added AX-g preprocessor

official/nlp/data/classifier_data_lib.py

@@ -18,6 +18,7 @@ import collections
 import csv
 import importlib
 import os
+import json
 
 from absl import logging
 import tensorflow as tf
@@ -1275,6 +1276,46 @@ def convert_single_example(ex_index, example, label_list, max_seq_length,
 
   return feature
 
+class AXgProcessor(DataProcessor):
+  """Processor for the AX dataset (GLUE diagnostics dataset)."""
+
+  def get_dev_examples(self, data_dir):
+    """See base class."""
+    return self._create_examples(
+        self._read_jsonl(os.path.join(data_dir, "dev.jsonl")), "dev")
+
+  def get_test_examples(self, data_dir):
+    """See base class."""
+    return self._create_examples(
+        self._read_jsonl(os.path.join(data_dir, "test.jsonl")), "test")
+
+  def get_labels(self):
+    """See base class."""
+    return ["entailment", "not_entailment"]
+
+  @staticmethod
+  def get_processor_name():
+    """See base class."""
+    return "AXg"
+
+  def _create_examples(self, lines, set_type):
+    """Creates examples for the training/dev/test sets."""
+    examples = []
+    for line in lines:
+      guid = "%s-%s" % (set_type, self.process_text_fn(str(line['idx'])))
+      text_a = self.process_text_fn(line["hypothesis"])
+      text_b = self.process_text_fn(line["premise"])
+      label = self.process_text_fn(line["label"])
+      examples.append(
+          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
+    return examples
+  def _read_jsonl(self, input_path):
+    with tf.io.gfile.GFile(input_path, "r") as f:
+      lines = []
+      for json_str in f:
+        lines.append(json.loads(json_str))
+    return lines
+
 
 def file_based_convert_examples_to_features(examples,
                                             label_list,
@@ -1374,8 +1415,9 @@ def generate_tf_record_from_data_file(processor,
   label_type = getattr(processor, "label_type", None)
   is_regression = getattr(processor, "is_regression", False)
   has_sample_weights = getattr(processor, "weight_key", False)
-  assert train_data_output_path
 
+  num_training_data = 0
+  if train_data_output_path:
     train_input_data_examples = processor.get_train_examples(data_dir)
     file_based_convert_examples_to_features(train_input_data_examples, label_list,
                                             max_seq_length, tokenizer,

official/nlp/data/create_finetuning_data.py

@@ -49,7 +49,7 @@ flags.DEFINE_string(
 flags.DEFINE_enum(
     "classification_task_name", "MNLI", [
         "AX", "COLA", "IMDB", "MNLI", "MRPC", "PAWS-X", "QNLI", "QQP", "RTE",
-        "SST-2", "STS-B", "WNLI", "XNLI", "XTREME-XNLI", "XTREME-PAWS-X"
+        "SST-2", "STS-B", "WNLI", "XNLI", "XTREME-XNLI", "XTREME-PAWS-X", "AX-g"
     ], "The name of the task to train BERT classifier. The "
     "difference between XTREME-XNLI and XNLI is: 1. the format "
     "of input tsv files; 2. the dev set for XTREME is english "
@@ -238,7 +238,10 @@ def generate_classifier_dataset():
             functools.partial(
                 classifier_data_lib.XtremePawsxProcessor,
                 translated_data_dir=FLAGS.translated_input_data_dir,
-                only_use_en_dev=FLAGS.only_use_en_dev)
+                only_use_en_dev=FLAGS.only_use_en_dev),
+        "ax-g":
+          classifier_data_lib.AXgProcessor
+
     }
     task_name = FLAGS.classification_task_name.lower()
     if task_name not in processors: