pyabsa.tasks.AspectTermExtraction.models.__lcf__.lcfs_atepc_large

Classes

LCFS_ATEPC_LARGE

Base class for all neural network modules.

Module Contents

class pyabsa.tasks.AspectTermExtraction.models.__lcf__.lcfs_atepc_large.LCFS_ATEPC_LARGE(bert_base_model, config)

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 them to be nested 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) -> None:
        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 also have their parameters converted 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.

bert4global

config

bert4local

dropout

SA1

SA2

linear_double

linear_triple

pooler

dense

num_labels

classifier

get_batch_token_labels_bert_base_indices(labels)

get_ids_for_local_context_extractor(text_indices)

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)