class_docstr: A user facing class for text classification
class_docstr_tok: ["A","user","facing","class","for","text","classification"]
code: def eval(
      self, input_filepath, args: WPEvalArgs = WPEvalArgs()
  ) -> EvalResult:
      return super(HappyWordPrediction, self).eval(input_filepath, args)
  
file_path: happy-transformer/happytransformer/happy_word_prediction.py
imports: from dataclasses import dataclass
  from datasets import Dataset
  from typing import List, Optional, Union
  from transformers import AutoModelForMaskedLM, DataCollatorForLanguageModeling, FillMaskPipeline
  from happytransformer.adaptors import get_adaptor
  from happytransformer.args import WPEvalArgs, WPTrainArgs
  from happytransformer.fine_tuning_util import EvalResult, tok_text_gen_mlm, csv_tok_text_gen_mlm
  from happytransformer.happy_transformer import HappyTransformer
label_desc: Use image node_id 4 for calling the HappyWordPrediction obj's underlying member method code with example usage: obj.eval(input_filepath, args) and returns: super