diff --git a/bin/get_freqs.py b/bin/get_freqs.py
new file mode 100755
index 000000000..e25b8b9d3
--- /dev/null
+++ b/bin/get_freqs.py
@@ -0,0 +1,97 @@
+#!/usr/bin/env python
+
+from __future__ import unicode_literals
+
+import plac
+import joblib
+from os import path
+import os
+import bz2
+import ujson
+import codecs
+from preshed.counter import PreshCounter
+from joblib import Parallel, delayed
+
+import spacy.en
+from spacy.strings import StringStore
+from spacy.en.attrs import ORTH
+
+
+def iter_comments(loc):
+    with bz2.BZ2File(loc) as file_:
+        for line in file_:
+            yield ujson.loads(line)
+
+
+def null_props(string):
+    return {
+        'flags': 0,
+        'length': len(string),
+        'orth': string,
+        'lower': string,
+        'norm': string,
+        'shape': string,
+        'prefix': string,
+        'suffix': string,
+        'cluster': 0,
+        'prob': -22,
+        'sentiment': 0
+    }
+
+
+def count_freqs(input_loc, output_loc):
+    nlp = spacy.en.English(data_dir=os.environ['SPACY_DATA'], Parser=None,
+                           Tagger=None, Entity=None, load_vectors=False)
+    nlp.vocab.lexeme_props_getter = null_props
+
+    counts = PreshCounter()
+    tokenizer = nlp.tokenizer
+    for json_comment in iter_comments(input_loc):
+        doc = tokenizer(json_comment['body'])
+        doc.count_by(ORTH, counts=counts)
+
+    with codecs.open(output_loc, 'w', 'utf8') as file_:
+        for orth, freq in counts:
+            string = nlp.vocab.strings[orth]
+            file_.write('%d\t%s\n' % (freq, repr(string)))
+
+
+def parallelize(func, iterator, n_jobs):
+    Parallel(n_jobs=n_jobs)(delayed(func)(*item) for item in iterator)
+
+
+def merge_counts(locs, out_loc):
+    string_map = StringStore()
+    counts = PreshCounter()
+    for loc in locs:
+        with codecs.open(loc, 'r', 'utf8') as file_:
+            for line in file_:
+                freq, word = line.strip().split('\t', 1)
+                orth = string_map[word]
+                counts.inc(orth, int(freq))
+    with codecs.open(out_loc, 'w', 'utf8') as file_:
+        for orth, count in sorted(counts, reverse=True, key=lambda item: item[1]):
+            string = string_map[orth]
+            file_.write('%d\t%s\n' % (count, string))
+
+
+@plac.annotations(
+    input_dir=("Directory of input files"),
+    freqs_dir=("Directory for frequency files"),
+    output_loc=("Location for output file"),
+    n_jobs=("Number of workers", "option", "n", int),
+)
+def main(input_dir, freqs_dir, output_loc, n_jobs=2):
+    tasks = []
+    for filename in os.listdir(input_dir):
+        input_path = path.join(input_dir, filename)
+        output_path = path.join(freqs_dir, filename.replace('bz2', 'freq'))
+        tasks.append((input_path, output_path))
+
+    parallelize(count_freqs, tasks, n_jobs)
+
+    merge_counts([out for in_, out in tasks], output_loc)
+                
+
+if __name__ == '__main__':
+    plac.call(main)