def METHOD_NAME(hf_tokenizer, smiles_regression_dataset):
    y = np.random.choice([0, 1], size=smiles_regression_dataset.y.shape)
    dataset = dc.data.NumpyDataset(X=smiles_regression_dataset.X,
                                   y=y,
                                   w=smiles_regression_dataset.w,
                                   ids=smiles_regression_dataset.ids)
    from transformers import RobertaConfig, RobertaForSequenceClassification
    config = RobertaConfig(vocab_size=hf_tokenizer.vocab_size)
    model = RobertaForSequenceClassification(config)
    hf_model = HuggingFaceModel(model=model,
                                task='classification',
                                tokenizer=hf_tokenizer,
                                device=torch.device('cpu'))
    hf_model.fit(dataset, nb_epoch=1)
    result = hf_model.predict(dataset)
    assert result.all()
    score = hf_model.evaluate(dataset,
                              metrics=dc.metrics.Metric(dc.metrics.f1_score))
    assert score