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from transformers import AutoModel | |
from torch import nn | |
class BERTClassifier(nn.Module): | |
def __init__(self, bert_path="cointegrated/rubert-tiny2"): | |
super().__init__() | |
self.bert = AutoModel.from_pretrained(bert_path) | |
for param in self.bert.parameters(): | |
param.requires_grad = False | |
self.linear = nn.Sequential( | |
nn.Linear(312, 150), | |
nn.Dropout(0.1), | |
nn.ReLU(), | |
nn.Linear(150, 1), | |
nn.Sigmoid() | |
) | |
def forward(self, x, masks): | |
bert_out = self.bert(x, attention_mask=masks)[0][:, 0, :] | |
out = self.linear(bert_out) | |
return out |