Create README.md

README.md

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+---
+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.