✅ (cli) add test to cli

README.md

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 # Bert Embeddings
 
-[![Build Status](https://travis-ci.org/imgarylai/bert-embedding.svg?branch=master)](https://travis-ci.org/imgarylai/bert-embedding) [![PyPI version](https://badge.fury.io/py/bert-embedding.svg)](https://pypi.org/project/bert-embedding/) [![Documentation Status](https://readthedocs.org/projects/bert-embedding/badge/?version=latest)](https://bert-embedding.readthedocs.io/en/latest/?badge=latest)
+[![Build Status](https://travis-ci.org/imgarylai/bert-embedding.svg?branch=master)](https://travis-ci.org/imgarylai/bert-embedding) [![codecov](https://codecov.io/gh/imgarylai/bert-embedding/branch/master/graph/badge.svg)](https://codecov.io/gh/imgarylai/bert-embedding) [![PyPI version](https://badge.fury.io/py/bert-embedding.svg)](https://pypi.org/project/bert-embedding/) [![Documentation Status](https://readthedocs.org/projects/bert-embedding/badge/?version=latest)](https://bert-embedding.readthedocs.io/en/latest/?badge=latest) 
 
 
 [BERT](https://arxiv.org/abs/1810.04805), published by [Google](https://github.com/google-research/bert), is new way to obtain pre-trained language model word representation. Many NLP tasks are benefit from BERT to get the SOTA.

bert_embedding/tests/test_cli.py

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+import subprocess
+import time
+
+
+def test_bert_embedding():
+    process = subprocess.check_call(['python', './bert_embedding/cli.py',
+                                     '--model', 'bert_12_768_12', '--dataset_name', 'book_corpus_wiki_en_uncased',
+                                     '--max_seq_length', '25', '--batch_size', '256',
+                                     '--oov_way', 'avg', '--sentences', '"is this jacksonville ?"'])
+    time.sleep(5)