Update README.md

README.md

@@ -1,3 +1,71 @@
 ---
+language:
+- en
+- es
+pipeline_tag: text-classification
+tags:
+- roberta
+- bpo
+- customer-support
+- it-service-management
+- text-classification
 license: mit
 ---
+
+# SITOR - Intelligent Triage and Routing Engine (RoBERTa)
+
+## Model Description
+
+**SITOR** (Sistema Inteligente de Tipificación, Orquestación y Resolución de Incidencias) is a fine-tuned `roberta-base` (125M) model designed for automated BPO (Business Process Outsourcing) and IT Helpdesk ticket routing. 
+
+Unlike standard classification models that attempt to predict blindly, SITOR is calibrated to act as an algorithmic firewall. It predicts over **56 highly overlapping operational queues** but is designed to only automate routing when the mathematical certainty (Softmax output calibrated via L-BFGS) exceeds a rigorous **0.85 threshold**.
+
+- **Developed by:** Pedro Andreu Torres (Master in Data Science & AI Thesis)
+- **Model Type:** Transformer-based sequence classifier
+- **Language(s):** English (Trained on English telecom support corpus)
+- **Base Model:** `roberta-base`
+
+## Intended Use & Business Logic
+
+This model is intended for enterprise-level ticket orchestration. 
+
+Due to the asymmetric cost of misrouting a ticket in a Tier-1 BPO environment, the model does not operate on a simple `argmax` decision. 
+* **High Confidence (>= 0.85):** The ticket is strictly automated and bypassed to the designated technical queue.
+* **Low Confidence (< 0.85):** The model acknowledges ambiguity and flags the ticket for manual human review.
+
+## Training Data
+To ensure corporate confidentiality and academic rigor, the model was trained on a public Telecommunications Customer Support dataset sourced from Kaggle. The raw data underwent severe deduplication and asymmetric mapping to simulate a complex 56-queue BPO ecosystem.
+
+## Evaluation Results
+
+Validation was performed using a strict `StratifiedGroupKFold` strategy to prevent Data Leakage on a blind hold-out set of 3,080 tickets.
+
+* **Base Algorithmic Accuracy:** ~59% (Top-1 Accuracy on 56 highly overlapping classes).
+* **Automated Volume (0.85 Threshold):** 38.5% of the total influx.
+* **Operational Precision (AI):** On the automated volume, the model achieves **97.3% precision** (only 2.7% residual error).
+* **In-Vitro ROI:** The hybrid approach yields a theoretical **31.8% reduction in global operational errors** compared to a 100% human-operated baseline. *(Note: Requires Shadow Mode validation in a live production environment).*
+
+## How to use
+
+You can load this model and its tokenizer directly using the `transformers` library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load the model and tokenizer from the Hugging Face Hub
+tokenizer = AutoTokenizer.from_pretrained("Andregon79/sitor")
+model = AutoModelForSequenceClassification.from_pretrained("Andregon79/sitor")
+
+# Example ticket
+ticket_text = "My home wifi keeps dropping every 10 minutes since yesterday."
+
+inputs = tokenizer(ticket_text, return_tensors="pt", truncation=True, max_length=128)
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+# Apply softmax to get confidence scores
+probabilities = torch.softmax(logits, dim=1)
+confidence, predicted_class = torch.max(probabilities, dim=1)
+
+print(f"Confidence: {confidence.item():.4f}")