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-# RECLASS: Multi-Task Deep Learning for App Review Classification
-
-**COMP6013 | Oxford Brookes University | 2025-26**
-
----
-
-# README not finished
-
-## Overview
-
-RECLASS is a multitask learning system which uses a shared multilingual transformer encoder with task-specific heads and single-task implementations for optional comparison.
-
-| Task | Output | Classes |
-|------|--------|---------|
-| Bug Report Detection | Binary | Yes / No |
-| Feature Request Detection | Binary | Yes / No |
-| Aspect Classification | Multi-class | Driver, App, Pricing, Service, Payment, General |
-| Aspect Sentiment | Multi-class | Positive, Neutral, Negative |
-
-## Dataset
-
-- **Source**: [Uber Customer Reviews (Kaggle)](https://www.kaggle.com/datasets/khushipitroda/ola-vs-uber-play-store-reviews)
-- **Original size**: ~1.07M Reviews
-- **After Preprocessing**: ~495K Reviews
-- **Annotation subsets**: 5,000 from the original distribution, 5,000 from a keyword boosted sample
-
-## Preprocessing Steps
-
-- Removed URLS and emails
-- Normalised text and punctuation
-- Removed duplicate reviews
-- Filtered reviews less than 5 words
-
-- Output sets
-    -   Original: matches the original distribution of the raw dataset
-    -   Boosted: oversamples bug reports and feature requests using keyword heuristics
-
-## Model
-
-- Encoder: XLM-RoBERTa (large multilingual transformer model)
-- Architecture:
-    - Shared encoder
-    - Task-specific classification heads
-- Training setups:
-    - MTL (Multitask learning)
-    - STL (Single-task learning)
-
-Class weights are applied to reduce imbalance effects.
-
-## Repository Structure
-
-.
-├── data
-│   └── processed
-│       ├── boosted_test.csv
-│       ├── boosted_train.csv
-│       ├── boosted_val.csv
-│       ├── original_test.csv
-│       ├── original_train.csv
-│       ├── original_val.csv
-│       └── review.csv
-├── notebooks/
-│   
-├── outputs
-│   └── figures/
-├── README.md
-├── architecture.png
-└── src
-    ├── dataset.py
-    ├── evaluate.py
-    ├── infer.py
-    ├── model.py
-    ├── multitag.py
-    ├── preprocess.py
-    ├── sampler.py
-    └── train.py
-
-## Results
-
-Evaluation includes Precision, Recall, Macro F1, Confusion matrices and confidence analysis.
-
-Results and summaries are found in outputs/*.json and outputs/figures/
-
-## Installation
-
-```
-# Create conda environment
-conda create -n reclass python=3.11 
-conda activate reclass
-```
-
-```
-# Install dependencies
-conda install --file requirements.txt
-```
-
-## Usage
-
-#### Train Model
-
-```
-python src/train.py --mode mtl --dataset original
-```
-
-#### Evaluate Model
-
-```
-python src/evaluate.py --mode mtl --dataset original --model_path <model>.pt
-```
-
-#### Run Inference
-
-```
-python src/infer.py --mode mtl --model_path <model>.pt --dataset review
-```
-
-## Notes
-
-- The same tokenizer is used across training, evaluation and inference to ensure consistency
-- Sampling and preprocessing choices are documented further in src files and dissertation
-
----
-
-*Last updated: January 2025*