Update README.md

README.md

@@ -5,18 +5,37 @@ tags:
 - information-retrieval
 - semantic-search
 widget:
-- source_sentence: "Descrivi dettagliatamente il processo chimico e fisico che avviene durante la preparazione di un impasto per crostata"
+- source_sentence: >-
+    Descrivi dettagliatamente il processo chimico e fisico che avviene durante
+    la preparazione di un impasto per crostata
   sentences:
-  - "## La Magia Chimica e Fisica nell'Impasto della Crostata: Un Viaggio Dagli Ingredienti Secchi al Trionfo del Forno\n\nLa preparazione di una crostata, apparentemente un gesto semplice e familiare, cela in realtà un affascinante balletto di reazioni chimiche e trasformazioni fisiche..."
-  - "## L'Arte Effimera: Creare un Dolce Paesaggio Invernale\n\nImmergiamoci nel cuore pulsante della pasticceria festiva, dove l'arte culinaria si fonde con la creatività artistica..."
-  - "Le piattaforme di comunicazione digitale, con la loro ubiquità crescente, si configurano come un'arma a doppio taglio nel panorama sociale contemporaneo..."
+  - >-
+    ## La Magia Chimica e Fisica nell'Impasto della Crostata: Un Viaggio Dagli
+    Ingredienti Secchi al Trionfo del Forno
+
+
+    La preparazione di una crostata, apparentemente un gesto semplice e
+    familiare, cela in realtà un affascinante balletto di reazioni chimiche e
+    trasformazioni fisiche...
+  - >-
+    ## L'Arte Effimera: Creare un Dolce Paesaggio Invernale
+
+
+    Immergiamoci nel cuore pulsante della pasticceria festiva, dove l'arte
+    culinaria si fonde con la creatività artistica...
+  - >-
+    Le piattaforme di comunicazione digitale, con la loro ubiquità crescente, si
+    configurano come un'arma a doppio taglio nel panorama sociale
+    contemporaneo...
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
+language:
+- it
 ---
 
-# Fine-tuned Qwen3-Embedding for Italian-English Cross-Lingual Semantic Retrieval
+# Fine-tuned Qwen3-Embedding for Italian Semantic Retrieval
 
-This model is a specialized fine-tuned version of [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) optimized for cross-lingual semantic retrieval tasks, with particular emphasis on Italian query understanding and multilingual document ranking.
+This model is a specialized fine-tuned version of [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) optimized for Italian semantic retrieval tasks, with particular emphasis on Italian query understanding and document ranking.
 
 ## Model Description
 
@@ -24,16 +43,16 @@ This model is a specialized fine-tuned version of [Qwen/Qwen3-Embedding-0.6B](ht
 - **Base Model**: [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B)
 - **Output Dimensionality**: 1,024-dimensional dense vectors
 - **Maximum Sequence Length**: 32,768 tokens
-- **Primary Languages**: Italian, English
+- **Primary Language**: Italian
 - **Similarity Function**: Cosine similarity
 
 ## Capabilities
 
-### Cross-Lingual Retrieval
-The model demonstrates strong performance in matching Italian queries to English documents and vice versa, particularly effective in technical and academic domains.
+### Italian Semantic Retrieval
+The model demonstrates strong performance in matching Italian queries to Italian documents, particularly effective in technical and academic domains within the Italian language context.
 
 ### Domain Coverage
-Trained on diverse knowledge domains including:
+Trained on diverse Italian knowledge domains including:
 - **Medical & Health Sciences**: Diagnostic imaging, clinical procedures, medical terminology
 - **STEM Fields**: Physics, computer science, geology, engineering
 - **Professional Domains**: Finance, law, agriculture, software development
@@ -41,18 +60,18 @@ Trained on diverse knowledge domains including:
 
 ### Query Understanding
 Enhanced comprehension of:
-- Conversational and informal query patterns
-- Technical terminology across domains
-- Cross-lingual semantic concepts
-- Complex multi-faceted questions
+- Conversational and informal Italian query patterns
+- Technical terminology in Italian across domains
+- Italian semantic concepts and nuances
+- Complex multi-faceted questions in Italian
 
 ## Training Data
 
-The model was fine-tuned on a curated corpus of Italian-English cross-lingual data, featuring high-quality triplets designed to capture semantic nuances across multiple domains. The dataset emphasizes:
+The model was fine-tuned on a curated corpus of Italian semantic data, featuring high-quality triplets designed to capture semantic nuances across multiple domains. The dataset emphasizes:
 
 - **Hard negative mining**: Strategic inclusion of semantically related but incorrect documents
-- **Cross-lingual alignment**: Balanced representation of Italian-English language pairs
-- **Domain diversity**: Comprehensive coverage of academic, professional, and conversational contexts
+- **Italian language focus**: Comprehensive representation of Italian language patterns
+- **Domain diversity**: Comprehensive coverage of academic, professional, and conversational contexts in Italian
 - **Quality curation**: Manual review and automated filtering for coherence and relevance
 
 ## Usage
@@ -63,12 +82,12 @@ from sentence_transformers import SentenceTransformer
 
 model = SentenceTransformer("your-model-name")
 
-# Cross-lingual query-document matching
+# Italian query-document matching
 query = "Come si distingue una faglia trascorrente da una normale?"
 documents = [
-    "Strike-slip faults are characterized by horizontal movement...",
-    "Normal faults occur due to extensional stress...",
-    "Investment portfolio management strategies..."
+    "Le faglie trascorrenti sono caratterizzate da movimento orizzontale...",
+    "Le faglie normali si verificano a causa di stress estensionale...",
+    "Le strategie di gestione del portafoglio di investimenti..."
 ]
 
 query_embedding = model.encode(query, prompt="Represent this search query for finding relevant passages: ")
@@ -83,17 +102,17 @@ The model is optimized for specific prompt templates:
 
 ## Applications
 
-- **Cross-lingual information retrieval systems**
-- **Academic and technical document search**
-- **Multilingual question-answering platforms**
-- **Educational content recommendation**
-- **Professional knowledge base systems**
+- **Italian information retrieval systems**
+- **Academic and technical document search in Italian**
+- **Italian question-answering platforms**
+- **Educational content recommendation for Italian speakers**
+- **Professional knowledge base systems in Italian**
 
 ## Limitations
 
-- **Language coverage**: Primarily optimized for Italian-English pairs
+- **Language coverage**: Specifically optimized for Italian language
 - **Domain specificity**: Performance may vary on highly specialized domains not represented in training
-- **Cultural context**: Reflects primarily Western/European knowledge perspectives
+- **Cultural context**: Reflects primarily Italian/European knowledge perspectives
 - **Computational requirements**: Dense representations require significant storage for large-scale deployment
 
 ## Model Architecture
@@ -110,7 +129,7 @@ SentenceTransformer(
 
 ```bibtex
 @misc{qwen3-italian-retrieval-2024,
-  title={Fine-tuned Qwen3-Embedding for Italian-English Cross-Lingual Semantic Retrieval},
+  title={Fine-tuned Qwen3-Embedding for Italian Semantic Retrieval},
   year={2024},
   howpublished={\\url{https://huggingface.co/your-model-name}}
 }