Source code for pyabsa.tasks.AspectTermExtraction.models.__lcf__.lcf_template_atepc

# -*- coding: utf-8 -*-
# file: lcf_template_atepc.py
# time: 2021/6/22
# author: YANG, HENG <hy345@exeter.ac.uk> (杨恒)
# github: https://github.com/yangheng95
# Copyright (C) 2021. All Rights Reserved.


import numpy as np
import torch
import torch.nn as nn
from transformers.models.bert.modeling_bert import BertForTokenClassification




[docs]
class LCF_TEMPLATE_ATEPC(nn.Module):
    def __init__(self, bert_base_model, config):
        super(LCF_TEMPLATE_ATEPC, self).__init__()
        bert_config = bert_base_model.config
        self.bert4global = bert_base_model
        self.config = config
        self.bert4local = self.bert4global
        self.dropout = nn.Dropout(self.config.dropout)

        self.num_labels = config.get("num_labels", 0)
        self.classifier = nn.Linear(config.hidden_dim, self.num_labels)



[docs]
    def get_batch_token_labels_bert_base_indices(self, labels):
        if labels is None:
            return
        # convert tags of BERT-SPC input to BERT-BASE format
        labels = labels.detach().cpu().numpy()
        for text_i in range(len(labels)):
            sep_index = np.argmax((labels[text_i] == self.num_labels - 1))
            labels[text_i][sep_index + 1 :] = 0
        return torch.tensor(labels).to(self.config.device)





[docs]
    def get_ids_for_local_context_extractor(self, text_indices):
        # convert BERT-SPC input to BERT-BASE format
        text_ids = text_indices.detach().cpu().numpy()
        for text_i in range(len(text_ids)):
            sep_index = np.argmax((text_ids[text_i] == self.config.sep_indices))
            text_ids[text_i][sep_index + 1 :] = 0
        return torch.tensor(text_ids).to(self.config.device)





[docs]
    def forward(
        self,
        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,
    ):
        lcf_cdm_vec = lcf_cdm_vec.unsqueeze(2) if lcf_cdm_vec is not None else None
        lcf_cdw_vec = lcf_cdw_vec.unsqueeze(2) if lcf_cdw_vec is not None else None
        raise NotImplementedError(
            "This is a template ATEPC model based on LCF, "
            "please implement your model use this template."
        )