def __init__(self, config):
super(SpanAttention, self).__init__()
# create modules
self.attention = SpanWordAttention(config)
self.output = BertSelfOutput(config)
# initialize weights
self.init_weights()
def __init__(self, config, add_conditional_layernorm=True):
super().__init__(config)
self.self = MyBertSelfAttention10(config)
self.add_conditional_layernorm = add_conditional_layernorm
if add_conditional_layernorm:
self.output = MyBertSelfOutput10(config)
else:
self.output = BertSelfOutput(config)
self.pruned_heads = set()
def __init__(self, config: BertConfig, bias=False):
super().__init__()
self.config = config
self.head_num = config.num_attention_heads
self.head_dim = config.hidden_size // self.head_num
self.all_dim = self.head_num * self.head_dim
self.query = nn.Linear(config.hidden_size, self.all_dim, bias=bias)
self.kv = nn.Linear(config.hidden_size, self.all_dim, bias=bias)
self.output = BertSelfOutput(config)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.scale = 1 / (self.head_dim**0.5)
def __init__(self, config):
super(EntityAwareAttention, self).__init__()
self.self = EntityAwareSelfAttention(config)
self.output = BertSelfOutput(config)
def __init__(self, config):
super(BertAttention, self).__init__()
self.self = BertSelfAttention(config)
self.output = BertSelfOutput(config)
self.pruned_heads = set()
def __init__(self, config):
super().__init__()
self.att = BertSelfAttention(config)
self.output = BertSelfOutput(config)
def __init__(self, config):
super().__init__()
self.self = BertScanAttentionHeads(config)
self.output = BertSelfOutput(config)
def __init__(self, config):
super(CaptionBertAttention, self).__init__(config)
self.self = CaptionBertSelfAttention(config)
self.output = BertSelfOutput(config)
def __init__(self, config):
super(BertAttention_attention, self).__init__()
self.self = BertSelfAttention_attention(config)
self.output = BertSelfOutput(config)
