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DeepMount00/
universal_ner_ita

Name: universal_ner_ita
Availability: InStock

DeepMount00/GLINER_ITA

apache-2.0

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Universal NER for Italian (Zero-Shot)

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 90/99% 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.

Try here: https://huggingface.co/spaces/DeepMount00/universal_ner_ita

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_ita.

Installation

To use this model, you must download the GLiNER project:

!pip install gliner

Usage

python

from gliner import GLiNER

model = GLiNER.from_pretrained("DeepMount00/universal_ner_ita")

text = """
Il comune di Castelrosso, con codice fiscale 80012345678, ha approvato il finanziamento di 15.000€ destinati alla ristrutturazione del parco giochi cittadino, affidando l'incarico alla società 'Verde Vivo Società Cooperativa', con sede legale in Corso della Libertà 45, Verona, da completarsi entro il 30/09/2024.
"""

labels = ["comune", "codice fiscale", "importo", "società", "indirizzo", "data di completamento"]

entities = model.predict_entities(text, labels)

max_length = max(len(entity["text"]) for entity in entities)

for entity in entities:
    padded_text = entity["text"].ljust(max_length)
    print(f"{padded_text} => {entity['label']}")