Modernbert Italian Finetuned Ner

🚀 modernbert-italian-finetuned-ner

This model is a fine - tuned version of DeepMount00/Italian - ModernBERT - base on the tner/wikiann dataset. It is designed for token classification, specifically for Named Entity Recognition (NER) in the Italian language, achieving excellent results on the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of DeepMount00/Italian - ModernBERT - base on tner/wikiann dataset. It achieves the following results on the evaluation set:

• Loss: 0.0422
• Precision: 0.9339
• Recall: 0.9452
• F1: 0.9395
• Accuracy: 0.9909

✨ Features

• Token Classification: Specialized in token classification tasks, especially NER for the Italian language.
• Fine - Tuned: Based on a pre - trained model and fine - tuned on a specific dataset for better performance.

💻 Usage Examples

Basic Usage

from transformers import pipeline
ner_pipeline = pipeline("ner", model="nickprock/modernbert-italian-finetuned-ner", aggregation_strategy="simple")
text = "La sede storica della Olivetti è ad Ivrea"
output = ner_pipeline(text)

📚 Documentation

Intended uses & limitations

The model can be used on token classification, in particular NER. It is fine tuned on italian language.

Training and evaluation data

The dataset used is wikiann

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e - 05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon = 1e - 08 and optimizer_args = No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Framework versions

• Transformers 4.48.3
• Pytorch 2.5.1+cu124
• Datasets 3.3.2
• Tokenizers 0.21.0

📄 License

This model is released under the MIT license.