from datasets import load_dataset from torch.utils.data import DataLoader from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch from tqdm import tqdm def train_one_epoch(model, dataloader, optimizer): model.train() loss_list = [] for batch in tqdm(dataloader): batch_data = { ‘input_ids’: batch[‘input_ids’], ‘attention_mask’: batch[‘attention_mask’], ‘labels’: batch[‘labels’] } loss = model(**batch_data).loss loss.backward() optimizer.step() optimizer.zero_grad() loss_list.append(loss.detach().item()) avg_loss = sum(loss_list) / len(loss_list) print(‘avg loss […]