Bert Italian Finetuned Ner

🚀 bert-italian-finetuned-ner

This model is a fine - tuned BERT model for Italian token classification, specifically NER, achieving high precision, recall, F1 - score, and accuracy on the wiki_neural dataset.

🚀 Quick Start

This model is a fine - tuned version of [dbmdz/bert - base - italian - cased](https://huggingface.co/dbmdz/bert - base - italian - cased) on the wiki_neural dataset. It achieves the following results on the evaluation set:

• Loss: 0.0361
• Precision: 0.9438
• Recall: 0.9542
• F1: 0.9490
• Accuracy: 0.9918

✨ Features

• Token Classification: Specialized in token classification tasks, especially NER for the Italian language.
• High Performance: Achieves high precision, recall, F1 - score, and accuracy on the evaluation set.

💻 Usage Examples

Basic Usage

from transformers import pipeline
ner_pipeline = pipeline("ner", model="nickprock/bert-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 the 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: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• num_epochs: 3

Training Results

Framework Versions

• Transformers 4.27.3
• Pytorch 1.13.0
• Datasets 2.1.0
• Tokenizers 0.13.2

📄 License

This model is released under the MIT license.

📦 Additional Information

Model Details

Widget Examples

• Example 1: 'Ciao, sono Giacomo. Vivo a Milano e lavoro da Armani. '
• Example 2: 'Domenica andrò allo stadio con Giovanna a guardare la Fiorentina. '

Model Index

• Name: bert - italian - finetuned - ner
  • Results:
    • Task:
      • Type: token - classification
      • Name: Token Classification
    • Dataset:
      • Name: wiki_neural
      • Type: wiki_neural
      • Config: it
      • Split: validation
      • Args: it
    • Metrics:
      • Type: precision, Value: 0.9438064759036144, Name: Precision
      • Type: recall, Value: 0.954225352112676, Name: Recall
      • Type: f1, Value: 0.9489873178118493, Name: F1
      • Type: accuracy, Value: 0.9917883014379933, Name: Accuracy