XLMRoBERTa_Multilingual_Sentiment_Analysis

XLM-RoBERTa-Base Multilingual Model for Sentiment Analysis on Amazon Reviews

This repository contains a multilingual sentiment analysis model fine-tuned on the Amazon Reviews Multi dataset using the xlm-roberta-base architecture from Hugging Face Transformers. The model is capable of analyzing product review sentiment in multiple languages and is suitable for real-world multilingual applications.

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Model Details

• Model Architecture: XLM-RoBERTa Base
• Task: Sentiment Classification (Binary: Positive / Negative)
• Dataset: Amazon Reviews Multi (en subset used for fine-tuning)
• Languages Supported: Trained on English, generalizes to multilingual due to XLM-R architecture
• Fine-tuning Framework: Hugging Face Transformers

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Usage

Installation

pip install transformers torch

Loading and Testing the Model

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_path = "your-username/xlm-roberta-sentiment-amazon-reviews"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
model.eval()

# Prediction function
def predict_sentiment(texts):
    inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt").to(model.device)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.softmax(outputs.logits, dim=1)
        preds = torch.argmax(probs, dim=1)
    
    label_map = {0: "Negative", 1: "Positive"}
    results = []
    for text, pred, prob in zip(texts, preds, probs):
        results.append({
            "text": text,
            "prediction": label_map[pred.item()],
            "confidence": round(prob[pred].item(), 4)
        })
    return results

# Example
examples = ["This product is amazing!", "Worst purchase ever."]
print(predict_sentiment(examples))

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Performance Metrics

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Fine-Tuning Details

Dataset

• Source: Amazon Reviews Multi
• Labels: Originally 5 classes; remapped to binary sentiment (0 = Negative [1–2 stars], 1 = Positive [4–5 stars])
• Neutral (3 stars) were excluded from training

Training Configuration

• Epochs: 3
• Batch size: 16
• Learning rate: 2e-5
• Optimizer: AdamW
• Evaluation strategy: Epoch-based

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Repository Structure

.
├── model/                # Fine-tuned model and config files
├── tokenizer/            # Tokenizer files
├── inference.py          # Inference and testing script
├── README.md             # Model documentation

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Limitations

• Trained only on the English subset of Amazon Reviews Multi; multilingual performance may vary.
• Neutral reviews (3-star) are excluded, so the model may not detect nuanced sentiment.
• Fine-tuning was not domain-specific, so performance may degrade in highly specialized review categories.

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Contributing

Contributions are welcome! Feel free to open an issue or pull request for improvements or bug fixes.