Upload README.md

README.md

@@ -0,0 +1,125 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- information-retrieval
+- semantic-search
+widget:
+- 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..."
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# Fine-tuned Qwen3-Embedding for Italian-English Cross-Lingual 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.
+
+## Model Description
+
+- **Model Type**: Dense embedding model for semantic retrieval
+- **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
+- **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.
+
+### Domain Coverage
+Trained on diverse 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
+- **Educational Content**: Historical studies, culinary arts, general knowledge
+
+### Query Understanding
+Enhanced comprehension of:
+- Conversational and informal query patterns
+- Technical terminology across domains
+- Cross-lingual semantic concepts
+- Complex multi-faceted questions
+
+## 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:
+
+- **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
+- **Quality curation**: Manual review and automated filtering for coherence and relevance
+
+## Usage
+
+### Basic Retrieval
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("your-model-name")
+
+# Cross-lingual 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..."
+]
+
+query_embedding = model.encode(query, prompt="Represent this search query for finding relevant passages: ")
+doc_embeddings = model.encode(documents, prompt="Represent this passage for retrieval: ")
+similarities = model.similarity(query_embedding, doc_embeddings)
+```
+
+### Prompt Templates
+The model is optimized for specific prompt templates:
+- **Queries**: `"Represent this search query for finding relevant passages: "`
+- **Documents**: `"Represent this passage for retrieval: "`
+
+## Applications
+
+- **Cross-lingual information retrieval systems**
+- **Academic and technical document search**
+- **Multilingual question-answering platforms**
+- **Educational content recommendation**
+- **Professional knowledge base systems**
+
+## Limitations
+
+- **Language coverage**: Primarily optimized for Italian-English pairs
+- **Domain specificity**: Performance may vary on highly specialized domains not represented in training
+- **Cultural context**: Reflects primarily Western/European knowledge perspectives
+- **Computational requirements**: Dense representations require significant storage for large-scale deployment
+
+## Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 32768, 'architecture': 'Qwen3Model'})
+  (1): Pooling({'pooling_mode_lasttoken': True, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Citation
+
+```bibtex
+@misc{qwen3-italian-retrieval-2024,
+  title={Fine-tuned Qwen3-Embedding for Italian-English Cross-Lingual Semantic Retrieval},
+  year={2024},
+  howpublished={\\url{https://huggingface.co/your-model-name}}
+}
+```
+
+## Acknowledgments
+
+This work builds upon the Qwen3-Embedding architecture and advances in contrastive learning for dense retrieval. We acknowledge the contributions of the Qwen team and the sentence-transformers community.
+
+---
+
+**License**: Inherits licensing terms from the base Qwen/Qwen3-Embedding-0.6B model.