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roberta-NER-conll2003
learnrr/roberta-NER-conll2003
roberta-NER-conll2003 is a token classification model from learnrr. Use it when you need labels on individual words, such as names. The card lists the license as mit.
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),…
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At a glance
- Task
- Token Classification
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Apr 12, 2026
- Updated
- Apr 12, 2026
- SHA
- 4df126f9
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Base models
Datasets
Model card
From the Hugging Face model README
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}")
- Task
- Token Classification
- Type
- roberta
- License
- mit
- Languages
- en
- Created
- Apr 12, 2026
- Updated
- Apr 12, 2026