def run(df, fold, use_vaccine):
    train_df = df[(df.kfold != fold)].reset_index(drop=True)
    print(f'Länge Traing_DF  {len(train_df)}')
    valid_df = df[(df.kfold == fold)].reset_index(drop=True)
    print(f'Länge Valid_DF  {len(valid_df)}')
    if (not use_vaccine):
        train_df = train_df[(train_df['label'] != 2)]
    train_df['label'] = train_df['label'].apply((lambda x: (1 if (x > 0) else 0)))
    valid_df['label'] = valid_df['label'].apply((lambda x: (1 if (x > 0) else 0)))
    print('Fitting tokenizer')
    tokenizer = create_tokenizer(train_df, f'{config.TOKENIZER_SAVE_PATH}_{fold}.pickle')
    train_model(train_df, valid_df, tokenizer, fold)
def run(df, fold, use_vaccine):
    train_df = df[(df.kfold != fold)].reset_index(drop=True)
    print(f'Länge Traing_DF  {len(train_df)}')
    valid_df = df[(df.kfold == fold)].reset_index(drop=True)
    print(f'Länge Valid_DF  {len(valid_df)}')
    if (not use_vaccine):
        train_df = train_df[(train_df['label'] != 2)]
    train_df['label'] = train_df['label'].apply((lambda x: (1 if (x > 0) else 0)))
    valid_df['label'] = valid_df['label'].apply((lambda x: (1 if (x > 0) else 0)))
    print('Fitting tokenizer')
    tokenizer = create_tokenizer(train_df, f'{config.TOKENIZER_SAVE_PATH}_{fold}.pickle')
    train_model(train_df, valid_df, tokenizer, fold)