def __init__(
self,
dictionary,
num_chars,
char_embed_dim,
token_embed_dim,
normalize_embed,
char_rnn_units,
char_rnn_layers,
hidden_dim,
num_layers,
dropout_in,
dropout_out,
residual_level,
bidirectional,
):
super().__init__(dictionary)
self.dropout_in = dropout_in
self.embed_chars = char_encoder.CharRNNModel(
dictionary=dictionary,
num_chars=num_chars,
char_embed_dim=char_embed_dim,
char_rnn_units=char_rnn_units,
char_rnn_layers=char_rnn_layers,
)
self.embed_tokens = None
if token_embed_dim > 0:
self.embed_tokens = rnn.Embedding(
num_embeddings=len(dictionary),
embedding_dim=token_embed_dim,
padding_idx=dictionary.pad(),
freeze_embed=False,
normalize_embed=normalize_embed,
)
self.word_dim = char_rnn_units + token_embed_dim
self.bilstm = rnn.BiLSTM(
num_layers=num_layers,
bidirectional=bidirectional,
embed_dim=self.word_dim,
hidden_dim=hidden_dim,
dropout=dropout_out,
residual_level=residual_level,
)
# disables sorting and word-length thresholding if True
# (enables ONNX tracing of length-sorted input with batch_size = 1)
self.onnx_export_model = False
def __init__(
self,
dictionary,
num_chars=50,
unk_only_char_encoding=False,
embed_dim=32,
token_embed_dim=256,
freeze_embed=False,
normalize_embed=False,
char_cnn_params="[(128, 3), (128, 5)]",
char_cnn_nonlinear_fn="tanh",
char_cnn_pool_type="max",
char_cnn_num_highway_layers=0,
char_cnn_output_dim=-1,
hidden_dim=512,
num_layers=1,
dropout_in=0.1,
dropout_out=0.1,
residual_level=None,
bidirectional=False,
word_dropout_params=None,
use_pretrained_weights=False,
finetune_pretrained_weights=False,
weights_file=None,
):
super().__init__(dictionary)
self.dropout_in = dropout_in
convolutions_params = literal_eval(char_cnn_params)
self.char_cnn_encoder = char_encoder.CharCNNModel(
dictionary,
num_chars,
embed_dim,
convolutions_params,
char_cnn_nonlinear_fn,
char_cnn_pool_type,
char_cnn_num_highway_layers,
char_cnn_output_dim,
use_pretrained_weights,
finetune_pretrained_weights,
weights_file,
)
self.embed_tokens = None
num_tokens = len(dictionary)
self.padding_idx = dictionary.pad()
self.unk_idx = dictionary.unk()
if token_embed_dim > 0:
self.embed_tokens = rnn.Embedding(
num_embeddings=num_tokens,
embedding_dim=token_embed_dim,
padding_idx=self.padding_idx,
freeze_embed=freeze_embed,
normalize_embed=normalize_embed,
)
self.word_dim = (
char_cnn_output_dim
if char_cnn_output_dim != -1
else sum(out_dim for (out_dim, _) in convolutions_params)
)
self.token_embed_dim = token_embed_dim
self.unk_only_char_encoding = unk_only_char_encoding
if self.unk_only_char_encoding:
assert char_cnn_output_dim == token_embed_dim, (
"char_cnn_output_dim (%d) must equal to token_embed_dim (%d)"
% (char_cnn_output_dim, token_embed_dim)
)
self.word_dim = token_embed_dim
else:
self.word_dim = self.word_dim + token_embed_dim
self.bilstm = rnn.BiLSTM(
num_layers=num_layers,
bidirectional=bidirectional,
embed_dim=self.word_dim,
hidden_dim=hidden_dim,
dropout=dropout_out,
residual_level=residual_level,
)
# Variable tracker
self.tracker = VariableTracker()
# Initialize adversarial mode
self.set_gradient_tracking_mode(False)
self.set_embed_noising_mode(False)
