Add in IMDB dataset processor to TF-NLP.

PiperOrigin-RevId: 339134320

official/nlp/data/classifier_data_lib.py

@@ -214,6 +214,44 @@ class ColaProcessor(DataProcessor):
     return examples
 
 
+class ImdbProcessor(DataProcessor):
+  """Processor for the IMDb dataset."""
+
+  def get_labels(self):
+    return ["neg", "pos"]
+
+  def get_train_examples(self, data_dir):
+    return self._create_examples(os.path.join(data_dir, "train"))
+
+  def get_dev_examples(self, data_dir):
+    return self._create_examples(os.path.join(data_dir, "test"))
+
+  @staticmethod
+  def get_processor_name():
+    """See base class."""
+    return "IMDB"
+
+  def _create_examples(self, data_dir):
+    """Creates examples."""
+    examples = []
+    for label in ["neg", "pos"]:
+      cur_dir = os.path.join(data_dir, label)
+      for filename in tf.io.gfile.listdir(cur_dir):
+        if not filename.endswith("txt"):
+          continue
+
+        if len(examples) % 1000 == 0:
+          logging.info("Loading dev example %d", len(examples))
+
+        path = os.path.join(cur_dir, filename)
+        with tf.io.gfile.GFile(path, "r") as f:
+          text = f.read().strip().replace("<br />", " ")
+        examples.append(
+            InputExample(
+                guid="unused_id", text_a=text, text_b=None, label=label))
+    return examples
+
+
 class MnliProcessor(DataProcessor):
   """Processor for the MultiNLI data set (GLUE version)."""
 
@@ -1032,6 +1070,11 @@ def convert_single_example(ex_index, example, label_list, max_seq_length,
     if len(tokens_a) > max_seq_length - 2:
       tokens_a = tokens_a[0:(max_seq_length - 2)]
 
+  seg_id_a = 0
+  seg_id_b = 1
+  seg_id_cls = 0
+  seg_id_pad = 0
+
   # The convention in BERT is:
   # (a) For sequence pairs:
   #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
@@ -1053,19 +1096,19 @@ def convert_single_example(ex_index, example, label_list, max_seq_length,
   tokens = []
   segment_ids = []
   tokens.append("[CLS]")
-  segment_ids.append(0)
+  segment_ids.append(seg_id_cls)
   for token in tokens_a:
     tokens.append(token)
-    segment_ids.append(0)
+    segment_ids.append(seg_id_a)
   tokens.append("[SEP]")
-  segment_ids.append(0)
+  segment_ids.append(seg_id_a)
 
   if tokens_b:
     for token in tokens_b:
       tokens.append(token)
-      segment_ids.append(1)
+      segment_ids.append(seg_id_b)
     tokens.append("[SEP]")
-    segment_ids.append(1)
+    segment_ids.append(seg_id_b)
 
   input_ids = tokenizer.convert_tokens_to_ids(tokens)
 
@@ -1077,7 +1120,7 @@ def convert_single_example(ex_index, example, label_list, max_seq_length,
   while len(input_ids) < max_seq_length:
     input_ids.append(0)
     input_mask.append(0)
-    segment_ids.append(0)
+    segment_ids.append(seg_id_pad)
 
   assert len(input_ids) == max_seq_length
   assert len(input_mask) == max_seq_length

official/nlp/data/create_finetuning_data.py

@@ -47,9 +47,9 @@ flags.DEFINE_string(
     "for the task.")
 
 flags.DEFINE_enum("classification_task_name", "MNLI",
-                  ["AX", "COLA", "MNLI", "MRPC", "PAWS-X", "QNLI", "QQP", "RTE",
-                   "SST-2", "STS-B", "WNLI", "XNLI", "XTREME-XNLI",
-                   "XTREME-PAWS-X"],
+                  ["AX", "COLA", "IMDB", "MNLI", "MRPC", "PAWS-X", "QNLI",
+                   "QQP", "RTE", "SST-2", "STS-B", "WNLI", "XNLI",
+                   "XTREME-XNLI", "XTREME-PAWS-X"],
                   "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 "
@@ -182,6 +182,8 @@ def generate_classifier_dataset():
             classifier_data_lib.AxProcessor,
         "cola":
             classifier_data_lib.ColaProcessor,
+        "imdb":
+            classifier_data_lib.ImdbProcessor,
         "mnli":
             functools.partial(classifier_data_lib.MnliProcessor,
                               mnli_type=FLAGS.mnli_type),