diff --git a/tools/ml/trainer2/train_ml_helpers.py b/tools/ml/trainer2/train_ml_helpers.py
new file mode 100644
index 0000000..36113a2
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+++ b/tools/ml/trainer2/train_ml_helpers.py
@@ -0,0 +1,158 @@
+# Copyright 2019 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
+
+"""
+Helper functions for spam and component classification. These are mostly for
+feature extraction, so that the serving code and training code both use the same
+set of features.
+TODO(jeffcarp): This file is duplicate of services/ml_helpers.py
+  (with slight difference). Will eventually be merged to one.
+"""
+
+from __future__ import absolute_import
+
+import csv
+import hashlib
+import re
+import sys
+
+SPAM_COLUMNS = ['verdict', 'subject', 'content', 'email']
+LEGACY_CSV_COLUMNS = ['verdict', 'subject', 'content']
+DELIMITERS = [r'\s', r'\,', r'\.', r'\?', r'!', r'\:', r'\(', r'\)']
+
+# Must be identical to settings.spam_feature_hashes.
+SPAM_FEATURE_HASHES = 500
+# Must be identical to settings.component_features.
+COMPONENT_FEATURES = 5000
+
+
+def _ComponentFeatures(content, num_features, top_words):
+  """
+    This uses the most common words in the entire dataset as features.
+    The count of common words in the issue comments makes up the features.
+  """
+
+  features = [0] * num_features
+  for blob in content:
+    words = blob.split()
+    for word in words:
+      if word in top_words:
+        features[top_words[word]] += 1
+
+  return features
+
+
+def _SpamHashFeatures(content, num_features):
+  """
+    Feature hashing is a fast and compact way to turn a string of text into a
+    vector of feature values for classification and training.
+    See also: https://en.wikipedia.org/wiki/Feature_hashing
+    This is a simple implementation that doesn't try to minimize collisions
+    or anything else fancy.
+  """
+  features = [0] * num_features
+  total = 0.0
+  for blob in content:
+    words = re.split('|'.join(DELIMITERS).encode('utf-8'), blob)
+    for word in words:
+      feature_index = int(int(hashlib.sha1(word).hexdigest(), 16)
+                          % num_features)
+      features[feature_index] += 1.0
+      total += 1.0
+
+  if total > 0:
+    features = [f / total for f in features]
+
+  return features
+
+
+def GenerateFeaturesRaw(content, num_features, top_words=None):
+  """Generates a vector of features for a given issue or comment.
+
+  Args:
+    content: The content of the issue's description and comments.
+    num_features: The number of features to generate.
+  """
+  # If we've been passed real unicode strings, convert them to just bytestrings.
+  for idx, value in enumerate(content):
+    content[idx] = value.encode('utf-8')
+  if top_words:
+    return {'word_features': _ComponentFeatures(content,
+                                                   num_features,
+                                                   top_words)}
+
+  return {'word_hashes': _SpamHashFeatures(content, num_features)}
+
+
+def transform_spam_csv_to_features(contents, labels):
+  """Generate arrays of features and targets for spam.
+  """
+  features = []
+  targets = []
+  for i, row in enumerate(contents):
+    subject, content = row
+    label = labels[i]
+    features.append(GenerateFeaturesRaw([str(subject), str(content)],
+                                 SPAM_FEATURE_HASHES))
+    targets.append(1 if label == 'spam' else 0)
+  return features, targets
+
+
+def transform_component_csv_to_features(contents, labels, top_list):
+  """Generate arrays of features and targets for components.
+  """
+  features = []
+  targets = []
+  top_words = {}
+
+  for i, row in enumerate(top_list):
+    top_words[row] = i
+
+  component_to_index = {}
+  index_to_component = {}
+  component_index = 0
+
+  for i, content in enumerate(contents):
+    component = labels[i]
+    component = str(component).split(",")[0]
+
+    if component not in component_to_index:
+      component_to_index[component] = component_index
+      index_to_component[component_index] = component
+      component_index += 1
+
+    features.append(GenerateFeaturesRaw([content],
+                                 COMPONENT_FEATURES,
+                                 top_words))
+    targets.append(component_to_index[component])
+
+  return features, targets, index_to_component
+
+
+def spam_from_file(f):
+  """Reads a training data file and returns arrays of contents and labels."""
+  contents = []
+  labels = []
+  skipped_rows = 0
+  for row in csv.reader(f):
+    if len(row) >= len(LEGACY_CSV_COLUMNS):
+      # Throw out email field.
+      contents.append(row[1:3])
+      labels.append(row[0])
+    else:
+      skipped_rows += 1
+  return contents, labels, skipped_rows
+
+
+def component_from_file(f):
+  """Reads a training data file and returns arrays of contents and labels."""
+  contents = []
+  labels = []
+  csv.field_size_limit(sys.maxsize)
+  for row in csv.reader(f):
+    label, content = row
+    contents.append(content)
+    labels.append(label)
+  return contents, labels