trilingual-sentiment-lora

Trilingual Sentiment LoRA Model

Model Summary

Trilingual Sentiment LoRA is a fine-tuned XLM-RoBERTa model optimized for multilingual sentiment classification across Arabic, English, and French texts.
It applies Low-Rank Adaptation (LoRA) using the PEFT framework, enabling efficient fine-tuning while maintaining robust multilingual understanding.

Model Details

• Developed by: Osama Naguib
• Funded by: Ahmed Zaky
• Model Type: Sequence Classification
• Languages: Arabic, English, French
• License: MIT
• Finetuned From: xlm-roberta-base
• Frameworks: Transformers, Datasets, PEFT, PyTorch

Model Sources

• Repository: https://huggingface.co/osamanaguib/trilingual-sentiment-lora
• Base Model: https://huggingface.co/xlm-roberta-base

Intended Uses

Direct Use

This model can be used directly for sentiment classification on multilingual (EN/FR/AR) text data.

Example labels:

• 0 → Negative
• 1 → Neutral
• 2 → Positive

Example use: library_name: transformers tags: [sentiment-analysis, multilingual, lora, xlm-roberta, peft]

Trilingual Sentiment LoRA Model

Model Summary

Trilingual Sentiment LoRA is a fine-tuned XLM-RoBERTa model optimized for multilingual sentiment classification across Arabic, English, and French texts.
It applies Low-Rank Adaptation (LoRA) using the PEFT framework, enabling efficient fine-tuning while maintaining robust multilingual understanding.

Model Details

• Developed by: Osama Naguib
• Funded by: Ahmed Zaky
• Model Type: Sequence Classification
• Languages: Arabic, English, French
• License: MIT
• Finetuned From: xlm-roberta-base
• Frameworks: Transformers, Datasets, PEFT, PyTorch

Model Sources

• Repository: https://huggingface.co/osamanaguib/trilingual-sentiment-lora
• Base Model: https://huggingface.co/xlm-roberta-base

Intended Uses

Direct Use

This model can be used directly for sentiment classification on multilingual (EN/FR/AR) text data.

Example labels:

• 0 → Negative
• 1 → Neutral
• 2 → Positive