[{'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:def dataframe_to_tensors(df, max_len=512):\n  # offsets are: pron_tok_offset, a_tok_offset, a_tok_right_offset, b_tok_offset, b_tok_right_offset\n  offsets = list()\n  labels = np.zeros((len(df),), dtype=np.int64)\n  processed = list()\n  for i, row in tqdm(df.iterrows()):\n    try:\n      text = row["Text"]\n      a_offset = row["A-offset"]\n      a_len = len(nlp(row["A"]))\n      \n      b_offset = row["B-offset"]\n      b_len = len(nlp(row["B"]))\n\n      pron_offset = row["Pronoun-offset"]\n      is_a = row["A-coref"]\n      is_b = row["B-coref"]\n      a_tok_offset = get_token_num_by_offset(text, a_offset)\n      b_tok_offset = get_token_num_by_offset(text, b_offset)\n      a_right_offset = a_tok_offset + a_len - 1\n      b_right_offset = b_tok_offset + b_len - 1\n      pron_tok_offset = get_token_num_by_offset(text, pron_offset)\n      tokenized = tokenizer.process_text(text, spacy_tok)[:max_len]\n      tokenized = ["xxpad"] * (max_len - len(tokenized)) + tokenized  # add padding\n      a_tok_offset = adjust_token_num(tokenized, a_tok_offset)\n      a_tok_right_offset = adjust_token_num(tokenized, a_right_offset)\n      b_tok_offset = adjust_token_num(tokenized, b_tok_offset)\n      b_tok_right_offset = adjust_token_num(tokenized, b_right_offset)\n      pron_tok_offset = adjust_token_num(tokenized, pron_tok_offset)\n      numericalized = vocab.numericalize(tokenized)\n      processed.append(torch.tensor(numericalized, dtype=torch.long))\n      offsets.append([pron_tok_offset, a_tok_offset, a_tok_right_offset, b_tok_offset, b_tok_right_offset])\n      if is_a:\n        labels[i] = 0\n      elif is_b:\n        labels[i] = 1\n      else:\n        labels[i] = 2\n    except Exception as e:\n      print(i)\n      raise\n  processed = torch.stack(processed)\n  offsets = torch.tensor(offsets, dtype=torch.long)\n  labels = torch.from_numpy(labels)\n  return processed, offsets, labels'}, {'role': 'assistant', 'content': "['data_preprocessing']"}]