pyabsa.tasks.AspectPolarityClassification.models.__plm__.cabasc_bert
Module Contents
Classes
Base class for all neural network modules. |
- class pyabsa.tasks.AspectPolarityClassification.models.__plm__.cabasc_bert.Cabasc_BERT(bert, config, _type='c')[source]
Bases:
torch.nn.Module
Base class for all neural network modules.
Your models should also subclass this class.
Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:
import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x))
Submodules assigned in this way will be registered, and will have their parameters converted too when you call
to()
, etc.Note
As per the example above, an
__init__()
call to the parent class must be made before assignment on the child.- Variables:
training (bool) – Boolean represents whether this module is in training or evaluation mode.
- inputs = ['text_indices', 'aspect_indices', 'left_with_aspect_indices', 'right_with_aspect_indices'][source]
- locationed_memory(memory, memory_len)[source]
# differ from description in paper here, but may be better for i in range(memory.size(0)):
- for idx in range(memory_len[i]):
aspect_start = left_len[i] - aspect_len[i] aspect_end = left_len[i] if idx < aspect_start: l = aspect_start.item() - idx elif idx <= aspect_end: l = 0 else: l = idx - aspect_end.item() memory[i][idx] *= (1-float(l)/int(memory_len[i]))