arif481/crosslingual-sentiment-model

A cross-lingual sentiment analysis model fine-tuned on XLM-RoBERTa for binary sentiment classification (positive/negative) across en, bn.

Model Description

This model performs sentiment classification across multiple languages using transfer learning. It was trained using the combined strategy.

Supported Languages

• English (en)
• Bengali (bn)

Training Mode: combined

Trained on combined English and Bengali data for multilingual learning.

Usage

from transformers import pipeline

classifier = pipeline("sentiment-analysis", model="arif481/crosslingual-sentiment-model")

# English
result = classifier("This movie is absolutely fantastic!")
print(result)  # [{'label': 'positive', 'score': 0.99}]

# Bengali
result = classifier("এই সিনেমাটি অসাধারণ ছিল!")
print(result)  # [{'label': 'positive', 'score': 0.95}]

Training

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("arif481/crosslingual-sentiment-model")
tokenizer = AutoTokenizer.from_pretrained("arif481/crosslingual-sentiment-model")

Metrics

Metric	Value
Accuracy	N/A
Macro F1	N/A
Precision	N/A
Recall	N/A

Limitations

• Binary classification only (positive/negative)
• May not perform well on neutral sentiment
• Bengali performance may be lower than English due to limited training data

Citation

If you use this model, please cite:

@misc{crosslingual-sentiment,
  author = {Cross-Lingual Sentiment Team},
  title = {Cross-Lingual Sentiment Analysis Model},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/arif481/crosslingual-sentiment-model}
}

License

This model is released under the MIT License.