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--- |
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language: es |
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license: mit |
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library_name: transformers |
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tags: |
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- spam-detection |
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- sms |
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- text-classification |
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- beto |
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- bert |
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- spanish |
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- pytorch |
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datasets: |
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- sms_spam |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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base_model: dccuchile/bert-base-spanish-wwm-cased |
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pipeline_tag: text-classification |
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widget: |
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- text: "¡FELICIDADES! Ganaste un premio de $1000. Haz clic aquí para reclamarlo" |
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example_title: "Spam - Premio falso" |
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- text: "¡Increíble! Ha ganado un viaje con todos los gastos pagados a Cancún. Llame al 1-800-VIAJES" |
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example_title: "Spam - Oferta fraudulenta" |
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- text: "URGENTE: Su cuenta ha sido suspendida. Haga clic aquí para reactivarla" |
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example_title: "Spam - Phishing bancario" |
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- text: "Hola mamá, llegaré tarde a casa. Nos vemos en la cena" |
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example_title: "Legítimo - Mensaje familiar" |
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- text: "Buenos días, confirmo la reunión de mañana a las 3pm" |
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example_title: "Legítimo - Mensaje de trabajo" |
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model-index: |
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- name: spamvision-beto |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Spanish SMS Spam Detection |
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type: sms_spam |
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metrics: |
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- type: accuracy |
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value: 0.962 |
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name: Accuracy |
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- type: f1 |
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value: 0.951 |
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name: F1 Score |
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- type: precision |
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value: 0.948 |
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name: Precision |
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- type: recall |
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value: 0.955 |
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name: Recall |
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--- |
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# 🛡️ SpamVision BETO - Spanish SMS Spam Detector |
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<div align="center"> |
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<img src="https://img.shields.io/badge/Language-Spanish-green" alt="Spanish"> |
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<img src="https://img.shields.io/badge/Accuracy-96.2%25-blue" alt="Accuracy"> |
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<img src="https://img.shields.io/badge/F1--Score-95.1%25-orange" alt="F1"> |
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<img src="https://img.shields.io/badge/License-MIT-yellow" alt="License"> |
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</div> |
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## 📖 Model Description |
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**SpamVision BETO** is a fine-tuned BERT model for Spanish language specifically designed to detect spam SMS messages with high accuracy. Built on top of the [BETO](https://github.com/dccuchile/beto) (BERT trained on Spanish corpus), this model achieves **96.2% accuracy** in distinguishing between legitimate messages and spam. |
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This model is part of the [SpamVision project](https://github.com/tu-usuario/spamvision-api), a hybrid AI system that combines rule-based filtering (AFD) with deep learning for maximum spam detection performance. |
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### Key Features |
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- 🎯 **High Accuracy**: 96.2% on test dataset |
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- ⚡ **Fast Inference**: < 200ms per message |
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- 🇪🇸 **Spanish-optimized**: Fine-tuned on Spanish SMS data |
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- 📱 **SMS-focused**: Optimized for short messages (< 160 characters) |
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- 🔄 **Production-ready**: Used in real-world mobile app |
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### Model Architecture |
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- **Base Model**: `dccuchile/bert-base-spanish-wwm-cased` |
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- **Parameters**: ~110M |
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- **Layers**: 12 transformer encoder layers |
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- **Hidden Size**: 768 |
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- **Max Sequence Length**: 128 tokens |
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- **Vocabulary Size**: 31,002 tokens |
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--- |
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## 🚀 Quick Start |
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### Installation |
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