emilyyy04/burmese-sentiment-xlm-roberta

Burmese Sentiment Analysis with XLM-RoBERTa

Model Details

Model Description

This model is a fine-tuned version of FacebookAI/xlm-roberta-base for Burmese sentiment analysis.
It classifies Burmese text into one of three sentiment categories:

• Positive
• Negative
• Neutral

The model was trained using publicly available Burmese sentiment datasets and additional manually curated data, with careful preprocessing to normalize encoding (Zawgyi → Unicode conversion).

• Developer: Yoon Thiri Aung (GitHub)
• Model type: Transformer-based multilingual masked language model fine-tuned for text classification
• Languages: Burmese (my), with multilingual base model support
• License: MIT
• Finetuned from: FacebookAI/xlm-roberta-base
• Demo: https://huggingface.co/spaces/emilyyy04/burmese-sentiment-analysis-demo

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Uses

Direct Use

• Sentiment classification of Burmese text from social media, reviews, comments, and other user-generated content.
• Building sentiment-aware Burmese NLP applications such as chatbots, analytics dashboards, and content moderation tools.

Limitations

• May not generalize well to domains significantly different from the training data.
• May misclassify sentences with mixed sentiments or sarcasm.
• Performance may drop for code-mixed Burmese-English text with heavy slang or informal spelling.

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Training Details

Training Data

• Sources:

  • kalixlouiis/burmese-sentiment-analysis
  • chuuhtetnaing/myanmar-social-media-sentiment-analysis-dataset
  • Additional curated data collected and annotated by the author.

• Preprocessing:

  • Converted Zawgyi-encoded text to Unicode.
  • Cleaned and normalized text fields.
  • Tokenized using the XLM-RoBERTa tokenizer with:
    • max_length=128
    • Truncation and padding to maximum length.

Training Procedure

• Optimizer: AdamW (default in Hugging Face Trainer)
• Learning rate: 2e-5
• Batch size: 8 (train & eval)
• Epochs: 3
• Weight decay: 0.01
• Mixed precision (fp16): Enabled when training on GPU
• Metric for best model: F1 score (weighted average)
• Evaluation strategy: Per epoch
• Model selection: Best F1 score checkpoint

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Evaluation

Metrics

The model was evaluated on a held-out validation set using accuracy, precision, recall, and F1 score.

Epoch	Val Loss	Accuracy	Precision	Recall	F1
1	0.6171	0.7859	0.7994	0.7859	0.7875
2	0.4268	0.8470	0.8465	0.8470	0.8464
3	0.4115	0.8451	0.8447	0.8451	0.8448

The final model used is the checkpoint with the highest F1 score.

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How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "emilyyy04/burmese-sentiment-xlm-roberta"  # Replace with actual repo name
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "ဒီဇာတ်လမ်းက တကယ်ကောင်းတယ်။"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)
predicted_class = torch.argmax(outputs.logits, dim=1).item()

label_map = {0: "positive", 1: "negative", 2: "neutral"}
print("Predicted Sentiment:", label_map[predicted_class])