def __init__(self,
tokens_encoder=None,
**kwargs
):
super(OneEdgeModel, self).__init__()
if tokens_encoder is None:
tokens_encoder = modules.ConvWordsEncoder(**kwargs)
self._tokens_encoder: nn.Module = tokens_encoder
def __init__(self,
tokens_encoder=None,
**kwargs
):
super(PooledEdgesModel, self).__init__()
if tokens_encoder is None:
tokens_encoder = modules.ConvWordsEncoder(**kwargs)
self._tokens_encoder: nn.Module = tokens_encoder
self._pool = self._tokens_encoder._pool
def __init__(self,
tokens_encoder=None,
**kwargs
):
super(STAGGModel, self).__init__()
if tokens_encoder is None:
tokens_encoder = modules.ConvWordsEncoder(**kwargs)
self._tokens_encoder: nn.Module = tokens_encoder
self._weights_layer = nn.Sequential(nn.Linear(in_features=9,
out_features=1),
nn.ReLU()
)
def __init__(self, tokens_encoder=None, **kwargs):
super(GNNModel, self).__init__()
if tokens_encoder is None:
tokens_encoder = modules.ConvWordsEncoder(**kwargs)
self._tokens_encoder: nn.Module = tokens_encoder
self.output_vector_size = tokens_encoder.output_vector_size // 2
self._gnn: nn.Module = GNN(
self._tokens_encoder._word_embedding.embedding_dim,
tokens_encoder.output_vector_size,
hp_dropout=kwargs.get("hp_dropout", 0.1),
hp_gated=kwargs.get("hp_gated", True))
# self._pool = nn.AdaptiveMaxPool1d(1)
self._question_layer = nn.Sequential(
nn.Linear(in_features=tokens_encoder.output_vector_size,
out_features=self.output_vector_size), nn.ReLU())
self._graph_layer = nn.Sequential(
nn.Linear(in_features=tokens_encoder.output_vector_size,
out_features=self.output_vector_size), nn.ReLU())