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--- |
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language: |
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- it |
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pipeline_tag: token-classification |
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--- |
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# Universal NER for Italian (Zero-Shot) |
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## Model Description |
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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. |
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## Model Performance |
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- **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. |
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## Try It Out |
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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). |
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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 [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. |
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# Installation |
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To use this model, you must download the GLiNER repository and install its dependencies!!: |
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``` |
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!git clone https://github.com/urchade/GLiNER.git |
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%cd GLiNER |
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!pip install -r requirements.txt |
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``` |
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# Usage |
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```python |
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from model import GLiNER |
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model = GLiNER.from_pretrained("DeepMount00/universal_ner_ita") |
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text = """ |
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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. |
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""" |
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labels = ["comune", "codice fiscale", "importo", "società", "indirizzo", "data di completamento"] |
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entities = model.predict_entities(text, labels) |
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max_length = max(len(entity["text"]) for entity in entities) |
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for entity in entities: |
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padded_text = entity["text"].ljust(max_length) |
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print(f"{padded_text} => {entity['label']}") |
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``` |
