diff --git a/examples/benchmarks/TFT/data_formatters/traffic.py b/examples/benchmarks/TFT/data_formatters/traffic.py
deleted file mode 100644
index ee8ef2e5d..000000000
--- a/examples/benchmarks/TFT/data_formatters/traffic.py
+++ /dev/null
@@ -1,117 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The Google Research Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Lint as: python3
-"""Custom formatting functions for Traffic dataset.
-
-Defines dataset specific column definitions and data transformations. This also
-performs z-score normalization across the entire dataset, hence re-uses most of
-the same functions as volatility.
-"""
-
-import data_formatters.base
-import data_formatters.volatility
-
-VolatilityFormatter = data_formatters.volatility.VolatilityFormatter
-DataTypes = data_formatters.base.DataTypes
-InputTypes = data_formatters.base.InputTypes
-
-
-class TrafficFormatter(VolatilityFormatter):
-    """Defines and formats data for the traffic dataset.
-
-    This also performs z-score normalization across the entire dataset, hence
-    re-uses most of the same functions as volatility.
-
-    Attributes:
-      column_definition: Defines input and data type of column used in the
-        experiment.
-      identifiers: Entity identifiers used in experiments.
-    """
-
-    _column_definition = [
-        ("id", DataTypes.REAL_VALUED, InputTypes.ID),
-        ("hours_from_start", DataTypes.REAL_VALUED, InputTypes.TIME),
-        ("values", DataTypes.REAL_VALUED, InputTypes.TARGET),
-        ("time_on_day", DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
-        ("day_of_week", DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
-        ("hours_from_start", DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT),
-        ("categorical_id", DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT),
-    ]
-
-    def split_data(self, df, valid_boundary=151, test_boundary=166):
-        """Splits data frame into training-validation-test data frames.
-
-        This also calibrates scaling object, and transforms data for each split.
-
-        Args:
-          df: Source data frame to split.
-          valid_boundary: Starting year for validation data
-          test_boundary: Starting year for test data
-
-        Returns:
-          Tuple of transformed (train, valid, test) data.
-        """
-
-        print("Formatting train-valid-test splits.")
-
-        index = df["sensor_day"]
-        train = df.loc[index < valid_boundary]
-        valid = df.loc[(index >= valid_boundary - 7) & (index < test_boundary)]
-        test = df.loc[index >= test_boundary - 7]
-
-        self.set_scalers(train)
-
-        return (self.transform_inputs(data) for data in [train, valid, test])
-
-    # Default params
-    def get_fixed_params(self):
-        """Returns fixed model parameters for experiments."""
-
-        fixed_params = {
-            "total_time_steps": 8 * 24,
-            "num_encoder_steps": 7 * 24,
-            "num_epochs": 100,
-            "early_stopping_patience": 5,
-            "multiprocessing_workers": 5,
-        }
-
-        return fixed_params
-
-    def get_default_model_params(self):
-        """Returns default optimised model parameters."""
-
-        model_params = {
-            "dropout_rate": 0.3,
-            "hidden_layer_size": 320,
-            "learning_rate": 0.001,
-            "minibatch_size": 128,
-            "max_gradient_norm": 100.0,
-            "num_heads": 4,
-            "stack_size": 1,
-        }
-
-        return model_params
-
-    def get_num_samples_for_calibration(self):
-        """Gets the default number of training and validation samples.
-
-        Use to sub-sample the data for network calibration and a value of -1 uses
-        all available samples.
-
-        Returns:
-          Tuple of (training samples, validation samples)
-        """
-        return 450000, 50000