def __init__(self, n_lstm_hidden=400, n_mlp_sib=100, mlp_dropout=.33, **kwargs):
super().__init__(**kwargs)
self.mlp_sib_s = MLP(n_in=n_lstm_hidden*2, n_out=n_mlp_sib, dropout=mlp_dropout)
self.mlp_sib_d = MLP(n_in=n_lstm_hidden*2, n_out=n_mlp_sib, dropout=mlp_dropout)
self.mlp_sib_h = MLP(n_in=n_lstm_hidden*2, n_out=n_mlp_sib, dropout=mlp_dropout)
self.sib_attn = Triaffine(n_in=n_mlp_sib, bias_x=True, bias_y=True)
self.crf = CRF2oDependency()
def __init__(self,
n_words,
n_feats,
n_rels,
feat='char',
n_embed=100,
n_feat_embed=100,
n_char_embed=50,
bert=None,
n_bert_layers=4,
mix_dropout=.0,
embed_dropout=.33,
n_lstm_hidden=400,
n_lstm_layers=3,
lstm_dropout=.33,
n_mlp_arc=500,
n_mlp_sib=100,
n_mlp_rel=100,
mlp_dropout=.33,
feat_pad_index=0,
pad_index=0,
unk_index=1,
**kwargs):
super().__init__(**Config().update(locals()))
# the embedding layer
self.word_embed = nn.Embedding(num_embeddings=n_words,
embedding_dim=n_embed)
if feat == 'char':
self.feat_embed = CharLSTM(n_chars=n_feats,
n_embed=n_char_embed,
n_out=n_feat_embed,
pad_index=feat_pad_index)
elif feat == 'bert':
self.feat_embed = BertEmbedding(model=bert,
n_layers=n_bert_layers,
n_out=n_feat_embed,
pad_index=feat_pad_index,
dropout=mix_dropout)
self.n_feat_embed = self.feat_embed.n_out
elif feat == 'tag':
self.feat_embed = nn.Embedding(num_embeddings=n_feats,
embedding_dim=n_feat_embed)
else:
raise RuntimeError(
"The feat type should be in ['char', 'bert', 'tag'].")
self.embed_dropout = IndependentDropout(p=embed_dropout)
# the lstm layer
self.lstm = LSTM(input_size=n_embed + n_feat_embed,
hidden_size=n_lstm_hidden,
num_layers=n_lstm_layers,
bidirectional=True,
dropout=lstm_dropout)
self.lstm_dropout = SharedDropout(p=lstm_dropout)
# the MLP layers
self.mlp_arc_d = MLP(n_in=n_lstm_hidden * 2,
n_out=n_mlp_arc,
dropout=mlp_dropout)
self.mlp_arc_h = MLP(n_in=n_lstm_hidden * 2,
n_out=n_mlp_arc,
dropout=mlp_dropout)
self.mlp_sib_s = MLP(n_in=n_lstm_hidden * 2,
n_out=n_mlp_sib,
dropout=mlp_dropout)
self.mlp_sib_d = MLP(n_in=n_lstm_hidden * 2,
n_out=n_mlp_sib,
dropout=mlp_dropout)
self.mlp_sib_h = MLP(n_in=n_lstm_hidden * 2,
n_out=n_mlp_sib,
dropout=mlp_dropout)
self.mlp_rel_d = MLP(n_in=n_lstm_hidden * 2,
n_out=n_mlp_rel,
dropout=mlp_dropout)
self.mlp_rel_h = MLP(n_in=n_lstm_hidden * 2,
n_out=n_mlp_rel,
dropout=mlp_dropout)
# the Biaffine layers
self.arc_attn = Biaffine(n_in=n_mlp_arc, bias_x=True, bias_y=False)
self.sib_attn = Triaffine(n_in=n_mlp_sib, bias_x=True, bias_y=True)
self.rel_attn = Biaffine(n_in=n_mlp_rel,
n_out=n_rels,
bias_x=True,
bias_y=True)
self.criterion = nn.CrossEntropyLoss()
self.pad_index = pad_index
self.unk_index = unk_index
self.crf = CRF2oDependency()