def fit_model(train_pool, test_pool, **kwargs):
    model = CatBoostClassifier(
        random_seed=0,
        max_ctr_complexity=1,
        task_type='GPU',
        iterations=10000,
        eval_metric='AUC',
        od_type='Iter',
        od_wait=500,
        learning_rate=0.03,
        **kwargs
    )

    return model.fit(
        train_pool,
        eval_set=test_pool,
        verbose=1000,
        plot=False,
        use_best_model=True
    )

model_embeddings = fit_model(
    train_pool, test_pool,
    dictionaries = [{
        'dictionaryId': 'Unigram',
        'max_dictionary_size': '50000',
        'gram_count': '1',
    },{
        'dictionaryId': 'Bigram',
        'max_dictionary_size': '50000',
        'gram_count': '2',
    }]    
)

CatBoostError: catboost/private/libs/options/text_processing_options.cpp:75: DictionaryOptions: no dictionary_id was specified

model_embeddings = fit_model(
    train_pool, test_pool,
    dictionaries = [{
        'dictionary_id': 'Unigram',
        'max_dictionary_size': '50000',
        'gram_count': '1',
    },{
        'dictionary_id': 'Bigram',
        'max_dictionary_size': '50000',
        'gram_count': '2',
    }]    
)

tokenizers=[{'tokenizer_id': 'Space'}],
    dictionaries = [{
        'dictionary_id': 'Unigram',
        'max_dictionary_size': '50000',
        'gram_count': '1',
    },{
        'dictionary_id': 'Bigram',
        'max_dictionary_size': '50000',
        'gram_count': '2',
    }],
    feature_calcers=['BoW']