Update README.md

README.md

@@ -1,18 +1,18 @@
-# AutoTSFlow: An automated code-base for time-series classifiers
+# AutoTSFlow: An automated code-base for time-series classification
 This repository contains the code for time-series (TS) classification with various state-of-the-art TS classification models. 
 The entire pipeline is developed for easy integration of ***mlflow***, ***hydra***, and ***optuna sweeper***.
 
-1. The simplest way to run a model on a specific dataset is to run the following command in the terminal, in the root directory of the repository:
+1. The simplest way to run a model on a specific dataset is to run the following command in the terminal in the root directory of the repository:
 ```bash
 python main.py
 ```
 This will run a model on a dataset specified in the config file `main_config.yaml`, located in the `config` directory. 
 
-2. To optimize hyperparameters of a model, run the following command in the terminal, in the root directory of the repository:
+2. To optimize the hyperparameters of a model, run the following command in the terminal in the root directory of the repository:
 ```bash
 python main.py --multirun
 ```
-This will run a model on a dataset specified in the config file `main_config.yaml`, located in the `config` directory. However, this time, a search space, specified in `config/search_space/model_name` will be used by optuna to find the optimal hyperparameters. A total number of trial is specified in the `main_config.yaml` file.
+This will run a model on a dataset specified in the config file `main_config.yaml`, located in the `config` directory. However, this time, a search space specified in `config/search_space/model_name` will be used by optuna to find the optimal hyperparameters. A total number of trials is specified in the `main_config.yaml` file.
 
 3. To run a model on a specific dataset, run the following command in the terminal, in the root directory of the repository:
 ```bash