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.
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 [email protected]. 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.