def __init__(self, config):
super().__init__(config)
self.embeddings = BertEmbeddings(config)
self.encoder = BertEncoder(config)
# self.apply(self.init_weights) # old versions of pytorch_transformers
self.init_weights()
def __init__(self, config, depth=None):
super(CustomBertModel, self).__init__(config)
self.depth = depth
self.embeddings = BertEmbeddings(config)
self.encoder = BertEncoder(config)
self.cls = BertPreTrainingHeads(config)
self.apply(self.init_weights)
def __init__(self, config, args):
super(BERT_MAG_model, self).__init__(config)
self.newly_added_config = args
if args.output_mode == 'regression':
self.num_labels = 1
#BertEncoder
self.output_attentions = self.config.output_attentions
self.output_hidden_states = self.config.output_hidden_states
self.layer = nn.ModuleList([
BertLayer(self.config)
for _ in range(self.config.num_hidden_layers)
])
self.MAG = MAG(self.config, args)
self.MAG_all = nn.ModuleList([
MAG(self.config, args)
for _ in range(self.config.num_hidden_layers)
])
# MultimodalBertModel
self.embeddings = BertEmbeddings(self.config)
self.pooler = BertPooler(self.config)
# MultimodalBertForSequenceClassification
self.classifier = nn.Linear(self.config.hidden_size, self.num_labels)
self.dropout = nn.Dropout(args["hidden_dropout_prob"])
self.apply(self.init_weights)
def __init__(self, config):
super(BertModel, self).__init__(config)
self.embeddings = BertEmbeddings(config)
self.encoder = BertEncoder(config)
self.pooler = BertPooler(config)
self.apply(self.init_weights)