Create README.md

README.md

@@ -0,0 +1,61 @@
+---
+language:
+- en
+- tl
+tags:
+- sarcasm-detection
+- mock-politeness
+- multi-task-learning
+- code-mixed
+license: mit
+base_model:
+- FacebookAI/xlm-roberta-base
+---
+
+# XLM-RoBERTa with Multi-Task Learning for Sarcasm and Mock Politeness Detection
+
+## Model Description
+This project fine-tunes **XLM-RoBERTa** for detecting **sarcasm** and **mock politeness** in **Filipino (English, Tagalog, or code-mixed (Taglish))** faculty evaluation texts.  
+
+Two models are included:
+- **MTL model** → sarcasm detection (main task) + mock politeness detection (auxiliary task)  
+- **STL model** → sarcasm detection only  
+
+The models are packaged into a **desktop app (Tkinter + Python)** for easy testing.
+
+---
+
+## Intended Uses & Limitations
+
+### Intended Use
+- Demonstrating multi-task learning in NLP  
+- Exploring sarcasm and politeness detection in Taglish text  
+- Academic/research purposes only  
+
+### Limitations
+- Trained on a domain-specific dataset (faculty evaluations)  
+- May not generalize well outside Taglish or academic settings  
+- Predictions are not guaranteed to be accurate for all contexts  
+
+---
+
+## How to Use
+
+1. Download the **XLM-R folder** from this repository.  
+2. Inside the folder, locate and open:  XLM-R/XLM-R.exe
+3. Use the GUI to input text or upload a `.csv` file (see included `INPUT_SAMPLE.csv`).  
+4. The app will output predictions for sarcasm (and mock politeness if using MTL).  
+
+*(No coding required — the `.exe` is standalone on Windows.)*
+
+---
+
+## Training Data
+- Collected faculty evaluation texts written in **Filipino**  (English, Tagalog, or code-mixed (Taglish)) 
+- Annotated for sarcasm and mock politeness  
+
+---
+
+## Evaluation
+- Compared **Single-Task (STL)** vs **Multi-Task (MTL)**  
+- Metrics: accuracy, precision, recall, F1