code: def test_hf_model_regression(hf_tokenizer, smiles_regression_dataset):
      from transformers.models.roberta import (
          RobertaConfig,
          RobertaForSequenceClassification,
      )
  
      config = RobertaConfig(
          vocab_size=hf_tokenizer.vocab_size,
          problem_type="regression",
          num_labels=1,
      )
      model = RobertaForSequenceClassification(config)
      hf_model = HuggingFaceModel(
          model=model,
          tokenizer=hf_tokenizer,
          task="regression",
          device=torch.device("cpu"),
      )
      hf_model.fit(smiles_regression_dataset, nb_epoch=1)
      result = hf_model.predict(smiles_regression_dataset)
  
      assert result.all()
      score = hf_model.evaluate(
          smiles_regression_dataset,
          metrics=dc.metrics.Metric(dc.metrics.mae_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 3 for calling a global function with example usage: test_hf_model_regression(hf_tokenizer, smiles_regression_dataset) without return types