diff --git a/scripts/msmarco_v2/segment_docs.py b/scripts/msmarco_v2/segment_docs.py
new file mode 100644
index 000000000..8a8c7aa7b
--- /dev/null
+++ b/scripts/msmarco_v2/segment_docs.py
@@ -0,0 +1,106 @@
+#
+# Pyserini: Reproducible IR research with sparse and dense representations
+#
+# 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.
+#
+# Starting point for writing this script
+# https://github.com/castorini/docTTTTTquery/blob/master/convert_msmarco_passages_doc_to_anserini.py
+import argparse
+import os
+import sys
+import gzip
+import json
+import spacy #Currently using spacy 2.3.5
+from tqdm import tqdm
+import re
+import glob
+from multiprocessing import Pool
+
+def create_segments(doc_text, max_length, stride):
+    doc_text = doc_text.strip()
+    doc = nlp(doc_text[:10000])
+    sentences = [sent.string.strip() for sent in doc.sents]
+    segments = []
+    
+    for i in range(0, len(sentences), stride):
+        segment = " ".join(sentences[i:i+max_length])
+        segments.append(segment)
+        if i + max_length >= len(sentences):
+            break
+    return segments
+
+def split_document(f_ins, f_out): 
+    print('Spliting documents...')
+    output = open(f_out, 'w')
+    output_id = open(f_out.replace(".json", ".id"), 'w')
+    for f_in in f_ins:
+        with gzip.open(f_in, 'rt', encoding='utf8') as in_fh:
+            for json_string in tqdm(in_fh):
+                doc = json.loads(json_string)
+                f_doc_id = doc['docid']
+                doc_url = doc['url']
+                doc_title = doc['title']
+                doc_headings = doc['headings']
+                doc_text = doc['body']
+
+                segments = create_segments(doc_text, args.max_length, args.stride)
+
+                for seg_id, segment in enumerate(segments):
+                    # expanded_text = f'{doc_url}\n{doc_headings}\n{doc_title}\n{segment}'
+                    doc_seg = f'{f_doc_id}#{seg_id}'
+                    output_dict = {'docid': doc_seg, 'url': doc_url, 'title': doc_title, 'headings': doc_headings, 'segment': segment}
+                    output.write(json.dumps(output_dict) + '\n')  
+                    output_id.write(doc_seg+'\n')
+
+    output.close()
+    output_id.close()
+    print('Done!')
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(
+        description='Segment MS MARCO V2 original docs into passages')
+    parser.add_argument('--input', required=True, help='MS MARCO V2 corpus path.')
+    parser.add_argument('--output', required=True, help='output file path with json format.')
+    parser.add_argument('--max_length', default=10, help='maximum sentence length per passage')
+    parser.add_argument('--stride', default=5, help='the distance between each beginning sentence of passage in a document')
+    parser.add_argument('--num_workers', default=1, type=int)
+    args = parser.parse_args()
+
+
+    os.makedirs(os.path.dirname(args.output_docs_path), exist_ok=True)
+
+
+    max_length = args.max_length
+    stride = args.stride
+    nlp = spacy.blank("en")
+    nlp.add_pipe(nlp.create_pipe("sentencizer"))
+
+    files = glob.glob(os.path.join(args.original_docs_path, '*.gz'))
+    num_files = len(files)
+    pool = Pool(args.num_workers)
+    num_files_per_worker=num_files//args.num_workers
+    for i in range(args.num_workers):
+        f_out = os.path.join(args.output_docs_path, 'doc' + str(i) + '.json')
+        if i==(args.num_workers-1):
+            file_list = files[i*num_files_per_worker:]
+        else:
+            file_list = files[i*num_files_per_worker:((i+1)*num_files_per_worker)]
+
+        pool.apply_async(split_document ,(file_list, f_out))
+
+    pool.close()
+    pool.join()
+
+    print('Done!')
+
+