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import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
class SentimentAnalysisModel(torch.nn.Module):
def __init__(self, bert_model_name, num_labels=7, dropout_rate=0.4):
super(SentimentAnalysisModel, self).__init__()
self.model = AutoModelForSequenceClassification.from_pretrained(bert_model_name)
self.model.classifier.out_proj = torch.nn.Linear(self.model.config.hidden_size, num_labels)
self.model.classifier.dropout = torch.nn.Dropout(p=dropout_rate, inplace=False)
# ปรับ dropout ในทุก RobertaLayer
for layer in self.model.roberta.encoder.layer:
layer.attention.self.dropout = torch.nn.Dropout(p=dropout_rate)
layer.attention.output.dropout = torch.nn.Dropout(p=dropout_rate)
layer.intermediate.dropout = torch.nn.Dropout(p=dropout_rate)
layer.output.dropout = torch.nn.Dropout(p=dropout_rate)
self.tokenizer = AutoTokenizer.from_pretrained(bert_model_name)
def forward(self, input_ids, attention_mask, labels=None):
outputs = self.model(input_ids, attention_mask=attention_mask, labels=labels)
return outputs
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