class_docstr: A user facing class for text classification
class_docstr_tok: ["A","user","facing","class","for","text","classification"]
code: def train(
      self,
      input_filepath: str,
      args: WPTrainArgs = WPTrainArgs(),
      eval_filepath: str = "",
  ):
      super(HappyWordPrediction, self).train(
          input_filepath, args, eval_filepath
      )
  
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 3 for calling the HappyWordPrediction obj's underlying member method code with example usage: obj.train(input_filepath, args, eval_filepath) without return types