diff --git a/examples/vectors_tensorboard_standalone.py b/examples/vectors_tensorboard_standalone.py
new file mode 100644
index 000000000..7a9abf785
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+++ b/examples/vectors_tensorboard_standalone.py
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+#!/usr/bin/env python
+# coding: utf8
+"""Export spaCy model vectors for use in TensorBoard's standalone embedding projector.
+https://github.com/tensorflow/embedding-projector-standalone
+
+Usage:
+
+ python vectors_tensorboard_standalone.py ./myVectorModel ./output [name]
+
+This outputs two files that have to be copied into the "oss_data" of the standalone projector:
+
+ [name]_labels.tsv - metadata such as human readable labels for vectors
+ [name]_tensors.bytes - numpy.ndarray of numpy.float32 precision vectors
+
+"""
+from __future__ import unicode_literals
+
+import json
+import math
+from os import path
+
+import numpy
+import plac
+import spacy
+import tqdm
+
+
+@plac.annotations(
+    vectors_loc=("Path to spaCy model that contains vectors", "positional", None, str),
+    out_loc=("Path to output folder writing tensors and labels data", "positional", None, str),
+    name=("Human readable name for tsv file and vectors tensor", "positional", None, str),
+)
+def main(vectors_loc, out_loc, name="spaCy_vectors"):
+    # A tab-separated file that contains information about the vectors for visualization
+    #
+    # Learn more: https://www.tensorflow.org/programmers_guide/embedding#metadata
+    meta_file = "{}_labels.tsv".format(name)
+    out_meta_file = path.join(out_loc, meta_file)
+
+    print('Loading spaCy vectors model: {}'.format(vectors_loc))
+    model = spacy.load(vectors_loc)
+
+    print('Finding lexemes with vectors attached: {}'.format(vectors_loc))
+    voacb_strings = [
+        w for w in tqdm.tqdm(model.vocab.strings, total=len(model.vocab.strings), leave=False)
+        if model.vocab.has_vector(w)
+    ]
+    vector_count = len(voacb_strings)
+
+    print('Building Projector labels for {} vectors: {}'.format(vector_count, out_meta_file))
+    vector_dimensions = model.vocab.vectors.shape[1]
+    tf_vectors_variable = numpy.zeros((vector_count, vector_dimensions), dtype=numpy.float32)
+
+    # Write a tab-separated file that contains information about the vectors for visualization
+    #
+    # Reference: https://www.tensorflow.org/programmers_guide/embedding#metadata
+    with open(out_meta_file, 'wb') as file_metadata:
+        # Define columns in the first row
+        file_metadata.write("Text\tFrequency\n".encode('utf-8'))
+        # Write out a row for each vector that we add to the tensorflow variable we created
+        vec_index = 0
+
+        for text in tqdm.tqdm(voacb_strings, total=len(voacb_strings), leave=False):
+            # https://github.com/tensorflow/tensorflow/issues/9094
+            text = '<Space>' if text.lstrip() == '' else text
+            lex = model.vocab[text]
+
+            # Store vector data and metadata
+            tf_vectors_variable[vec_index] = numpy.float64(model.vocab.get_vector(text))
+            file_metadata.write("{}\t{}\n".format(text, math.exp(lex.prob) * len(voacb_strings)).encode('utf-8'))
+            vec_index += 1
+
+    # Write out "[name]_tensors.bytes" file for standalone embeddings projector to load
+    tensor_path = '{}_tensors.bytes'.format(name)
+    tf_vectors_variable.tofile(path.join(out_loc, tensor_path))
+
+    print('Done.')
+    print('Add the following entry to "oss_data/oss_demo_projector_config.json"')
+    print(json.dumps({
+        "tensorName": name,
+        "tensorShape": [vector_count, vector_dimensions],
+        "tensorPath": 'oss_data/{}'.format(tensor_path),
+        "metadataPath": 'oss_data/{}'.format(meta_file)
+    }, indent=2))
+
+
+if __name__ == '__main__':
+    plac.call(main)