Swarnadeep-28/bengali-code-mix-sentiment

Bengali-English Code-Mixed Sentiment Model

Model Summary

This model is a fine-tuned version of xlm-roberta-base for sentiment analysis on Bengali–English code-mixed text (social media posts, comments, and tweets).

• Task: Text Classification (Sentiment Analysis)
• Languages: Bengali (Romanized) + English
• Classes: 0, 1, 2, 3
• Fine-tuning method: Full fine-tuning
• Dataset: Bengali-English Code-Mixed Sentiment Dataset

This model provides strong baseline performance for code-mixed sentiment classification and can be directly applied to social media analysis and low-resource NLP research.

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How to Use

Inference Example

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "Swarnadeep-28/bengali-code-mix-sentiment"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

text = "Aaj match ta khub bhalo chilo! Loved it."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)

with torch.no_grad():
    logits = model(**inputs).logits
pred = torch.argmax(logits, dim=-1).item()
labels = ["0", "1", "2", "3"]
print("Predicted label:", labels[pred])

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Training Details

• Base model: xlm-roberta-base
• Method: Full fine-tuning (all parameters updated)
• Optimizer: AdamW
• Learning Rate: 2e-5
• Epochs: 3
• Batch Size: 16 (train), 32 (eval)
• Hardware: Trained on a single GPU (Colab T4 / equivalent)

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Evaluation

Classification Report

Label	Precision	Recall	F1-Score	Support
0	0.80	0.73	0.77	528
1	0.73	0.73	0.73	617
2	0.69	0.76	0.72	675
3	0.67	0.57	0.62	182

Overall Metrics

• Accuracy: 0.73
• Macro Avg: Precision = 0.72, Recall = 0.70, F1 = 0.71
• Weighted Avg: Precision = 0.73, Recall = 0.73, F1 = 0.73
• Total Samples: 2002

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Applications

• Sentiment classification of Bengali-English social media text
• Research in code-mixed NLP for Indic languages
• Benchmark for parameter-efficient fine-tuning (compare with LoRA model)

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Limitations

• Heavily Romanized or slang-heavy Bengali may reduce accuracy
• Trained primarily on short-form text (tweets, comments, reviews)
• Not designed for abusive/toxic content moderation or safety-critical use cases

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Ethical Considerations

• Data reflects natural biases from social media sources
• Misclassifications may occur in sarcasm or offensive text
• Should not be the sole basis for critical decision-making

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Citation

If you use this model, please cite:

@model{das2025_bn_code_mix_sentiment,
  author    = {Swarnadeep Das},
  title     = {Bengali-English Code-Mixed Sentiment Model},
  year      = {2025},
  url       = {https://huggingface.co/Swarnadeep-28/bengali-code-mix-sentiment}
}

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Acknowledgements

• Dataset: Bengali-English Code-Mixed Sentiment Dataset
• Base model: xlm-roberta-base
• Frameworks: Transformers, Datasets