Update README.md

README.md

@@ -1,3 +1,62 @@
----
-license: mit
----
+---
+language:
+- it
+- en
+license: mit
+library_name: transformers
+tags:
+- text-classification
+- safety
+- toxicity
+- insults
+- xlm-roberta
+- nlp
+base_model: xlm-roberta-base
+pipeline_tag: text-classification
+---
+
+# XLM-RoBERTa Safety Classifier (Italian & English)
+
+## Model Description
+
+This is an **XLM-RoBERTa-based** binary text classification model fine-tuned to detect **toxicity and insults** in user queries. It is trained on a bilingual dataset (Italian and English) to distinguish between **SAFE** (benign) and **UNSAFE** (toxic/harmful) inputs.
+
+- **Model Type:** XLM-RoBERTa (Fine-tuned)
+- **Languages:** Italian (`it`), English (`en`)
+- **Task:** Binary Classification
+- **Training Dataset Size:** 9,035 samples
+- **Created by:** [Famezz](https://huggingface.co/Famezz)
+
+## Intended Use
+
+This model is designed to act as a **guardrail** for Chatbots and LLMs. It can be used to:
+1.  Filter out toxic user inputs before they reach a Large Language Model.
+2.  Flag offensive content in user-generated text.
+
+## Label Mapping
+
+The model is trained to predict the following string labels directly:
+
+| Label | Description |
+| :--- | :--- |
+| **SAFE** | Benign queries, general knowledge, small talk. |
+| **UNSAFE** | Toxic content, insults, offensive language. |
+
+## Usage
+
+You can use this model directly with the Hugging Face `pipeline`. The pipeline will automatically output the labels "SAFE" or "UNSAFE".
+
+```python
+from transformers import pipeline
+
+# Load the classifier
+# Make sure the repo name matches exactly what you created on HF
+classifier = pipeline("text-classification", model="Famezz/roberta_safety_classifier")
+
+# Test with English
+print(classifier("How do I bake a cake?"))
+# Output: [{'label': 'SAFE', 'score': 0.99}]
+
+# Test with Italian
+print(classifier("Sei un idiota"))
+# Output: [{'label': 'UNSAFE', 'score': 0.98}]