Fixaro/myanmar-absa-aspect-detection

Myanmar ABSA - Aspect Detection Model (Stage 1)

A multi-label classification model for detecting aspects in Burmese product/service reviews. This is Stage 1 of a two-stage Aspect-Based Sentiment Analysis (ABSA) pipeline.

Model Details

• Base Model: xlm-roberta-base
• Architecture: XLM-RoBERTa for Sequence Classification
• Problem Type: Multi-label classification
• Language: Burmese (Myanmar)
• License: MIT

Aspects Detected

The model predicts the presence of up to 5 aspects in a given review:

1. product_quality - Quality, functionality, and effectiveness of products/services
2. fulfillment_and_speed - Delivery speed, order fulfillment, and logistics
3. price_and_value - Pricing, value for money, and cost-related aspects
4. staff_and_service - Customer service, staff behavior, and support interactions
5. variety_and_availability - Product variety, stock availability, and selection

Training Data

• Total Samples: 5,281 Burmese reviews
• Data Split: Stratified train/val/test split
  • Train: 4,224 samples
  • Validation: 528 samples
  • Test: 529 samples
• Label Distribution: Multi-label annotations (reviews can have multiple aspects)
• Taxonomy: Updated 5-aspect taxonomy (removed digital_experience, renamed aspects for clarity)

Performance Metrics

Evaluated on the test set:

Metric	Score
Macro F1	0.9314
Micro F1	0.9279

Training Configuration

• Learning Rate: 2e-5
• Batch Size: 8 (with gradient accumulation steps = 4, effective batch size = 32)
• Epochs: 4
• Max Sequence Length: 128 tokens
• Optimizer: AdamW
• Precision: bfloat16

Usage

Basic Usage with Pipeline

from transformers import pipeline

# Load the model
classifier = pipeline(
    "text-classification",
    model="Fixaro/myanmar-absa-aspect-detection",
    return_all_scores=True,
    function_to_apply="sigmoid"  # For multi-label classification
)

# Predict aspects
review = "ပစ္စည်းအရည်အသွေးက ကောင်းပြီး ပို့တာလည်း မြန်တယ်"
results = classifier(review)

# Results will show scores for all 5 aspects
for result in results[0]:
    if result['score'] > 0.5:  # Threshold for multi-label
        print(f"{result['label']}: {result['score']:.4f}")

Advanced Usage with Tokenizer

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "Fixaro/myanmar-absa-aspect-detection"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Prepare input
review = "ဈေးလည်း သက်သာတယ်၊ ဝန်ထမ်းတွေကလည်း ဖော်ရွေတယ်"
inputs = tokenizer(
    review,
    return_tensors="pt",
    truncation=True,
    max_length=128,
    padding=True
)

# Get predictions
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    probabilities = torch.sigmoid(logits)
    
# Apply threshold (0.5)
threshold = 0.5
predictions = (probabilities > threshold).int()

# Map to aspect names
aspect_names = [
    "product_quality",
    "fulfillment_and_speed", 
    "price_and_value",
    "staff_and_service",
    "variety_and_availability"
]

detected_aspects = [
    aspect_names[i] 
    for i, pred in enumerate(predictions[0]) 
    if pred == 1
]

print(f"Detected aspects: {detected_aspects}")

Model Architecture

XLM-RoBERTa Base (xlm-roberta-base)
├── Encoder: 12 transformer layers
├── Hidden size: 768
├── Attention heads: 12
└── Classification head: Linear(768, 5) with sigmoid activation

Limitations and Biases

• Domain Specificity: Trained on product/service reviews; may not generalize to other domains
• Language Limitation: Optimized for Burmese; performance may vary for code-mixed text
• Aspect Overlap: Some reviews may express multiple aspects ambiguously
• Threshold Sensitivity: The 0.5 threshold for multi-label prediction may need adjustment based on use case

Intended Use

This model is intended for:

• Aspect detection in Burmese customer reviews
• First stage of ABSA pipelines
• Research and academic purposes
• Commercial applications with proper validation

Citation

If you use this model in your research, please cite:

@software{myanmar_absa_aspect_detection,
  title = {Myanmar ABSA: Aspect Detection Model},
  author = {Fixaro},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/Fixaro/myanmar-absa-aspect-detection}
}

Related Models

• Stage 2 Model: Fixaro/myanmar-absa-sentiment-classification - Aspect-level sentiment classification

Contact

For questions or issues, please open an issue on the model repository.