def create(
cls,
label_size,
intent_size,
# BertEmbedder params
model_name='bert-base-multilingual-cased',
mode="weighted",
is_freeze=True,
# BiLSTM
hidden_dim=512,
rnn_layers=1,
lstm_dropout=0.3,
# Attn params
embedding_size=768,
key_dim=64,
val_dim=64,
num_heads=3,
attn_dropout=0.3,
# CRFDecoder params
crf_dropout=0.5,
# Clf params
clf_dropout=0.3,
# Global params
device="cuda"):
embeddings = BERTEmbedder.create(model_name=model_name,
device=device,
mode=mode,
is_freeze=is_freeze)
attn = MultiHeadAttention(key_dim, val_dim, hidden_dim, num_heads,
attn_dropout)
crf = CRFDecoder.create(label_size, hidden_dim, crf_dropout)
clf = ClassDecoder(intent_size, hidden_dim, clf_dropout)
return cls(embeddings, attn, crf, clf, device)
def create(
cls,
label_size,
# BertEmbedder params
model_name='bert-base-multilingual-cased',
mode="weighted",
is_freeze=True,
# Attn params
embedding_size=768,
key_dim=64,
val_dim=64,
num_heads=3,
attn_dropout=0.3,
# NCRFDecoder params
crf_dropout=0.5,
nbest=1,
# Global params
device="cuda"):
embeddings = BERTEmbedder.create(model_name=model_name,
device=device,
mode=mode,
is_freeze=is_freeze)
attn = MultiHeadAttention(key_dim, val_dim, embedding_size, num_heads,
attn_dropout)
crf = NCRFDecoder.create(label_size,
embedding_size,
crf_dropout,
nbest=nbest,
device=device)
return cls(embeddings, attn, crf, device)
