Update Authors section in README.md

README.md

@@ -59,67 +59,4 @@ You can find the results in the following table:
 
 
 ## Authors
-* [**Md Mijanur Rahman**](https://github.com/mijanr)
-# This repository contains the code for time-series (TS) classification with various state-of-the-art TS classification models. 
-
-Entire pipeline is developed in a way such that an easy integration of ***mlflow***, ***hydra*** and ***optuna sweeper*** is possible.
-
-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:
-```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.
-
-3. To run a model on a specific dataset, run the following command in the terminal, in the root directory of the repository:
-```bash
-python main.py "dataset_name=[Handwriting]" 
-```
-For a multirun case:
-```bash
-python main.py --multirun "dataset_name=[Handwriting]"  
-```
-To run for a specific model:
-```bash
-python main.py --multirun "dataset_name=[Handwriting]" "models=LSTM_FCN"
-```
-Model name can be anything that is available in the `codes/models` directory, given corresponding configs are also available.
-
-Similarly, other parameters can also be specified in the terminal, and passed as arguments. 
-## Mlruns
-All the runs are stored in the `mlruns` directory. To visualize the runs, run the following command in the terminal, in the root directory of the repository:
-```bash
-mlflow ui
-```
-This will start a server, and the runs can be visualized in the browser at `localhost:5000`.
-
-## Requirements
-requirements.yml file contains all the dependencies required to run the code. To install all the dependencies, run the following command in the terminal, given that anaconda is installed:
-```bash
-conda env create -f requirements.yaml
-```
-This will create a conda environment named `ts_cl` with all the dependencies installed.
-It insall Pytorch with CPU support. To install Pytorch with GPU support, follow the instructions given [here](https://pytorch.org/get-started/locally/).
-
-## Datasets
-This repository uses the datasets from the [UEA & UCR Time Series Classification Repository](https://www.timeseriesclassification.com/). The datasets are automatically downloaded and stored in the `data` directory.
-
-## Models
-We use the classification models available in [tsai library](https://timeseriesai.github.io/tsai/). Models can be added to this repository by adding the corresponding config file in the `config` directory, and the corresponding model file in the `codes/models` directory.
-
-## Results
-You can find the results in the following table:
-
-<!--START-->
-
-<!--END-->
-
-
-## Authors
-* [**Md Mijanur Rahman**](https://github.com/mijanr)
-
+* [**Md Mijanur Rahman**](https://github.com/mijanr)