pyabsa.tasks.AspectTermExtraction.models.__lcf__.lcfs_atepc

Module Contents

Classes

LCFS_ATEPC

Base class for all neural network modules.
class pyabsa.tasks.AspectTermExtraction.models.__lcf__.lcfs_atepc.LCFS_ATEPC(bert_base_model, config)[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.

get_batch_token_labels_bert_base_indices(labels)[source]
get_ids_for_local_context_extractor(text_indices)[source]
forward(input_ids_spc, token_type_ids=None, attention_mask=None, labels=None, polarity=None, valid_ids=None, attention_mask_label=None, lcf_cdm_vec=None, lcf_cdw_vec=None)[source]