code: def test_hf_model_classification(
      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
  
file_path: deepchem/deepchem/models/torch_models/tests/test_hf_models.py
imports: import os
  import deepchem
  import numpy
  import pytest
label_desc: Use image node_id 4 for calling a global function with example usage: test_hf_model_classification(hf_tokenizer, smiles_regression_dataset) without return types