def _build_context_encoder(self):
"""
Build the context (i.e. dialogue history) encoder.
"""
if self.opt.get("share_encoder"):
self.context_encoder = self.label_encoder
else:
if (self.opt["load_context_encoder_from"] is None
and self.opt["context_encoder_embedding_type"]
== "fasttext_cc"):
embeddings = load_fasttext_embeddings(
self.dictionary, self.opt["embedding_size"],
self.opt["datapath"])
else:
embeddings = nn.Embedding(len(self.dictionary),
self.opt["embedding_size"])
self.context_encoder = TransformerEncoder(
opt=self.opt,
embedding=embeddings,
vocabulary_size=len(self.dictionary),
padding_idx=self.dictionary.tok2ind[
self.dictionary.null_token],
embeddings_scale=False,
output_scaling=1.0,
)
if self.opt.get("load_context_encoder_from") is not None:
self._load_context_encoder_state()
def _build_context_encoder(self):
"""
Build the context (i.e. dialogue history) encoder.
"""
if self.opt.get("share_encoder"):
self.context_encoder = self.label_encoder
else:
if (self.opt["load_context_encoder_from"] is None
and self.opt["context_encoder_embedding_type"]
== "fasttext_cc"):
embeddings = load_fasttext_embeddings(
self.dictionary, self.opt["embedding_size"],
self.opt["datapath"])
else:
embeddings = nn.Embedding(len(self.dictionary),
self.opt["embedding_size"])
self.context_encoder = TransformerEncoder(
n_heads=self.opt["n_heads"],
n_layers=self.opt["n_layers"],
embedding_size=self.opt["embedding_size"],
ffn_size=self.opt["ffn_size"],
vocabulary_size=len(self.dictionary),
embedding=embeddings,
dropout=self.opt["dropout"],
attention_dropout=self.opt["attention_dropout"],
relu_dropout=self.opt["relu_dropout"],
padding_idx=self.dictionary.tok2ind[
self.dictionary.null_token],
learn_positional_embeddings=self.
opt["learn_positional_embeddings"],
embeddings_scale=False,
n_positions=self.opt["n_positions"],
activation=self.opt["activation"],
variant=self.opt["variant"],
n_segments=self.opt["n_segments"],
)
if self.opt.get("load_context_encoder_from") is not None:
self._load_context_encoder_state()