def __init__(self, **kwargs):
super().__init__(**kwargs)
# Unpack args
mention_word_embs = kwargs['mention_word_embs']
mention_ent_embs = kwargs['mention_ent_embs']
autoencoder_state_dict = kwargs['autoencoder_state_dict']
hidden_size = kwargs['hidden_size']
char_embs = kwargs['char_embs']
# Mention embeddings
self.mention_word_embs = nn.Embedding(*mention_word_embs.shape,
padding_idx=0,
sparse=self.args.sparse)
self.mention_word_embs.weight.data.copy_(
np_to_tensor(mention_word_embs))
self.mention_word_embs.requires_grad = self.args.train_mention
# Entity mention embeddings
self.mention_ent_embs = nn.Embedding(*mention_ent_embs.shape,
padding_idx=0,
sparse=self.args.sparse)
self.mention_ent_embs.weight.data.copy_(np_to_tensor(mention_ent_embs))
self.mention_ent_embs.requires_grad = self.args.train_mention
# Dropout
self.dp = nn.Dropout(self.args.dp)
##### Autoencoder #####
max_char_size = self.args.max_char_size
self.autoencoder = StringAutoEncoder(max_char_size=max_char_size,
hidden_size=hidden_size,
char_embs=char_embs,
dp=self.args.dp,
activate=self.args.activate)
self.autoencoder.load_state_dict(autoencoder_state_dict)
self.autoencoder.requires_grad = False
# Linear
self.context_linear = nn.Linear(
self.args.mention_word_dim + self.args.context_word_dim +
hidden_size, 1)
self.prior_linear = nn.Linear(
self.args.mention_word_dim + self.args.context_word_dim +
hidden_size, 1)
self.str_linear = nn.Linear(
self.args.mention_word_dim + self.args.context_word_dim +
hidden_size, 1)
init_func = getattr(nn.init, self.args.init_linear)
logger.info(f'Initializing Linear layers using {init_func.__name__}')
init_func(self.context_linear.weight)
init_func(self.prior_linear.weight)
init_func(self.str_linear.weight)
# Sigmoid
self.sigmoid = nn.Sigmoid()
def __init__(self, **kwargs):
super().__init__(**kwargs)
# Unpack args
mention_word_embs = kwargs['mention_word_embs']
autoencoder_state_dict = kwargs['autoencoder_state_dict']
hidden_size = kwargs['hidden_size']
char_embs = kwargs['char_embs']
total_dims = self.args.mention_word_dim + self.args.context_word_dim + hidden_size
ent_embs = torch.load(
join(self.args.data_path, 'ent_combined_embs.pickle'))
del self.ent_embs
# Mention embeddings
self.mention_word_embs = nn.Embedding(*mention_word_embs.shape,
padding_idx=0,
sparse=self.args.sparse)
self.mention_word_embs.weight.data.copy_(
np_to_tensor(mention_word_embs))
self.mention_word_embs.requires_grad = self.args.train_mention
# Entity mention embeddings
self.ent_combined_embs = nn.Embedding(*ent_embs.shape,
padding_idx=0,
sparse=self.args.sparse)
self.ent_combined_embs.weight.data.copy_(np_to_tensor(ent_embs))
# Dropout
self.dp = nn.Dropout(self.args.dp)
##### Autoencoder #####
max_char_size = self.args.max_char_size
self.autoencoder = StringAutoEncoder(max_char_size=max_char_size,
hidden_size=hidden_size,
char_embs=char_embs,
dp=self.args.dp,
activate=self.args.activate)
self.autoencoder.load_state_dict(autoencoder_state_dict)
for p in self.autoencoder.parameters():
p.requires_grad = False
self.linear1 = nn.Linear(total_dims, total_dims)
self.linear2 = nn.Linear(total_dims, total_dims)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
init_func = getattr(nn.init, self.args.init_linear)
init_func(self.linear1.weight)
init_func(self.linear2.weight)
if self.args.num_linear == 2:
self.gate_net = nn.Sequential(self.linear1, self.relu,
self.linear2, self.sigmoid)
else:
self.gate_net = nn.Sequential(self.linear1, self.sigmoid)
def __init__(self, **kwargs):
super().__init__(**kwargs)
# Unpack args
mention_word_embs = kwargs['mention_word_embs']
mention_ent_embs = kwargs['mention_ent_embs']
# Mention embeddings
self.mention_word_embs = nn.Embedding(*mention_word_embs.shape,
padding_idx=0,
sparse=self.args.sparse)
self.mention_word_embs.weight.data.copy_(
np_to_tensor(mention_word_embs))
# Entity mention embeddings
self.mention_ent_embs = nn.Embedding(*mention_ent_embs.shape,
padding_idx=0,
sparse=self.args.sparse)
self.mention_ent_embs.weight.data.copy_(np_to_tensor(mention_ent_embs))
# Dp
self.dp = nn.Dropout(self.args.dp)
def __init__(self, **kwargs):
super().__init__(**kwargs)
# Unpack args
mention_word_embs = kwargs['mention_word_embs']
mention_ent_embs = kwargs['mention_ent_embs']
autoencoder_state_dict = kwargs['autoencoder_state_dict']
hidden_size = kwargs['hidden_size']
char_embs = kwargs['char_embs']
# Mention embeddings
self.mention_word_embs = nn.Embedding(*mention_word_embs.shape, padding_idx=0, sparse=self.args.sparse)
self.mention_word_embs.weight.data.copy_(np_to_tensor(mention_word_embs))
self.mention_word_embs.requires_grad = self.args.train_mention
# Entity mention embeddings
self.mention_ent_embs = nn.Embedding(*mention_ent_embs.shape, padding_idx=0, sparse=self.args.sparse)
self.mention_ent_embs.weight.data.copy_(np_to_tensor(mention_ent_embs))
self.mention_ent_embs.requires_grad = self.args.train_mention
# Dropout
self.dp = nn.Dropout(self.args.dp)
##### Autoencoder #####
max_char_size = self.args.max_char_size
self.autoencoder = StringAutoEncoder(max_char_size=max_char_size,
hidden_size=hidden_size,
char_embs=char_embs,
dp=self.args.dp,
activate=self.args.activate)
self.autoencoder.load_state_dict(autoencoder_state_dict)
self.autoencoder.requires_grad = False
total_dims = self.args.mention_word_dim + self.args.context_word_dim + hidden_size
self.combine_linear = nn.Linear(total_dims, total_dims, bias=False)
init_func = getattr(nn.init, self.args.init_linear)
init_func(self.combine_linear.weight)