This model was fine-tuned using the Amazon Reviews Multilingual Dataset, specifically on the English, German, and Italian subsets.
Training was done using PyTorch and Hugging Face Transformers.

Preprocessing

Texts were tokenized using XLMRobertaTokenizer
Labels were mapped to integers (negative: 0, neutral: 1, positive: 2)
Dataset was split into train/test/validation using an 80/10/10 ratio

Training

Model: xlm-roberta-base
Epochs: 2
Optimizer: AdamW
Batch size: 8
Evaluation metric: Macro F1-score
Hardware: Google Colab GPU

🔍 Example Usage

python

from transformers import pipeline

classifier = pipeline("sentiment-analysis", model="subba5076/multilingual-sentiment-xlm-roberta")

classifier("Der Film war unglaublich schön.")  # German
classifier("This phone is terrible.")           # English
classifier("È stato un buon acquisto.")         # Italian

📊 Evaluation Macro F1-score on test set: 0.81 Confusion matrix and training curves can be shared in future updates.

👨‍💻 Authors This project was developed as part of a team NLP assignment.

Team Members:

Subrahmanya Rajesh Nayak @subba5076

Rim Tafech

🪪 License This model is licensed under the MIT License.