[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:epoch_len = 20\nmodel, criteria, optimizer, scheduler = create_model(len(df_train), epoch_len)\nset_trainable(model.BERT, False)\naaa = 0\nfor t in range(epoch_len):\n    tot_loss = 0\n    correct_train = 0\n    val_loss = 0\n    val_correct = 0\n    model = model.train()\n    \n    if GPU:\n        model = model.cuda()\n    \n    for item in tqdm(train_loader):\n        \n        token = item[0]\n        at_mask = item[3]\n        offsets = item[2]\n        target = item[1]\n        if GPU:\n            token = token.cuda()\n            at_mask = at_mask.cuda()\n            target = target.cuda()\n            offsets = offsets.cuda()\n            \n        output = model(token, at_mask, offsets, -2)\n        loss = criteria(output, target)\n        tot_loss += loss.item()\n        correct_train += torch.sum(torch.max(torch.nn.functional.softmax(output, dim = 1), dim = 1)[1] == target)\n        \n        optimizer.zero_grad()\n        loss.backward()\n        nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n        optimizer.step()\n        scheduler.step()\n    \n    with torch.no_grad():\n        model = model.eval()\n        \n        if GPU:\n            model = model.cuda()            \n        for item in tqdm(val_loader):\n            token = item[0]\n            at_mask = item[3]\n            offsets = item[2]\n            target = item[1]\n            \n            if GPU:\n                token = token.cuda()\n                at_mask = at_mask.cuda()\n                offsets = offsets.cuda()\n                target = target.cuda()\n                \n            output = model(token, at_mask, offsets, -2)\n            val_correct += torch.sum(torch.max(torch.nn.functional.softmax(output, dim = 1), dim = 1)[1] == target)\n        if val_correct > aaa:\n            bst_model = model\n            aaa = val_correct\n    print(tot_loss, correct_train,"   ", val_correct," out of ", len(val_loader)*30)'}, {'role': 'assistant', 'content': "['modelling']"}]