initial commit

.gitignore

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+# Ignore Python bytecode
+__pycache__/
+*.pyc
+*.pyo
+
+# Ignore Jupyter Notebook checkpoints
+.ipynb_checkpoints/
+.ipynb_checkpoints/Qwen_sentiment_analysis.ipynb
+
+# Ignore virtual environment directories
+venv/
+env/
+*.egg-info/
+
+# Ignore environment configuration files
+*.env
+*.ini
+*.yaml
+
+# Ignore local configuration files
+*.local
+
+# Ignore log files
+*.log
+
+# Ignore system files
+.DS_Store
+Thumbs.db
+
+# Ignore Jupyter Notebook output files
+*.nbconvert.ipynb
+
+# Ignore other IDE or editor-specific files
+.idea/
+.vscode/
+*.sublime-project
+*.sublime-workspace

README.md

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+# Task: Sentiment Analysis with IMDB Dataset 
+
+## Description:
+
+### Task Objectives
+
+1. Design and refine prompts to achieve maximum accuracy in sentiment analysis
+2. Implement local inference for SLMs using Python
+3. Compare the performance and efficiency of two SLMs of different sizes
+4. Analyze results and draw meaningful insights
+5. Document the process and findings effectively
+
+
+### Data
+
+The dataset of IMDB movie reviews is accessed through [Huggingface](https://huggingface.co/datasets/ajaykarthick/imdb-movie-reviews), which contains around 50k samples; 40k for 'train' and 10k for 'test' dataset.
+
+### Models 
+
+1. [bartowski/Qwen2.5-1.5B-Instruct-GGUF](https://huggingface.co/bartowski/Qwen2.5-1.5B-Instruct-GGUF/blob/main/Qwen2.5-1.5B-Instruct-Q5_K_M.gguf): A lightweight 1.5B-parameter instruction-
+tuned model with enhanced capabilities in coding, mathematics, and handling structured data.
+
+
+2. [bartowski/Qwen2.5-0.5B-Instruct-GGUF](https://huggingface.co/bartowski/Qwen2.5-0.5B-Instruct-GGUF/blob/main/Qwen2.5-0.5B-Instruct-Q5_K_M.gguf): A model with 500M parameters, making it
+three times smaller than its 1.5B counterpart, yet still highly capable for many tasks.
+
+
+### Project Structure
+
+The project is organized as follows:
+
+    sentiment_analysis/
+    ├── config.py
+    ├── data_loader.py
+    ├── data/
+    │   ├── sampled_dataset.csv
+    ├── predictions/
+    │   ├── this folder contains text file of all predictions from model inference
+    ├── plots/
+    │   ├─ this folder contains plots for each prompting step
+    ├── Qwen_sentiment_analysis.ipynb
+    ├── requirements.txt
+    ├── README.md
+
+1. config.py: Contains configurations such as directory paths and dataset locations.
+
+2. data_loader.py : Contains code for dataset sampling.
+
+3. data/: Directory containing the dataset sampled and used in the project.
+
+4. predictions/: contains 8 text files consisting of model predictions for each step.
+
+5. plots/: contains 4 plots for average model accuracies across 3 runs per temperature [0,1,2] for each model based on the 4 prompting steps.
+
+6. Qwen_sentiment_analysis.ipynb: The Jupyter notebook containing the analysis and model-building code.  
+
+
+### Installation
+
+To run this project, follow the steps below:
+
+1. Clone the Project: Download the project as a zip file or clone it.
+
+2. Set Up the Environment: Install the required Python packages using pip:
+
+```python
+pip install -r requirements.txt
+```
+
+### Usage
+
+### Setting Up a Virtual Environment and Installing Dependencies
+
+To run this project in a local Jupyter Notebook, follow these steps:
+
+1. **Clone the repository**:
+   ```bash
+   git clone https://github.com/zarakokolagar/sentiment_analysis_LLM
+   cd sentiment_analysis_LLM  
+
+## Setting Up a Virtual Environment and Running the Project
+
+Follow these steps to set up a virtual environment and run the Jupyter Notebook locally:
+
+#### 1. Install `virtualenv` (if not already installed)
+
+If you don’t have `virtualenv` installed, you can install it using:
+
+```bash
+pip install virtualenv
+```
+
+#### 2. Create a virtual environment
+Create a virtual environment by running the following command:
+```bash
+virtualenv venv
+```
+#### 3. Activate the virtual environment
+On macOS/Linux:
+
+```bash
+source venv/bin/activate
+```
+#### 4. Install the required dependencies
+With the virtual environment activated, install the dependencies listed in the `requirements.txt` file
+
+```bash
+pip install -r requirements.txt
+```
+
+#### 5. Run Jupyter Notebook
+If you don’t have Jupyter Notebook installed, you can install it with:
+```bash
+pip install notebook
+```
+Then, start Jupyter Notebook by running:
+
+```bash
+jupyter notebook
+```
+
+#### 6. Deactivate the virtual environment
+Once you're done working, deactivate the virtual environment by running:
+
+```bash
+deactivate
+```
+
+**Note:**
+
+Alternatively, you could run the notebook on [Colab Notebook](https://colab.research.google.com/drive/1UWiUKyRz0HgGtB_frLV6Kl0MMb7J25Kg#scrollTo=ihomXxB4Svsk&uniqifier=1)
+If you are running the code from Google colab, all required libraries and data handling is included in the colab code.
+
+
+
+## License 
+
+Not required

config.py

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+import os
+
+# Define the base directory for the project
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+
+# Define the data directory
+DATA_DIR = os.path.join(BASE_DIR, 'data')
+
+#Define predictions directory
+PRED_DIR = os.path.join(BASE_DIR, 'predictions')
+
+#define plot directory for predictions
+PLOT_DIR = os.path.join(BASE_DIR, 'plots')
+os.makedirs(PLOT_DIR, exist_ok=True)
+
+SAMPLED_DATA_PATH = os.path.join(DATA_DIR,'sampled_dataset.tsv')