Rename inference mode inputs from predict to test

README.md

@@ -308,8 +308,8 @@ that it's running on something other than a Cloud TPU, which includes a GPU.
 
 #### Prediction from classifier
 Once you have trained your classifier you can use it in inference mode by using the --do_predict=true command.
-You need to have a file named predict.tsv in the input folder. 
-Output will be created in file called predict_results.tsv in the output folder.
+You need to have a file named test.tsv in the input folder. 
+Output will be created in file called test_results.tsv in the output folder.
 Each line will contain output for each sample, columns are the class probabilities.
 ```shell
 export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12

run_classifier.py

@@ -71,7 +71,7 @@ flags.DEFINE_bool("do_train", False, "Whether to run training.")
 
 flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")
 
-flags.DEFINE_bool("do_predict", False, "Whether to run the model in inference mode on predict set.")
+flags.DEFINE_bool("do_predict", False, "Whether to run the model in inference mode on the test set.")
 
 flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
 
@@ -164,7 +164,7 @@ class DataProcessor(object):
     """Gets a collection of `InputExample`s for the dev set."""
     raise NotImplementedError()
 
-  def get_predict_examples(self, data_dir):
+  def get_test_examples(self, data_dir):
     """Gets a collection of `InputExample`s for prediction."""
     raise NotImplementedError()
 
@@ -245,11 +245,11 @@ class MnliProcessor(DataProcessor):
         self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")),
         "dev_matched")
 
-  def get_predict_examples(self, data_dir):
+  def get_test_examples(self, data_dir):
     """See base class."""
     return self._create_examples(
-      self._read_tsv(os.path.join(data_dir, "predict.tsv")),
-      "predict")
+      self._read_tsv(os.path.join(data_dir, "test_matched.tsv")),
+      "test")
 
   def get_labels(self):
     """See base class."""
@@ -283,10 +283,10 @@ class MrpcProcessor(DataProcessor):
     return self._create_examples(
         self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
 
-  def get_predict_examples(self, data_dir):
+  def get_test_examples(self, data_dir):
     """See base class."""
     return self._create_examples(
-      self._read_tsv(os.path.join(data_dir, "predict.tsv")), "predict")
+      self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
 
   def get_labels(self):
     """See base class."""
@@ -320,10 +320,10 @@ class ColaProcessor(DataProcessor):
     return self._create_examples(
         self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
 
-  def get_predict_examples(self, data_dir):
+  def get_test_examples(self, data_dir):
     """See base class."""
     return self._create_examples(
-      self._read_tsv(os.path.join(data_dir, "predict.tsv")), "predict")
+      self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
 
   def get_labels(self):
     """See base class."""
@@ -772,7 +772,7 @@ def main(_):
         tf.logging.info("  %s = %s", key, str(result[key]))
         writer.write("%s = %s\n" % (key, str(result[key])))
   if FLAGS.do_predict:
-    predict_examples = processor.get_predict_examples(FLAGS.data_dir)
+    predict_examples = processor.get_test_examples(FLAGS.data_dir)
     predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
     convert_examples_to_features(predict_examples, label_list,
                                  FLAGS.max_seq_length, tokenizer, predict_file)
@@ -795,7 +795,7 @@ def main(_):
 
     result = estimator.predict(input_fn=predict_input_fn)
 
-    output_predict_file = os.path.join(FLAGS.output_dir, "predict_results.tsv")
+    output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv")
     with tf.gfile.GFile(output_predict_file, "w") as writer:
       tf.logging.info("***** Predict results *****")
       for prediction in result: