ahs95/banglabert-sentiment-analysis

BanglaBERT Fine-tuned for Bangla Sentiment Analysis

Model Description

This model is a fine-tuned version of csebuetnlp/banglabert on the SentiGOLD dataset for 5-class sentiment analysis in Bengali. It classifies text into:

1. 😠 Very Negative (SN)
2. 😞 Negative (WN)
3. 😐 Neutral (N)
4. 😊 Positive (WP)
5. 😍 Very Positive (SP)

Key Features:

• State-of-the-art Bangla language understanding
• Handles both formal and informal Bengali text
• Optimized for social media, reviews, and customer feedback
• Requires text normalization using Bangla Normalizer

Intended Uses & Limitations

Primary Use

• Sentiment analysis of Bengali text
• Social media monitoring
• Customer feedback analysis
• Product review classification

Limitations

• Performance may degrade on code-mixed text (Bengali-English)
• May struggle with sarcasm and highly contextual expressions
• Best for short to medium-length texts (up to 512 tokens)

Training Data

The model was fine-tuned on SentiGOLD, the largest gold-standard Bangla sentiment analysis dataset:

Feature	Value
Total Samples	70,000
Domains Covered	30+
Source Diversity	Social media, news, blogs, reviews
Class Distribution	Balanced across 5 classes
Annotation Quality	Fleiss' kappa = 0.88

Training Procedure

Hyperparameters

Parameter	Value
Learning Rate	2e-5 → 1.05e-6
Batch Size	48
Epochs	5
Optimizer	AdamW
Scheduler	ReduceLROnPlateau
Weight Decay	0.01
Gradient Accumulation	4 steps
Warmup Ratio	5%

Techniques

• Class-weighted loss handling imbalance
• Early stopping (patience=3)
• Mixed precision (FP16) training
• Gradient checkpointing
• Text normalization using Bangla Normalizer

Evaluation Results

Validation Performance

Epoch	F1 (Macro)	Accuracy	Very Neg F1	Neg F1	Neu F1	Pos F1	Very Pos F1
1	0.6334	0.6331	0.6789	0.5834	0.6407	0.5635	0.7004
5	0.6537	0.6551	0.7081	0.6157	0.6421	0.5789	0.7236

Final Test Performance

Metric	Score
Macro F1	0.6660
Accuracy	0.6671

How to Use

Direct Inference

from transformers import pipeline
from normalizer import normalize

# Load model
classifier = pipeline(
    "text-classification", 
    model="ahs95/banglabert-sentiment-analysis",
    tokenizer="ahs95/banglabert-sentiment-analysis"
)

# Prepare text
text = "আপনার পণ্যটি অসাধারণ! আমি খুবই সন্তুষ্ট।"
normalized_text = normalize(text)  # Important for BanglaBERT

# Classify
result = classifier(normalized_text)
print(f"Sentiment: {result[0]['label']} (Confidence: {result[0]['score']:.2f})")

Advanced Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
from normalizer import normalize

# Load model and tokenizer
model_name = "ahs95/banglabert-sentiment-analysis"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Prepare inputs
texts = [
    "সেবা খুব খারাপ ছিল। আমি কখনো ফিরে আসব না।",
    "পণ্যটির গুণগত মান মোটামুটি ভাল"
]
normalized_texts = [normalize(t) for t in texts]

# Tokenize and predict
inputs = tokenizer(normalized_texts, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)
    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)

# Get predictions
sentiment_labels = ["Very Negative", "Negative", "Neutral", "Positive", "Very Positive"]
predictions = [sentiment_labels[p] for p in probabilities.argmax(dim=1)]

for text, pred in zip(texts, predictions):
    print(f"Text: {text}\nPredicted Sentiment: {pred}\n")

Ethical Considerations

• Bias: While SentiGOLD reduces bias through synthetic data, real-world validation is recommended

• Use Cases: Suitable for:

  • Product feedback analysis
  • Social media monitoring
  • Market research
  • Avoid: Critical decision systems without human oversight

Citation

If you use this model, please cite:

@misc{banglabert-sentiment,
  author = {Arshadul Hoque},
  title = {Fine-tuned BanglaBERT for Bengali Sentiment Analysis},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/ahs95/banglabert-sentiment-analysis}}
}

Contact

For questions and support: ahsbd95@gmail.com