def __init__(self, hparams: Hparams, **kwargs):
super(BertForRoleNer, self).__init__(hparams, **kwargs)
pretrained_hparams = hparams.pretrained
model_hparams = hparams.model_attributes
self.num_labels = hparams.dataset.outputs[0].num
self.initializer_range = model_hparams.initializer_range
self.bert = BaseLayer.by_name(
pretrained_hparams.norm_name)(pretrained_hparams)
self.dropout = tf.keras.layers.Dropout(
model_hparams.hidden_dropout_prob)
# self.bilstm = Bilstm(model_hparams.hidden_size, model_hparams.hidden_dropout_prob, name="bilstm")
self.project = tf.keras.layers.Dense(
model_hparams.hidden_size,
kernel_initializer=get_initializer(
model_hparams.initializer_range),
name="project")
self.ner_output = tf.keras.layers.Dense(
self.num_labels,
kernel_initializer=get_initializer(
model_hparams.initializer_range),
name='ner_output')
self.crf = CRFLayer(self.num_labels,
self.initializer_range,
name="crf_output")
def __init__(self, hparams: Hparams, **kwargs):
super(BertForQA, self).__init__(hparams, **kwargs)
pretrained_hparams = hparams.pretrained
model_hparams = hparams.model_attributes
self.start_n_top = model_hparams.start_n_top
self.seq_len = hparams.dataset.tokenizer.max_len
assert pretrained_hparams.norm_name not in ["xlnet_chinese"], \
ValueError(f"{pretrained_hparams.norm_name} not be supported.")
self.encode_pretrained = BaseLayer.by_name(
pretrained_hparams.norm_name)(pretrained_hparams)
self.qa_layer = BaseLayer.by_name(model_hparams.qa_layer_name)(
model_hparams.hidden_size, self.seq_len, self.start_n_top,
self.start_n_top, get_initializer(model_hparams.initializer_range),
model_hparams.hidden_dropout_prob)
def __init__(self, hparams: Hparams, **kwargs):
super(BertDgcnnForNer, self).__init__(hparams, **kwargs)
pretrained_hparams = hparams.pretrained
model_hparams = hparams.model_attributes
self.num_labels = hparams.dataset.outputs[0].num
self.pos_num = hparams.dataset.inputs[-1].num
self.initializer_range = model_hparams.initializer_range
self.pos_embeddings = tf.keras.layers.Embedding(
self.pos_num,
32,
embeddings_initializer=get_initializer(model_hparams.initializer_range),
name="pos_embedding"
)
self.bert = BaseLayer.by_name(pretrained_hparams.norm_name)(pretrained_hparams)
self.dropout = tf.keras.layers.Dropout(
model_hparams.hidden_dropout_prob
)
self.project = tf.keras.layers.Dense(
model_hparams.hidden_size,
kernel_initializer=get_initializer(model_hparams.initializer_range),
name="project"
)
self.fusion_project = tf.keras.layers.Dense(
model_hparams.hidden_size,
kernel_initializer=get_initializer(model_hparams.initializer_range),
name="fusion_project"
)
self.dgcnn_encoder = DgcnnBlock(model_hparams.hidden_size, [3, 3, 3], [1, 2, 4], name="trigger_dgcnn_encoder")
self.ner_output = tf.keras.layers.Dense(self.num_labels,
kernel_initializer=get_initializer(model_hparams.initializer_range),
name='ner_output')
self.crf = CRFLayer(self.num_labels, self.initializer_range, label_mask=hparams.label_mask, name="crf_output")
def __init__(self, hparams: Hparams, **kwargs):
super(BertForTextGeneration, self).__init__(hparams, **kwargs)
pretrained_hparams = hparams.pretrained
assert pretrained_hparams.norm_name in ['gpt2'], \
ValueError(f"{pretrained_hparams.norm_name} not be supported.")
self.transformer = BaseLayer.by_name(
pretrained_hparams.norm_name)(pretrained_hparams)
def __init__(self, hparams: Hparams, **kwargs):
super(BertForRelationExtract, self).__init__(hparams, **kwargs)
pretrained_hparams = hparams.pretrained
model_hparams = hparams.model_attributes
self.hidden_size = model_hparams.hidden_size
self.num_labels = hparams.dataset.outputs[0].num
self.initializer_range = model_hparams.initializer_range
self.bert = BaseLayer.by_name(
pretrained_hparams.norm_name)(pretrained_hparams)
self.dropout = tf.keras.layers.Dropout(
model_hparams.hidden_dropout_prob)
self.project1 = tf.keras.layers.Dense(
model_hparams.hidden_size,
kernel_initializer=get_initializer(
model_hparams.initializer_range),
name="project1")
self.project2 = tf.keras.layers.Dense(
model_hparams.hidden_size,
kernel_initializer=get_initializer(
model_hparams.initializer_range),
name="project2")
self.project3 = tf.keras.layers.Dense(
model_hparams.hidden_size,
kernel_initializer=get_initializer(
model_hparams.initializer_range),
name="project3")
self.project4 = tf.keras.layers.Dense(
model_hparams.hidden_size,
kernel_initializer=get_initializer(
model_hparams.initializer_range),
name="project4")
self.project5 = tf.keras.layers.Dense(
model_hparams.hidden_size,
kernel_initializer=get_initializer(
model_hparams.initializer_range),
name="project5")
self.e1_attention = MultiHeadAttention(model_hparams,
name="entity1_attention_fusion")
self.e2_attention = MultiHeadAttention(model_hparams,
name="entity2_attention_fusion")
self.attention = MultiHeadAttention(model_hparams,
name="attention_fusion")
self.classifer = tf.keras.layers.Dense(
self.num_labels,
kernel_initializer=get_initializer(
model_hparams.initializer_range),
name="classifier")
def __init__(self, hparams: Hparams, **kwargs):
super(BertForSeqClassification, self).__init__(hparams, **kwargs)
self.num_lables = hparams.dataset.outputs[0].num
pretrained_hparams = hparams.pretrained
model_hparams = hparams.model_attributes
# self.bert = Bert(pretrained_hparams, name='bert')
assert pretrained_hparams.norm_name in ['bert', 'albert', 'albert_brightmart', "ernie", "xlnet", "electra"], \
ValueError(f"{pretrained_hparams.norm_name} not be supported.")
self.encoder = BaseLayer.by_name(
pretrained_hparams.norm_name)(pretrained_hparams)
self.dropout = tf.keras.layers.Dropout(
model_hparams.hidden_dropout_prob)
self.project = tf.keras.layers.Dense(
model_hparams.hidden_size,
kernel_initializer=get_initializer(
model_hparams.initializer_range),
name="project")
self.classifier = tf.keras.layers.Dense(
self.num_lables,
kernel_initializer=get_initializer(
model_hparams.initializer_range),
name="classifier")