Modernbert Ner Conll2003

🚀 ModernBERT NER (CoNLL2003)

This model is a fine - tuned version of [answerdotai/ModernBERT - base](https://huggingface.co/answerdotai/ModernBERT - base) on the conll2003 dataset for Named Entity Recognition (NER). It demonstrates robust performance in tasks related to recognizing Persons, Organizations, and Locations.

On the evaluation set, it achieves the following results:

• Loss: 0.0992
• Precision: 0.8349
• Recall: 0.8563
• F1: 0.8455
• Accuracy: 0.9752

🚀 Quick Start

This model is a fine - tuned version of [answerdotai/ModernBERT - base](https://huggingface.co/answerdotai/ModernBERT - base) on the conll2003 dataset for Named Entity Recognition (NER). It can effectively recognize Persons, Organizations, and Locations.

✨ Features

• Robust performance on Named Entity Recognition tasks for Persons, Organizations, and Locations.
• Achieved high precision, recall, F1 - score, and accuracy on the conll2003 evaluation set.

📚 Documentation

Model Details

Training Data

The model is fine - tuned on the CoNLL2003 dataset, a well - known benchmark for NER. This dataset provides a solid foundation for the model to generalize on general English text.

💻 Usage Examples

Basic Usage

from transformers import pipeline

ner = pipeline(task="token - classification", model="IsmaelMousa/modernbert - ner - conll2003", aggregation_strategy="max")

results = ner("Hi, I'm Ismael Mousa from Palestine working for NVIDIA inc.")

for entity in results:
    for key, value in entity.items():
        if key == "entity_group":
            print(f"{entity['word']} => {entity[key]}")

Expected Results

Ismael Mousa => PER
Palestine => LOC
NVIDIA => ORG

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e - 06
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use 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: 10

Training results

Framework versions

• Transformers 4.48.0.dev0
• Pytorch 2.2.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0

📄 License

This project is licensed under the apache - 2.0 license.