diff --git a/README.md b/README.md
index 51caf85..ca8cf56 100644
--- a/README.md
+++ b/README.md
@@ -7,12 +7,12 @@ One thing I would like to emphasize that because my motivation is more on demons
 1. Make sure `tensorflow` has been installed.
 2. First download the full S&P 500 data from [Yahoo! Finance ^GSPC](https://finance.yahoo.com/quote/%5EGSPC?p=^GSPC) (click the "Historical Data" tab and select the max time period). And save the .csv file to `data/SP500.csv`.
 3. Run `python data_fetcher.py` to download the prices of individual stocks in S & P 500, each saved to `data/{{stock_abbreviation}}.csv`.
-(NOTE: Google Finance API returns the prices for 4000 days maximum. If you are curious about the data in even early times, try modify `data_fetcher.py` code to send multiple queries for one stock. Here is the data archive ([stock-data-lilianweng.tar.gz](https://drive.google.com/open?id=1QKVkiwgCNJsdQMEsfoi6KpqoPgc4O6DD)) of stock prices I crawled up to Jul, 2017. Please untar this file to replace the "data" folder in the repo for test runs.)
+(NOTE: Google Finance API returns the prices for 4000 days maximum. If you are curious about the data, try modifying `data_fetcher.py` code to send multiple queries for a stock. Here is the data archive ([stock-data-lilianweng.tar.gz](https://drive.google.com/open?id=1QKVkiwgCNJsdQMEsfoi6KpqoPgc4O6DD)) of stock prices I stored up to Jul, 2017. Please unzip this file to replace the "data" folder in the repo for test runs.)
 4. Run `python main.py --help` to check the available command line args.
 5. Run `python main.py` to train the model.
 
 
-For examples,
+Examples
 - Train a model only on SP500.csv; no embedding
 ```bash
 python main.py --stock_symbol=SP500 --train --input_size=1 --lstm_size=128 --max_epoch=50