class_docstr: CLINC Dataset for Intent Classification and Out-of-Scope (OOS) Detection.
  
  This dataset is for evaluating the performance of intent classification
  systems in the presence of "out-of-scope" queries. By "out-of-scope",
  we mean queries that do not fall into any of the system-supported intent
  classes. Most datasets include only data that is "in-scope".
  Our dataset includes both in-scope and out-of-scope data.
  
  This version of the CLINC OOS dataset contains 150 "in-scope" intents from
  10 domains. Each intent has 100 train, 20 validation, and 30 test samples.
  There are 100 train and validation out-of-scope samples, and 1000 out-of-scope
  test samples. It also contains labels for intent domain which are not included
  in the original dataset.
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docstr: Yields examples.
docstr_tok: ["Yields","examples","."]
code: def _generate_examples(self, filename):
      """Yields examples."""
      with epath.Path(filename).open() as f:
          reader = csv.DictReader(f)
          for row_id, row in enumerate(reader):
              example = {}
              example["text"] = row["text"]
              example["intent"] = row["intent"]
              example["domain"] = row["domain"]
              example["intent_name"] = row["intent_name"]
              example["domain_name"] = row["domain_name"]
  
              yield row_id, example
  
file_path: datasets/tensorflow_datasets/text/clinc_oos.py
imports: from __future__ import annotations
  import csv
  import os
  from etils import epath
  import numpy
  import tensorflow_datasets.public_api
label_desc: Use image node_id 3 for calling the ClincOOS obj's underlying member method code with example usage: obj._generate_examples(filename) without return types