Update README.md

README.md

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----
-license: apache-2.0
----
+---
+language: es
+license: mit
+library_name: transformers
+tags:
+- spam-detection
+- sms
+- text-classification
+- beto
+- bert
+- spanish
+- pytorch
+datasets:
+- sms_spam
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+base_model: dccuchile/bert-base-spanish-wwm-cased
+pipeline_tag: text-classification
+widget:
+- text: "¡FELICIDADES! Ganaste un premio de $1000. Haz clic aquí para reclamarlo"
+  example_title: "Spam - Premio falso"
+- text: "¡Increíble! Ha ganado un viaje con todos los gastos pagados a Cancún. Llame al 1-800-VIAJES"
+  example_title: "Spam - Oferta fraudulenta"
+- text: "URGENTE: Su cuenta ha sido suspendida. Haga clic aquí para reactivarla"
+  example_title: "Spam - Phishing bancario"
+- text: "Hola mamá, llegaré tarde a casa. Nos vemos en la cena"
+  example_title: "Legítimo - Mensaje familiar"
+- text: "Buenos días, confirmo la reunión de mañana a las 3pm"
+  example_title: "Legítimo - Mensaje de trabajo"
+model-index:
+- name: spamvision-beto
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Spanish SMS Spam Detection
+      type: sms_spam
+    metrics:
+    - type: accuracy
+      value: 0.962
+      name: Accuracy
+    - type: f1
+      value: 0.951
+      name: F1 Score
+    - type: precision
+      value: 0.948
+      name: Precision
+    - type: recall
+      value: 0.955
+      name: Recall
+---
+
+# 🛡️ SpamVision BETO - Spanish SMS Spam Detector
+
+<div align="center">
+  <img src="https://img.shields.io/badge/Language-Spanish-green" alt="Spanish">
+  <img src="https://img.shields.io/badge/Accuracy-96.2%25-blue" alt="Accuracy">
+  <img src="https://img.shields.io/badge/F1--Score-95.1%25-orange" alt="F1">
+  <img src="https://img.shields.io/badge/License-MIT-yellow" alt="License">
+</div>
+
+## 📖 Model Description
+
+**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.
+
+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.
+
+### Key Features
+
+- 🎯 **High Accuracy**: 96.2% on test dataset
+- ⚡ **Fast Inference**: < 200ms per message
+- 🇪🇸 **Spanish-optimized**: Fine-tuned on Spanish SMS data
+- 📱 **SMS-focused**: Optimized for short messages (< 160 characters)
+- 🔄 **Production-ready**: Used in real-world mobile app
+
+### Model Architecture
+
+- **Base Model**: `dccuchile/bert-base-spanish-wwm-cased`
+- **Parameters**: ~110M
+- **Layers**: 12 transformer encoder layers
+- **Hidden Size**: 768
+- **Max Sequence Length**: 128 tokens
+- **Vocabulary Size**: 31,002 tokens
+
+---
+
+## 🚀 Quick Start
+
+### Installation
+