expertai/LLaMAntino-3-SLIMER-IT
expertai/LLaMAntino-3-SLIMER-IT is an 8 billion parameter language model built upon Meta Llama 3 and fine-tuned from swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA. This model is specifically instructed for zero-shot Named Entity Recognition (NER) in the Italian language. It excels at identifying never-before-seen entity tags by leveraging a prompt structure enriched with definitions and guidelines for the target entities. Its primary use case is advanced NER tasks on Italian text, particularly when dealing with novel or custom entity types.
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expertai/LLaMAntino-3-SLIMER-IT: Zero-Shot NER for Italian
expertai/LLaMAntino-3-SLIMER-IT is an 8 billion parameter large language model (LLM) developed by expertai, based on Meta Llama 3. It is specifically instruction-tuned for zero-shot Named Entity Recognition (NER) on the Italian language, building upon the swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA model.
Key Capabilities
- Zero-Shot NER: Designed to identify named entities without prior examples for specific tags.
- Prompt-Driven Entity Extraction: Leverages a unique prompting mechanism that includes a DEFINITION and GUIDELINES for the named entity to be extracted, enabling it to tackle novel entity types.
- Italian Language Focus: Optimized for performance on Italian text.
- Targeted Entity Recognition: While instructed on a reduced set of common tags (PER, ORG, LOC), its strength lies in its ability to generalize to new, user-defined entity categories.
Use Cases
This model is particularly well-suited for:
- Custom NER tasks in Italian: When standard NER models lack specific entity types required for a project.
- Rapid prototyping: Quickly extracting new entity types from Italian text without extensive re-training.
- Information extraction: Identifying specific, context-dependent information from unstructured Italian documents.
For more technical details and usage examples, refer to the GitHub repository and the associated arXiv paper.