python transformer_Python nn.TransformerEncoderLayer方法代码示例

# 需要导入模块: from torch import nn [as 别名]

# 或者: from torch.nn import TransformerEncoderLayer [as 别名]

def __init__(self, bert_config):

"""

:param bert_config: configuration for bert model

"""

super(BertABSATagger, self).__init__(bert_config)

self.num_labels = bert_config.num_labels

self.tagger_config = TaggerConfig()

self.tagger_config.absa_type = bert_config.absa_type.lower()

if bert_config.tfm_mode == 'finetune':

# initialized with pre-trained BERT and perform finetuning

# print("Fine-tuning the pre-trained BERT...")

self.bert = BertModel(bert_config)

else:

raise Exception("Invalid transformer mode %s!!!" % bert_config.tfm_mode)

self.bert_dropout = nn.Dropout(bert_config.hidden_dropout_prob)

# fix the parameters in BERT and rega

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