class_docstr: MultiNLI: The Stanford Question Answering Dataset. Version 1.1.
docstr: Generate mnli examples.
  
  Args:
    filepath: a string
  
  Yields:
    dictionaries containing "premise", "hypothesis" and "label" strings
docstr_tok: ["Generate","mnli","examples",".","Args",":","filepath",":","a","string","Yields",":","dictionaries","containing","``","premise","''",",","``","hypothesis","''","and","``","label","''","strings"]
code: def _generate_examples(self, filepath):
      """Generate mnli examples.
  
      Args:
        filepath: a string
  
      Yields:
        dictionaries containing "premise", "hypothesis" and "label" strings
      """
      for idx, line in enumerate(tf.io.gfile.GFile(filepath, "rb")):
          if idx == 0:
              continue  # skip header
          line = tf.compat.as_text(line.strip())
          split_line = line.split("\t")
          # Examples not marked with a three out of five consensus are marked with
          # "-" and should not be used in standard evaluations.
          if split_line[0] == "-":
              continue
          # Works for both splits even though dev has some extra human labels.
          yield idx, {
              "premise": split_line[5],
              "hypothesis": split_line[6],
              "label": split_line[0],
          }
  
file_path: datasets/tensorflow_datasets/text/multi_nli.py
imports: import os
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
label_desc: Use image node_id 3 for calling the MultiNLI obj's underlying member method code with example usage: obj._generate_examples(filepath) without return types