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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- information-retrieval |
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- semantic-search |
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widget: |
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- source_sentence: >- |
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Descrivi dettagliatamente il processo chimico e fisico che avviene durante |
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la preparazione di un impasto per crostata |
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sentences: |
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- >- |
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## La Magia Chimica e Fisica nell'Impasto della Crostata: Un Viaggio Dagli |
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Ingredienti Secchi al Trionfo del Forno |
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La preparazione di una crostata, apparentemente un gesto semplice e |
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familiare, cela in realtà un affascinante balletto di reazioni chimiche e |
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trasformazioni fisiche... |
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- >- |
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## L'Arte Effimera: Creare un Dolce Paesaggio Invernale |
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Immergiamoci nel cuore pulsante della pasticceria festiva, dove l'arte |
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culinaria si fonde con la creatività artistica... |
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- >- |
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Le piattaforme di comunicazione digitale, con la loro ubiquità crescente, si |
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configurano come un'arma a doppio taglio nel panorama sociale |
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contemporaneo... |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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language: |
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- it |
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license: apache-2.0 |
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--- |
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<p align="center"> |
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<img src="benchmark.png" style="max-width: 1024px; width: 100%; height: auto;"/> |
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</p> |
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<h1 style="font-size: 48px; text-align: center;">Ita-Search 🇮🇹</h1> |
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# Fine-tuned Qwen3-Embedding for Italian Semantic Retrieval |
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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. |
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## Model Description |
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- **Model Type**: Dense embedding model for semantic retrieval |
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- **Base Model**: [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) |
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- **Output Dimensionality**: 1,024-dimensional dense vectors |
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- **Maximum Sequence Length**: 32,768 tokens |
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- **Primary Language**: Italian |
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- **Similarity Function**: Cosine similarity |
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## Capabilities |
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### Italian Semantic Retrieval |
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The model demonstrates strong performance in matching Italian queries to Italian documents, particularly effective in technical and academic domains within the Italian language context. |
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### Domain Coverage |
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Trained on diverse Italian knowledge domains including: |
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- **Medical & Health Sciences**: Diagnostic imaging, clinical procedures, medical terminology |
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- **STEM Fields**: Physics, computer science, geology, engineering |
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- **Professional Domains**: Finance, law, agriculture, software development |
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- **Educational Content**: Historical studies, culinary arts, general knowledge |
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### Query Understanding |
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Enhanced comprehension of: |
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- Conversational and informal Italian query patterns |
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- Technical terminology in Italian across domains |
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- Italian semantic concepts and nuances |
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- Complex multi-faceted questions in Italian |
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## Training Data |
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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: |
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- **Hard negative mining**: Strategic inclusion of semantically related but incorrect documents |
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- **Italian language focus**: Comprehensive representation of Italian language patterns |
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- **Domain diversity**: Comprehensive coverage of academic, professional, and conversational contexts in Italian |
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- **Quality curation**: Manual review and automated filtering for coherence and relevance |
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## Usage |
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### Basic Retrieval |
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```python |
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from sentence_transformers import SentenceTransformer |
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model = SentenceTransformer("DeepMount00/Ita-Search") |
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# Italian query-document matching |
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query = "Come si distingue una faglia trascorrente da una normale?" |
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documents = [ |
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"Le faglie trascorrenti sono caratterizzate da movimento orizzontale...", |
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"Le faglie normali si verificano a causa di stress estensionale...", |
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"Le strategie di gestione del portafoglio di investimenti..." |
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] |
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query_embedding = model.encode(query, prompt="Represent this search query for finding relevant passages: ") |
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doc_embeddings = model.encode(documents, prompt="Represent this passage for retrieval: ") |
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similarities = model.similarity(query_embedding, doc_embeddings) |
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``` |
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### Prompt Templates |
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The model is optimized for specific prompt templates: |
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- **Queries**: `"Represent this search query for finding relevant passages: "` |
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- **Documents**: `"Represent this passage for retrieval: "` |
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## Applications |
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- **Italian information retrieval systems** |
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- **Academic and technical document search in Italian** |
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- **Italian question-answering platforms** |
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- **Educational content recommendation for Italian speakers** |
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- **Professional knowledge base systems in Italian** |
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## Limitations |
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- **Language coverage**: Specifically optimized for Italian language |
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- **Domain specificity**: Performance may vary on highly specialized domains not represented in training |
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## Acknowledgments |
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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. |
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--- |
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**License**: Inherits licensing terms from the base Qwen/Qwen3-Embedding-0.6B model. |
