Nepal Border Sentiment Classifier
XLM-RoBERTa-base fine-tuned for 3-class sentiment classification (positive/neutral/negative) on Nepali and English YouTube comments discussing the Nepal–India border dispute.
Performance
- Accuracy: ~50% on gold-standard evaluation set (manually verified)
- Random baseline: 33% (3-class)
- F1 (weighted): ~0.50
Dataset
Trained on the Nepal Border Sentiment Dataset — ~2100 YouTube comments scraped from 17 Nepali news and commentary channels.
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained("unicorn-s/nepal-border-sentiment-xlmr")
tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
text = "Your Nepali or English comment here"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.softmax(outputs.logits, dim=-1)
labels = ['negative', 'neutral', 'positive']
print({labels[i]: float(probs[0][i]) for i in range(3)})
Limitations
- Small training set (~1800 silver-labeled samples)
- Single annotator for gold-standard evaluation
- Translation quality varies for Nepali slang and code-switched text
- Class imbalance in training data skewed toward negative sentiment
Citation / Contribution
This is published as a baseline for low-resource Nepali political sentiment analysis. Contributions of additional labeled data and improved baselines are welcome — see the GitHub repo.