ModernBERT-large Fine-tuned on CoNLL-2003 for NER

This model is a fine-tuned version of answerdotai/ModernBERT-large on the CoNLL-2003 dataset for Named Entity Recognition (NER).

ModernBERT's architecture allows for efficient processing of long sequences and features optimized attention mechanisms, making it an excellent backbone for dense token-classification tasks like NER.

Model Description

• Developed by: Rúben Garrido
• Model type: ModernBERT (Encoder-only Transformer)
• Task: Named Entity Recognition (NER)
• Labels: O, B-PER, I-PER, B-ORG, I-ORG, B-LOC, I-LOC, B-MISC, I-MISC

Intended Uses & Limitations

This model is intended for identifying entities (Persons, Organizations, Locations, and Miscellaneous) in English text.

How to use

from transformers import pipeline

ner_pipeline = pipeline("ner", model="RGarrido03/modernbert-conll2003-ner-large", aggregation_strategy="simple")
text = "The CERN headquarters are located in Geneva, Switzerland."
results = ner_pipeline(text)

for entity in results:
    print(f"Entity: {entity['word']}, Label: {entity['entity_group']}, Score: {entity['score']:.4f}")

Training Data

The model was trained on the CoNLL-2003 dataset, which consists of Reuters news stories from 1996 and 1997.

• Train samples: 14,041
• Validation samples: 3,250
• Test samples: 3,453

Training Procedure

Training Hyperparameters

The following hyperparameters were used during training:

• Learning rate: 5e-5 (with AdamW optimizer)
• Batch size: 8
• Epochs: 3.0
• Weight decay: 0.01
• Warmup ratio: 0.1
• Max sequence length: 256
• Label all tokens: True (subword pieces inherit parent labels)

Training Results (Evaluation on Test Split)

Evaluation on Validation Split

Environmental Impact

• Runtime: ~44 minutes
• Hardware: MacBook Pro, M5 Pro 24GB

Citation

If you use this model, please cite the original CoNLL-2003 paper and the ModernBERT work.

@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
    title = "Introduction to the {CoNLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
    author = "Tjong Kim Sang, Erik F.  and De Meulder, Fien",
    booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
    year = "2003",
    url = "https://aclanthology.org/W03-0419",
    pages = "142--147",
}