README.md

@@ -154,20 +154,13 @@ The datasets used in this project will be taken from Li et al.[^6] We confine ou
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 ## 🛠️ Set Up <a name="setup"></a>
 
-Creating a virtual environment to ensure that dependencies between the different projects are separated is a recommended first step:
+Install all necessary requirements and create a virtual environment via:
 
 ```bash
-python3 -m venv mrda-venv
-source mrda-venv/bin/activate
+sbatch install.sh
 ```
 
-Install all necessary requirements next:
-
-```bash
-pip install -r requirements.txt
-```
-
-ℹ️ Note that we use `python3.9`.
+ℹ️ Note that you have to adapt your `conda` name in the `install.sh`.
 
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 ## ⚙️ Usage <a name="usage"></a>
@@ -233,9 +226,6 @@ python3 inference.py
 
 ## 🏯 Code-Structure <a name="code-structure"></a>
 
-- ⚙️ [`requirements.txt`](requirements.txt): All necessary modules to install.
-- 📱 [`main.py`](main.py): Our main code file is responsible for organizing input options and calling the necessary functions to preprocess datasets, train the model, and evaluate it on a test set.
-- 🎥 [`inference.py`](inference.py): Run a demo version of our to test if an input sentence contains a metonymy.
 - 💻 [`Code`](code): Here, you can find all code files for our different models and data augmentation methods, as well as a [`submit_template.sh`](Code/submit_template.sh).
 - 📀 [`data`](data): Find all datasets in this folder.
     - 🗂️ [`backtranslations`](data/backtranslations): Contains unfiltered generated paraphrases.
@@ -246,6 +236,12 @@ python3 inference.py
     - 🗂️ [`images`](documentation/images): Contains all relevant visualizations.
     - 🗂️ [`organization`](documentation/organization): Our research plan, presentation, final reports.
     - 🗂️ [`results`](documentation/results): Find tables of our results. 
+- 🎥 [`inference.py`](inference.py): Run a demo version of our to test if an input sentence contains a metonymy.
+- ⚙️ [`install.sh`](install.sh): Installs all necessary modules into a virtual `conda` environment.
+- 📱 [`main.py`](main.py): Our main code file is responsible for organizing input options and calling the necessary functions to preprocess datasets, train the model, and evaluate it on a test set.
+
+
+
 
 
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requirements.txt

-nltk==3.7
-numpy==1.23.5
-pandas==1.5.2
-torchvision
-torchaudio
-torch>=1.13.0+cu116
-tqdm==4.64.1
-evaluate ==0.3.0
-matplotlib==3.5.2
-scikit-learn==1.2.1
-transformers==4.26.1