This model is a fine‑tuned version of XLM‑RoBERTa‑base for 7‑class emotion detection in code‑mixed Pakistani text, including English, Roman Urdu, and Urdu script. It is designed for social media analysis, customer feedback, and multilingual NLP applications in the Pakistani context.

Base Model: xlm-roberta-base
Emotion Classes: anger, disgust, fear, joy, neutral, sad, surprise
Language Support: English, Roman Urdu, Urdu script

Training Data & Balancing Strategy

Data Sources

The training dataset was compiled from multiple sources:

Source	Description
GoEmotions	Subset of the Google emotion dataset
Parul Pandey’s Emotion Dataset	Primary source (most samples)
Roman Urdu‑English Code‑Switched Dataset	Specialised code‑mixed data
LLM‑generated samples (GPT, Grok, Gemini)	Used for English‑Urdu translation and augmentation

Cleaning & Preprocessing

Duplicate rows removed
Standardised column structure across three languages (English, Urdu, Roman Urdu)
Seven emotion classes retained
Light spelling normalisation for Roman Urdu

Class Imbalance & Balancing Strategy

Initial raw data showed significant imbalance:

English: Fear and Sad dominated (~14-16), Disgust very low (~7.1%).
Roman Urdu: Fear as low as 8.1%, Sad relatively low.
Urdu: More balanced but Sad often lowest.

Targeted Augmentation (Weak Classes)

We augmented Sad, Disgust, Surprise (and Fear/Joy where needed per language) using:

Random Swap (EDA) for Urdu and Roman Urdu – swaps two random words, preserving sentiment while increasing structural variety.
Oversampling + Cross‑Lingual Injection for English Disgust – duplicated high‑quality samples and added code‑switched (English + Roman Urdu) variants.
LLM‑based translation (GPT, Grok, Gemini) to generate parallel English‑Urdu/Roman Urdu versions.

Approximate additional samples added:

Total synthetic/augmented samples: ~2,000–2,500
English Disgust: +334 samples (~30% of that class augmented)
Urdu/Roman Urdu weak classes (Surprise, Disgust): +400–500 samples each

Majority Class Control (Mild Downsampling)

We randomly under‑sampled the majority English classes (Anger, Fear, Sad) to prevent bias:

Original counts: ~1,400–1,600 each
Reduced by ~200–400 samples to a ceiling of ~1,200 each

Quality Controls

Augmentation applied to the master dataset before train/test split (so test set also contains augmented variants, proving model robustness).
Stratified split ensures balanced representation across languages and emotions.
Manual spot‑check performed on augmented Roman Urdu samples.

Final Dataset Statistics

Total rows: 20,235
Languages: English (7,734), Roman Urdu (5,572), Urdu (6,929)

Per‑language emotion counts (exact numbers used in training):

Language	anger	disgust	fear	joy	neutral	sad	surprise	Total
English	1128	1106	1207	1200	1004	1207	882	7734
Roman Urdu	839	791	839	839	839	691	734	5572
Urdu	1009	935	1059	980	948	1059	939	6929
Total	2976	2832	3105	3019	2791	2957	2555	20235

Global percentages:

Highest: Fear (~15.34%)
Lowest: Surprise (~12.63%)
Others: 13.8% – 14.9%

The final dataset is much more balanced than raw sources, with all classes represented across all three languages.

Training Details

Framework: PyTorch + Hugging Face Transformers
Model: XLM‑RoBERTa‑base (12 layers, 278M parameters)
Optimizer: AdamW (learning rate = 2e‑5)
Batch size: 32
Epochs: 5 (early stopping patience = 2)
Max sequence length: 128 tokens
Warmup steps: 10% of total steps
Weight decay: 0.01
FP16: Enabled (GPU acceleration)

Evaluation Results

The model was evaluated on a held‑out test set of 4,047 samples (20% of total data, stratified).

Overall Performance

Accuracy: 83.4%
Macro F1‑score: 0.835
Weighted F1‑score: 0.833

Per‑Class Performance

Emotion	Precision	Recall	F1‑score	Support
anger	0.83	0.87	0.85	595
disgust	0.90	0.93	0.91	567
fear	0.93	0.90	0.92	621
joy	0.82	0.79	0.81	604
neutral	0.77	0.75	0.76	558
sad	0.75	0.75	0.75	591
surprise	0.80	0.81	0.81	511

Confusion Matrix (Actual vs. Predicted)

Actual ↓ / Predicted →	anger	disgust	fear	joy	neutral	sad	surprise
anger	518	10	13	10	12	26	6
disgust	9	526	2	0	13	8	9
fear	7	3	560	0	0	2	49
joy	6	3	0	480	54	38	23
neutral	9	19	2	39	418	64	7
sad	52	23	2	29	34	443	8
surprise	21	2	23	30	13	8	414

Per‑Language Accuracy

Language	Accuracy	Samples
English	84.5%	851
Roman Urdu	80.8%	506
Urdu	84.0%	681

muhammadsuleman1533/xlm-roberta-multilingual-emotion-classifier

xlm-roberta-multilingual-emotion-classifier

Model Description