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--- |
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language: |
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- en |
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- es |
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- fr |
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- de |
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- zh |
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license: apache-2.0 |
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tags: |
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- sentiment-analysis |
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- xlm-roberta |
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- multilingual |
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metrics: |
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- accuracy |
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- f1 |
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--- |
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# multi_lingual_sentiment_analyzer |
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## Overview |
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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. |
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## Model Architecture |
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The model is based on **XLMRobertaForSequenceClassification**, a transformer-based encoder model. |
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- **Backbone**: XLM-R (Base) |
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- **Parameters**: ~270M |
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- **Training Objective**: Cross-Entropy Loss with Label Smoothing |
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- **Input Processing**: SentencePiece tokenization with a shared multilingual vocabulary. |
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The classification head consists of a linear layer applied to the representation of the `<s>` (start-of-sentence) token, formulated as: |
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$$y = \text{Softmax}(W \cdot h_{<s>} + b)$$ |
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## Intended Use |
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- **Global Brand Monitoring**: Analyzing customer feedback across multiple regions in real-time. |
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- **Social Media Analytics**: Tracking public sentiment trends on global platforms. |
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- **Support Ticket Triage**: Automatically routing urgent negative feedback to specialized teams. |
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## Limitations |
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- **Sarcasm Detection**: Like many transformer models, it may struggle with highly nuanced or culturally specific sarcasm. |
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- **Context Length**: The maximum sequence length is limited to 512 tokens. |
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- **Low-Resource Languages**: While multilingual, performance may be lower for languages with minimal training data in the original XLM-R corpus. |
