Added Environment setup section to README.md notes

README.md

@@ -17,3 +17,75 @@ MultiWOZ 2.1 [dataset](https://github.com/budzianowski/multiwoz/blob/master/data
 | test | 193 | 894 |
 
 In the above table, term "*dpd*" refers to "*dialogues per domain*". For example, *50-dpd* means *50 dialogues per each domain*. 
+
+All the training and testing data can be found under [/data/baseline/](data/baseline/) folder.
+
+## Environment Setup
+### Baseline (SOLOIST) Environment Setup
+Python 3.6 is required for training the baseline model. `conda` is used for creating environments.
+
+#### Create conda environment
+Create an environment with specific python version (Python 3.6).
+```shell
+conda create -n <env-name> python=3.6
+```
+
+#### Activate the conda environment
+Activate the conda environment for installing the requirements.
+```shell
+conda activate <env-name>
+```
+
+#### Deactivating the conda evironment
+Deactivate the conda environment by running the following command:
+(After running all the experiments)
+```shell
+conda deactivate
+```
+#### Download and extract SOLOIST pre-trained model
+Download and unzip the pretrained model, this is used for finetuning the baseline and prompt-based methods. For more details about the pre-trained SOLOIST model, refer to the GitHub [repo](https://github.com/pengbaolin/soloist).
+
+Download the zip file, replace the `/path/to/folder` from the below command to a folder of your choice.
+```shell
+wget https://bapengstorage.blob.core.windows.net/soloist/gtg_pretrained.tar.gz \
+	-P /path/to/folder/
+```
+
+Extract the downloaded pretrained model zip file.
+```shell
+tar -xvf /path/to/folder/gtg_pretrained.tar.gz
+```
+
+#### Clone the repository
+Clone the repository for source code
+```shell
+git clone https://git.pavanmandava.com/pavan/master-thesis.git
+```
+Pull the changes from remote (if local is behind the remote)
+```shell
+git pull
+```
+Change directory
+```shell
+cd master-thesis
+```
+
+#### Set Environment variables
+Next step is to set environment variables that contains path to pre-trained model, saved models and output dirs.
+
+Edit the [set_env.sh](set_env.sh) file and set the paths for: (`nano` or `vim` can be used)
+`PRE_TRAINED_SOLOIST` - Path to the extracted pre-trained SOLOIST model
+`SAVED_MODELS_BASELINE` - Path for saving the trained models at checkpoints
+`OUTPUTS_DIR_BASELINE` - Path for storing the outputs of belief state predictions.
+
+```shell
+nano set_env.sh
+```
+Save the edited file and `source` it
+```shell
+source set_env.sh
+```
+Run the below line to unset the environment variables
+```shell
+sh unset_env.sh
+```