learnrr/roberta-NER-conll2003

Model description

This model is a fine-tuned version of roberta-base for the Named Entity Recognition (NER) task using the CoNLL-2003 dataset. It can identify four types of entities: Persons (PER), Organizations (ORG), Locations (LOC), and Miscellaneous (MISC).

Training procedure

• Hardware: NVIDIA V100 GPU
• Optimizer: AdamW
• Learning Rate: 2e-5
• Batch Size: 16
• Weight Decay: 0.01
• Epochs: 5
• Mixed Precision Training: FP16 enabled

Evaluation Results

Metric)	Value
F1 Score	95.99%
Precision	95.61%
Recall	96.38%
Accuracy	99.29%
Eval Loss	0.0464

How to use

from transformers import pipeline
model_id = "learnrr/roberta-NER-conll2003"
text = "Apple is looking at buying U.K. startup for $1 billion"
results = nlp(text)
for entity in results:
print(f"entity: {entity['word']} | class: {entity['entity_group']} | confidence: {entity['score']:.4f}")