diff --git a/tools/ml/trainer/top_words.py b/tools/ml/trainer/top_words.py
new file mode 100644
index 0000000..26da211
--- /dev/null
+++ b/tools/ml/trainer/top_words.py
@@ -0,0 +1,127 @@
+# Copyright 2018 The Chromium Authors. All rights reserved.
+# Use of this source code is governed by a BSD-style
+# license that can be found in the LICENSE file or at
+# https://developers.google.com/open-source/licenses/bsd
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import csv
+import os
+import re
+import StringIO
+import sys
+import tensorflow as tf
+import time
+
+from googleapiclient import discovery
+from googleapiclient import errors
+from oauth2client.client import GoogleCredentials
+import google
+from google.cloud.storage import blob, bucket, client
+
+import trainer.ml_helpers
+import trainer.dataset
+
+
+TOP_WORDS = 'topwords.txt'
+STOP_WORDS = 'stopwords.txt'
+
+
+def fetch_stop_words(project_id, objects):
+  request = objects.get_media(bucket=project_id + '-mlengine',
+                              object=STOP_WORDS)
+  response = trainer.dataset.make_api_request(request)
+  return response.split()
+
+
+def fetch_training_csv(filepath, objects, b):
+  request = objects.get_media(bucket=b, object=filepath)
+  return trainer.dataset.make_api_request(request)
+
+
+def GenerateTopWords(objects, word_dict, project_id):
+  stop_words = fetch_stop_words(project_id, objects)
+  sorted_words = sorted(word_dict, key=word_dict.get, reverse=True)
+
+  top_words = []
+  index = 0
+
+  while len(top_words) < trainer.ml_helpers.COMPONENT_FEATURES:
+    if sorted_words[index] not in stop_words:
+      top_words.append(sorted_words[index])
+    index += 1
+
+  return top_words
+
+
+def make_top_words_list(job_dir):
+  """Returns the top (most common) words in the entire dataset for component
+  prediction. If a file is already stored in GCS containing these words, the
+  words from the file are simply returned. Otherwise, the most common words are
+  determined and written to GCS, before being returned.
+
+  Returns:
+    A list of the most common words in the dataset (the number of them
+    determined by ml_helpers.COMPONENT_FEATURES).
+  """
+
+  credentials = GoogleCredentials.get_application_default()
+  storage = discovery.build('storage', 'v1', credentials=credentials)
+  objects = storage.objects()
+
+  subpaths = re.match('gs://(monorail-.*)-mlengine/(component_trainer_\d+)',
+                      job_dir)
+
+  if subpaths:
+    project_id = subpaths.group(1)
+    trainer_folder = subpaths.group(2)
+  else:
+    project_id = 'monorail-prod'
+
+  storage_bucket = project_id + '.appspot.com'
+  request = objects.list(bucket=storage_bucket,
+                         prefix='component_training_data')
+
+  response = trainer.dataset.make_api_request(request)
+
+  items = response.get('items')
+  csv_filepaths = [b.get('name') for b in items]
+
+  final_string = ''
+
+  for word in parse_words(csv_filepaths, objects, storage_bucket, project_id):
+    final_string += word + '\n'
+
+  if subpaths:
+    client_obj = client.Client(project=project_id)
+    bucket_obj = bucket.Bucket(client_obj, project_id + '-mlengine')
+
+    bucket_obj.blob = google.cloud.storage.blob.Blob(trainer_folder
+                                                   + '/'
+                                                   + TOP_WORDS,
+                                                   bucket_obj)
+    bucket_obj.blob.upload_from_string(final_string,
+                                       content_type='text/plain')
+  return final_string.split()
+
+
+def parse_words(files, objects, b, project_id):
+  word_dict = {}
+
+  csv.field_size_limit(sys.maxsize)
+  for filepath in files:
+    media = fetch_training_csv(filepath, objects, b)
+
+    for row in csv.reader(StringIO.StringIO(media)):
+      _, content = row
+      words = content.split()
+
+      for word in words:
+        if word in word_dict:
+          word_dict[word] += 1
+        else:
+          word_dict[word] = 1
+
+  return GenerateTopWords(objects, word_dict, project_id)