pretrained_word_embedding_loader=character_pretrained_embedding_loader)
label_vocabulary = LabelVocabulary(labels=sequence_label,
padding=LabelVocabulary.PADDING)
gazetter = Gazetteer(
gaz_pretrained_word_embedding_loader=gaz_pretrained_embedding_loader)
gaz_vocabulary_collate = GazVocabularyCollate(gazetteer=gazetter)
gaz_words = gaz_vocabulary_collate(batch_instances)
gaz_vocabulary = Vocabulary(tokens=gaz_words,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True)
gaz_vocabulary = PretrainedVocabulary(
vocabulary=gaz_vocabulary,
pretrained_word_embedding_loader=gaz_pretrained_embedding_loader)
flat_vocabulary = FlatPretrainedVocabulary(
character_pretrained_vocabulary=character_vocabulary,
gaz_word_pretrained_vocabulary=gaz_vocabulary)
flat_model_collate = FLATModelCollate(token_vocabulary=flat_vocabulary,
gazetter=gazetter,
label_vocabulary=label_vocabulary)
model_inputs = flat_model_collate(batch_instances)
logging.debug(json2str(model_inputs.model_inputs["metadata"]))
metadata_0 = model_inputs.model_inputs["metadata"][0]
sentence = "陈元呼吁加强国际合作推动世界经济发展"
# 陈元呼吁加强国际合作推动世界经济发展
expect_squeeze_gaz_words_0 = [
"陈元", "呼吁", "吁加", "加强", "强国", "国际", "合作", "推动", "世界", "经济", "发展"
]
squeeze_gaz_words_0 = metadata_0["squeeze_gaz_words"]
ASSERT.assertListEqual(expect_squeeze_gaz_words_0, squeeze_gaz_words_0)
expect_tokens = [character
for character in sentence] + expect_squeeze_gaz_words_0
tokens = metadata_0["tokens"]
ASSERT.assertListEqual(expect_tokens, tokens)
character_pos_begin = [index for index in range(len(sentence))]
character_pos_end = [index for index in range(len(sentence))]
squeeze_gaz_words_begin = list()
squeeze_gaz_words_end = list()
for squeeze_gaz_word in squeeze_gaz_words_0:
index = sentence.find(squeeze_gaz_word)
squeeze_gaz_words_begin.append(index)
squeeze_gaz_words_end.append(index + len(squeeze_gaz_word) - 1)
pos_begin = model_inputs.model_inputs["pos_begin"][0]
pos_end = model_inputs.model_inputs["pos_end"][0]
expect_pos_begin = character_pos_begin + squeeze_gaz_words_begin
expect_pos_begin += [0] * (pos_begin.size(0) - len(expect_pos_begin))
expect_pos_begin = torch.tensor(expect_pos_begin)
expect_pos_end = character_pos_end + squeeze_gaz_words_end
expect_pos_end += [0] * (pos_end.size(0) - len(expect_pos_end))
expect_pos_end = torch.tensor(expect_pos_end)
ASSERT.assertTrue(tensor_util.is_tensor_equal(expect_pos_begin, pos_begin))
ASSERT.assertTrue(tensor_util.is_tensor_equal(expect_pos_end, pos_end))
expect_character_length = len(sentence)
expect_squeeze_gaz_word_length = len(expect_squeeze_gaz_words_0)
character_length = model_inputs.model_inputs["sequence_length"][0]
squeeze_word_length = model_inputs.model_inputs["squeeze_gaz_word_length"][
0]
ASSERT.assertEqual(expect_character_length, character_length.item())
ASSERT.assertEqual(expect_squeeze_gaz_word_length,
squeeze_word_length.item())
def __call__(self, config: Dict, train_type: int):
serialize_dir = config["serialize_dir"]
vocabulary_dir = config["vocabulary_dir"]
pretrained_embedding_file_path = config["pretrained_embedding_file_path"]
word_embedding_dim = config["word_embedding_dim"]
pretrained_embedding_max_size = config["pretrained_embedding_max_size"]
is_fine_tuning = config["fine_tuning"]
word_vocab_dir = os.path.join(vocabulary_dir, "vocabulary", "word_vocabulary")
event_type_vocab_dir = os.path.join(vocabulary_dir, "vocabulary", "event_type_vocabulary")
entity_tag_vocab_dir = os.path.join(vocabulary_dir, "vocabulary", "entity_tag_vocabulary")
if train_type == Train.NEW_TRAIN:
if os.path.isdir(serialize_dir):
shutil.rmtree(serialize_dir)
os.makedirs(serialize_dir)
if os.path.isdir(vocabulary_dir):
shutil.rmtree(vocabulary_dir)
os.makedirs(vocabulary_dir)
os.makedirs(word_vocab_dir)
os.makedirs(event_type_vocab_dir)
os.makedirs(entity_tag_vocab_dir)
elif train_type == Train.RECOVERY_TRAIN:
pass
else:
assert False, f"train_type: {train_type} error!"
train_dataset_file_path = config["train_dataset_file_path"]
validation_dataset_file_path = config["validation_dataset_file_path"]
num_epoch = config["epoch"]
batch_size = config["batch_size"]
if train_type == Train.NEW_TRAIN:
# 构建词汇表
ace_dataset = ACEDataset(train_dataset_file_path)
vocab_data_loader = DataLoader(dataset=ace_dataset,
batch_size=10,
shuffle=False, num_workers=0,
collate_fn=EventVocabularyCollate())
tokens: List[List[str]] = list()
event_types: List[List[str]] = list()
entity_tags: List[List[str]] = list()
for colleta_dict in vocab_data_loader:
tokens.extend(colleta_dict["tokens"])
event_types.extend(colleta_dict["event_types"])
entity_tags.extend(colleta_dict["entity_tags"])
word_vocabulary = Vocabulary(tokens=tokens,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True)
glove_loader = GloveLoader(embedding_dim=word_embedding_dim,
pretrained_file_path=pretrained_embedding_file_path,
max_size=pretrained_embedding_max_size)
pretrained_word_vocabulary = PretrainedVocabulary(vocabulary=word_vocabulary,
pretrained_word_embedding_loader=glove_loader)
pretrained_word_vocabulary.save_to_file(word_vocab_dir)
event_type_vocabulary = Vocabulary(tokens=event_types,
padding="",
unk="Negative",
special_first=True)
event_type_vocabulary.save_to_file(event_type_vocab_dir)
entity_tag_vocabulary = LabelVocabulary(labels=entity_tags,
padding=LabelVocabulary.PADDING)
entity_tag_vocabulary.save_to_file(entity_tag_vocab_dir)
else:
pretrained_word_vocabulary = PretrainedVocabulary.from_file(word_vocab_dir)
event_type_vocabulary = Vocabulary.from_file(event_type_vocab_dir)
entity_tag_vocabulary = Vocabulary.from_file(entity_tag_vocab_dir)
model = EventModel(alpha=0.5,
activate_score=True,
sentence_vocab=pretrained_word_vocabulary,
sentence_embedding_dim=word_embedding_dim,
entity_tag_vocab=entity_tag_vocabulary,
entity_tag_embedding_dim=50,
event_type_vocab=event_type_vocabulary,
event_type_embedding_dim=300,
lstm_hidden_size=300,
lstm_encoder_num_layer=1,
lstm_encoder_droupout=0.4)
trainer = Trainer(
serialize_dir=serialize_dir,
num_epoch=num_epoch,
model=model,
loss=EventLoss(),
optimizer_factory=EventOptimizerFactory(is_fine_tuning=is_fine_tuning),
metrics=EventF1MetricAdapter(event_type_vocabulary=event_type_vocabulary),
patient=10,
num_check_point_keep=5,
devices=None
)
train_dataset = EventDataset(dataset_file_path=train_dataset_file_path,
event_type_vocabulary=event_type_vocabulary)
validation_dataset = EventDataset(dataset_file_path=validation_dataset_file_path,
event_type_vocabulary=event_type_vocabulary)
event_collate = EventCollate(word_vocabulary=pretrained_word_vocabulary,
event_type_vocabulary=event_type_vocabulary,
entity_tag_vocabulary=entity_tag_vocabulary,
sentence_max_len=512)
train_data_loader = DataLoader(dataset=train_dataset,
batch_size=batch_size,
num_workers=0,
collate_fn=event_collate)
validation_data_loader = DataLoader(dataset=validation_dataset,
batch_size=batch_size,
num_workers=0,
collate_fn=event_collate)
if train_type == Train.NEW_TRAIN:
trainer.train(train_data_loader=train_data_loader,
validation_data_loader=validation_data_loader)
else:
trainer.recovery_train(train_data_loader=train_data_loader,
validation_data_loader=validation_data_loader)
def test_bilstm_gat_model_collate(lattice_ner_demo_dataset,
gaz_pretrained_embedding_loader):
"""
测试 bilstm gat model collate
:return:
"""
# 仅仅取前两个作为测试
batch_instances = lattice_ner_demo_dataset[0:2]
vocabulary_collate = VocabularyCollate()
collate_result = vocabulary_collate(batch_instances)
tokens = collate_result["tokens"]
sequence_label = collate_result["sequence_labels"]
token_vocabulary = Vocabulary(tokens=tokens,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True)
label_vocabulary = LabelVocabulary(labels=sequence_label,
padding=LabelVocabulary.PADDING)
gazetter = Gazetteer(
gaz_pretrained_word_embedding_loader=gaz_pretrained_embedding_loader)
gaz_vocabulary_collate = GazVocabularyCollate(gazetteer=gazetter)
gaz_words = gaz_vocabulary_collate(batch_instances)
gaz_vocabulary = Vocabulary(tokens=gaz_words,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True)
gaz_vocabulary = PretrainedVocabulary(
vocabulary=gaz_vocabulary,
pretrained_word_embedding_loader=gaz_pretrained_embedding_loader)
bilstm_gat_model_collate = BiLstmGATModelCollate(
token_vocabulary=token_vocabulary,
gazetter=gazetter,
gaz_vocabulary=gaz_vocabulary,
label_vocabulary=label_vocabulary)
model_inputs = bilstm_gat_model_collate(batch_instances)
logging.debug(json2str(model_inputs.model_inputs["metadata"]))
t_graph_0 = model_inputs.model_inputs["t_graph"][0]
c_graph_0 = model_inputs.model_inputs["c_graph"][0]
l_graph_0 = model_inputs.model_inputs["l_graph"][0]
expect_t_graph_tensor = torch.tensor(expect_t_graph, dtype=torch.uint8)
ASSERT.assertTrue(is_tensor_equal(expect_t_graph_tensor, t_graph_0))
expect_c_graph_tensor = torch.tensor(expect_c_graph, dtype=torch.uint8)
ASSERT.assertTrue(is_tensor_equal(expect_c_graph_tensor, c_graph_0))
expect_l_graph_tensor = torch.tensor(expect_l_graph, dtype=torch.uint8)
ASSERT.assertTrue(is_tensor_equal(expect_l_graph_tensor, l_graph_0))
gaz_words_indices = model_inputs.model_inputs["gaz_words"]
ASSERT.assertEqual((2, 11), gaz_words_indices.size())
metadata_0 = model_inputs.model_inputs["metadata"][0]
# 陈元呼吁加强国际合作推动世界经济发展
expect_squeeze_gaz_words_0 = [
"陈元", "呼吁", "吁加", "加强", "强国", "国际", "合作", "推动", "世界", "经济", "发展"
]
sequeeze_gaz_words_0 = metadata_0["sequeeze_gaz_words"]
ASSERT.assertListEqual(expect_squeeze_gaz_words_0, sequeeze_gaz_words_0)
expect_squeeze_gaz_words_indices_0 = torch.tensor(
[gaz_vocabulary.index(word) for word in expect_squeeze_gaz_words_0],
dtype=torch.long)
ASSERT.assertTrue(
is_tensor_equal(expect_squeeze_gaz_words_indices_0,
gaz_words_indices[0]))
def test_gaz_model_collate(lattice_ner_demo_dataset,
gaz_pretrained_embedding_loader):
# 仅仅取前两个作为测试
batch_instances = lattice_ner_demo_dataset[0:2]
vocabulary_collate = VocabularyCollate()
collate_result = vocabulary_collate(batch_instances)
tokens = collate_result["tokens"]
sequence_label = collate_result["sequence_labels"]
token_vocabulary = Vocabulary(tokens=tokens,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True)
label_vocabulary = LabelVocabulary(labels=sequence_label,
padding=LabelVocabulary.PADDING)
gazetter = Gazetteer(
gaz_pretrained_word_embedding_loader=gaz_pretrained_embedding_loader)
gaz_vocabulary_collate = GazVocabularyCollate(gazetteer=gazetter)
gaz_words = gaz_vocabulary_collate(batch_instances)
gaz_vocabulary = Vocabulary(tokens=gaz_words,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True)
gaz_vocabulary = PretrainedVocabulary(
vocabulary=gaz_vocabulary,
pretrained_word_embedding_loader=gaz_pretrained_embedding_loader)
lattice_model_collate = LatticeModelCollate(
token_vocabulary=token_vocabulary,
gazetter=gazetter,
gaz_vocabulary=gaz_vocabulary,
label_vocabulary=label_vocabulary)
model_inputs = lattice_model_collate(batch_instances)
logging.debug(json2str(model_inputs.model_inputs["metadata"]))
metadata_0 = model_inputs.model_inputs["metadata"][0]
# 陈元呼吁加强国际合作推动世界经济发展
expect_gaz_words_0 = [["陈元"], [], ["呼吁"], ["吁加"], ["加强"], ["强国"], ["国际"],
[], ["合作"], [], ["推动"], [], ["世界"], [], ["经济"], [],
["发展"], []]
gaz_words_0 = metadata_0["gaz_words"]
ASSERT.assertListEqual(expect_gaz_words_0, gaz_words_0)
gaz_list_0 = model_inputs.model_inputs["gaz_list"][0]
expect_gaz_list_0 = list()
for expect_gaz_word in expect_gaz_words_0:
if len(expect_gaz_word) > 0:
indices = [gaz_vocabulary.index(word) for word in expect_gaz_word]
lengthes = [len(word) for word in expect_gaz_word]
expect_gaz_list_0.append([indices, lengthes])
else:
expect_gaz_list_0.append([])
logging.debug(
f"expect_gaz_list_0: {json2str(expect_gaz_list_0)}\n gaz_list_0:{json2str(gaz_list_0)}"
)
ASSERT.assertListEqual(expect_gaz_list_0, gaz_list_0)
tokens_0 = model_inputs.model_inputs["tokens"]
ASSERT.assertEqual((2, 19), tokens_0.size())
sequence_label_0 = model_inputs.labels
ASSERT.assertEqual((2, 19), sequence_label_0.size())
# 新华社华盛顿４月2８日电（记者翟景升）
expect_gaz_word_1 = [
["新华社", "新华"], # 新
["华社"], # 华
["社华"], # 社
["华盛顿", "华盛"], # 华
["盛顿"], # 盛
[], # 顿
[], # 4
[], # 月
[], # 2
[], # 8
[], # 日
[], # 电
[], # （
["记者"], # 记
[], # 者
["翟景升", "翟景"], # 翟
["景升"], # 景
[], # 升
[]
] # ）
metadata_1 = model_inputs.model_inputs["metadata"][1]
gaz_words_1 = metadata_1["gaz_words"]
ASSERT.assertListEqual(expect_gaz_word_1, gaz_words_1)
expect_gaz_list_1 = list()
for expect_gaz_word in expect_gaz_word_1:
if len(expect_gaz_word) > 0:
indices = [gaz_vocabulary.index(word) for word in expect_gaz_word]
lengthes = [len(word) for word in expect_gaz_word]
expect_gaz_list_1.append([indices, lengthes])
else:
expect_gaz_list_1.append([])
gaz_list_1 = model_inputs.model_inputs["gaz_list"][1]
ASSERT.assertListEqual(expect_gaz_list_1, gaz_list_1)