Fixed README formatting issues

README.md

@@ -31,6 +31,7 @@ Create an environment for baseline training with a specific python version (Pyth
 ```shell  
 conda create -n <baseline-env-name> python=3.6  
 ```  
+
 ### Create conda environment (for prompt learning)  
 Create an environment for prompt-based methods (Python 3.10 is **required**)  
 ```shell  
@@ -48,6 +49,7 @@ To deactivate the conda environment, run: (Only after running all the experiment
 ```shell  
 conda deactivate
 ```  
+
 #### Download and extract SOLOIST pre-trained model  
 Download and unzip the pretrained model, this is used for fine-tuning 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).  
   
@@ -66,10 +68,12 @@ 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
@@ -120,7 +124,8 @@ pip install requirements.txt
 Train a separate model for each data split. Edit the [train_baseline.sh](baseline/train_baseline.sh) file to modify the hyperparameters while training (learning rate, epochs). Use `CUDA_VISIBLE_DEVICES` to specify a CUDA device (GPU) for training the model.   
 ```shell  
 sh train_baseline.sh -d <data-split-name>
-```  
+```
+
 Pass the data split name to `-d` flag. Possible values are: `5-dpd`, `10-dpd`, `50-dpd`, `100-dpd`, `125-dpd`, `250-dpd`  
   
 Example training command: `sh train_baseline.sh -d 50-dpd`  
@@ -131,13 +136,15 @@ Choose a checkpoint of the saved baseline model to generate belief state predict
 Set the `MODEL_CHECKPOINT` environment variable with the path to the chosen model checkpoint. It should only contain the path from the "experiment-{datetime}" folder.  
 ```shell  
 export MODEL_CHECKPOINT=<experiment-folder>/<data-split-name>/<checkpoint-folder>
-```  
+```
+
 Example: `export MODEL_CHECKPOINT=experiment-20220831/100-dpd/checkpoint-90000`  
   
 Generate belief states by running decode script  
 ```shell  
 sh decode_baseline.sh
 ```  
+
 The generated predictions are saved under `OUTPUTS_DIR_BASELINE` folder. Some of the generated belief state predictions are uploaded to this repository and can be found under [outputs](outputs) folder.  
   
 ### Baseline Evaluation  
@@ -147,7 +154,8 @@ The standard Joint Goal Accuracy (JGA) is used to evaluate the belief prediction
 Edit the [evaluate.py](baseline/evaluate.py) to set the predictions output file before running the evaluation  
 ```shell  
 python evaluate.py
-```  
+```
+
 ### Results from baseline experiments  
 |data-split| JGA |  
 |--|:--:|  
@@ -171,21 +179,27 @@ Change directory to `prompt-learning` and install the requirements. Make sure th
 ```shell  
 cd prompt-learning
 pip install requirements.txt
-```  
+```
+
 ### Train the prompt model  
 Train a separate model for each data split. Edit the [train_prompting.sh](prompt-learning/train_prompting.sh) file to modify the default hyperparameters for training (learning rate, epochs).
 ```shell  
 sh train_prompting.sh -d <data-split-name>
-```  
+```
+
 Pass the data split name to `-d` flag. 
 Possible values are: `5-dpd`, `10-dpd`, `50-dpd`, `100-dpd`, `125-dpd`, `250-dpd`  
   
 Example training command: `sh train_baseline.sh -d 50-dpd`  
 
 **Some `train_prompting.sh` flags**:
+
 `--num_epochs 10` - Number of epochs
+
 `--learning_rate 5e-5` - Initial learning rate for Optimizer
+
 `--with_inverse_prompt` - Use Inverse Prompt while training
+
 `--inverse_prompt_weight 0.1` - Weight of the inverse prompt for loss function
 
 **Note:** The defaults in `train_prompting.sh` are the best performing values.
@@ -197,6 +211,7 @@ Generate belief states by running the below script:
 ```shell  
 sh test_prompting.sh -m <tuned-prompt-model-path>
 ```
+
 The argument `-m` takes the relative path of saved model from `SAVED_MODELS_PROMPT` env variable. It takes the following structure `-m <data-split-name>/<experiment-folder>/<epoch-folder>`
 
 Example: `sh test_prompting.sh -m 50-dpd/experiment-20221003T172424/epoch-09`
@@ -210,6 +225,7 @@ Edit the [evaluate.py](prompt-learning/evaluate.py) to set the predictions outpu
 ```shell  
 python evaluate.py
 ```  
+
 ### Results from prompt-based belief state generations
 |data-split| JGA* |  
 |--|:--:|  
@@ -241,4 +257,5 @@ Generate belief states by running the below script:
 ```shell  
 sh test_prompting.sh -m <tuned-prompt-model-path>
 ```
+
 // TODO :: Add results