---
language: en
license: mit
base_model: roberta-base
tags:
- token-classification
- ner
- named-entity-recognition
datasets:
- conll2003
metrics:
- f1
- precision
- recall
- accuracy
model-index:
- name: RoBERTa-base-NER-CoNLL2003
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      type: conll2003
      name: CoNLL-2003 (English)
    metrics:
      - type: f1
        value: 95.99
---

## 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
```python
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}")