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| import torch | |
| from torch import nn | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| class BERTClassifier(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.bert = AutoModelForSequenceClassification.from_pretrained('cointegrated/rubert-tiny-toxicity') | |
| self.bert.classifier = nn.Linear(312, 312) | |
| for param in self.bert.parameters(): | |
| param.requires_grad = False | |
| self.linear = nn.Sequential( | |
| nn.Linear(312, 128), | |
| nn.Sigmoid(), | |
| nn.Dropout(), | |
| nn.Linear(128, 1) | |
| ) | |
| def forward(self, x, attention_mask=None): | |
| bert_out = self.bert(x, attention_mask=attention_mask).logits | |
| out = self.linear(bert_out).squeeze(1) | |
| return out |