---
language: 
- en
- es
- fr
- de
- zh
license: apache-2.0
tags:
- sentiment-analysis
- xlm-roberta
- multilingual
metrics:
- accuracy
- f1
---

# multi_lingual_sentiment_analyzer

## Overview
This model is a high-performance multilingual sentiment classifier fine-tuned on the XLM-RoBERTa architecture. It is designed to detect emotional polarity in text across 100+ languages, categorizing inputs into **Negative**, **Neutral**, or **Positive** sentiments. It is particularly robust against code-switching and informal linguistic structures common in social media data.



## Model Architecture
The model is based on **XLMRobertaForSequenceClassification**, a transformer-based encoder model. 
- **Backbone**: XLM-R (Base)
- **Parameters**: ~270M
- **Training Objective**: Cross-Entropy Loss with Label Smoothing
- **Input Processing**: SentencePiece tokenization with a shared multilingual vocabulary.

The classification head consists of a linear layer applied to the representation of the `<s>` (start-of-sentence) token, formulated as:
$$y = \text{Softmax}(W \cdot h_{<s>} + b)$$

## Intended Use
- **Global Brand Monitoring**: Analyzing customer feedback across multiple regions in real-time.
- **Social Media Analytics**: Tracking public sentiment trends on global platforms.
- **Support Ticket Triage**: Automatically routing urgent negative feedback to specialized teams.

## Limitations
- **Sarcasm Detection**: Like many transformer models, it may struggle with highly nuanced or culturally specific sarcasm.
- **Context Length**: The maximum sequence length is limited to 512 tokens.
- **Low-Resource Languages**: While multilingual, performance may be lower for languages with minimal training data in the original XLM-R corpus.