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import torch
from transformers import BertModel, RobertaModel
from torch import nn
from sklearn.metrics import accuracy_score, classification_report
import numpy as np
class BertClassifier(nn.Module):
def __init__(self, model_name, dropout_rate=0.1):
super(BertClassifier, self).__init__()
self.bert = BertModel.from_pretrained(model_name)
self.dropout = nn.Dropout(dropout_rate)
self.fc = nn.Linear(self.bert.config.hidden_size, 1)
def forward(self, input_ids, attention_mask):
output = self.bert(input_ids, attention_mask)
output = output.pooler_output
output = self.dropout(output)
output = self.fc(output)
output = torch.sigmoid(output)
return output
class RobertaClassifier(nn.Module):
def __init__(self, model_name, dropout_rate=0.1):
super(RobertaClassifier, self).__init__()
self.roberta = RobertaModel.from_pretrained(model_name)
self.dropout = nn.Dropout(dropout_rate)
self.fc = nn.Linear(self.roberta.config.hidden_size, 1)
def forward(self, input_ids, attention_mask):
output = self.roberta(input_ids, attention_mask)
output = output.pooler_output
output = self.dropout(output)
output = self.fc(output)
output = torch.sigmoid(output)
return output
def train(model, data_loader, optimizer, scheduler, device):
model.to(device)
model.train()
loss_func = nn.BCELoss()
for batch in data_loader:
# print(batch)
optimizer.zero_grad()
# prepare inputs
input_ids = batch['input_ids'].to(device)
attention_mask = batch['attention_mask'].to(device)
y_true = batch['label'].reshape(-1, 1).to(device)
# Compute output
output = model(input_ids, attention_mask)
# Calculate Loss
loss = loss_func(output, y_true)
# Backward propagation
loss.backward()
optimizer.step()
scheduler.step()
def evaluate(model, data_loader, device):
model.eval()
predictions = []
val_labels = []
torch.cuda.empty_cache()
for batch in data_loader:
input_ids = batch['input_ids'].to(device)
attention_mask = batch['attention_mask'].to(device)
y_true = batch['label'].tolist()
output = model(input_ids, attention_mask)
y_pred = np.int64(output.cpu().detach().numpy() > 0.5).reshape(-1).tolist()
predictions.extend(y_pred)
val_labels.extend(y_true)
return accuracy_score(val_labels, predictions), classification_report(val_labels, predictions)
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