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import torch
from transformers import BertModel
class Ensembler(torch.nn.Module):
def __init__(self, specialists):
super().__init__()
self.specialists = specialists
def forward(self, input_ids, attention_mask):
outputs = torch.cat([specialist(input_ids, attention_mask)
for specialist in self.specialists], dim=1)
return torch.mean(outputs, dim=1).unsqueeze(1)
class LanguageIdentifier(torch.nn.Module):
def __init__(self):
super().__init__()
self.portuguese_bert = BertModel.from_pretrained("neuralmind/bert-large-portuguese-cased")
self.linear_layer = torch.nn.Sequential(
torch.nn.Dropout(p=0.2),
torch.nn.Linear(self.portuguese_bert.config.hidden_size, 1),
)
def forward(self, input_ids, attention_mask):
#(Batch_Size,Sequence Length, Hidden_Size)
outputs = self.portuguese_bert(input_ids=input_ids, attention_mask=attention_mask).last_hidden_state[:, 0, :]
outputs = self.linear_layer(outputs)
return outputs |