---
language:
- it
pipeline_tag: token-classification
---

# Universal NER for Italian (Zero-Shot)

## Model Description
This model is designed for Named Entity Recognition (NER) tasks, specifically tailored for the Italian language. It employs a zero-shot learning approach, enabling it to identify a wide range of entities without the need for specific training on those entities. This makes it incredibly versatile for various applications requiring entity extraction from Italian text.

## Model Performance
- **Inference Time:** The model runs on CPUs, with an inference time of 0.01 seconds on a GPU. Performance on a CPU will vary depending on the specific hardware configuration.

## Try It Out
You can test the model directly in your browser through the following Hugging Face Spaces link: [https://huggingface.co/spaces/DeepMount00/universal_ner](https://huggingface.co/spaces/DeepMount00/universal_ner).

It's important to note that **this model is universal and operates across all domains**. However, if you are seeking performance metrics close to a 98/99% F1 score for a specific domain, you are encouraged to reach out via email to Michele Montebovi at montebovi.michele@gmail.com. This direct contact allows for the possibility of customizing the model to achieve enhanced performance tailored to your unique entity recognition requirements in the Italian language.