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| Parameter | Value |
|---|---|
| Author | Rogendo |
| Batch Size | 4 |
| Epochs | 10 |
| Lr | 2e-05 |
| Model Name | distilbert-base-cased |
| Test Size | 0.1 |
| Training Date | 2025-10-30T11:58:48.315647 |
| Weight Decay | 0.01 |
| Metric | Value |
|---|---|
| Epoch | 10.0000 |
| Eval Accuracy | 0.9930 |
| Eval F1 | 0.9929 |
| Eval Loss | 0.0216 |
| Eval Precision | 0.9933 |
| Eval Recall | 0.9930 |
| Eval Runtime | 0.1509 |
| Eval Samples Per Second | 106.0170 |
| Eval Steps Per Second | 13.2520 |
pip install transformers torch1from transformers import pipeline
2
3ner = pipeline("ner", model="marlonbino/ner-distilbert-base-cased", aggregation_strategy="simple")
4text = "John Smith works at OpenCHS in Nairobi and can be reached at john@email.com"
5entities = ner(text)
6
7for entity in entities:
8 print(f"{entity['entity_group']}: {entity['word']} (score: {entity['score']:.2f})")10d2648a456a4f6ab74022a9e45c9f401@misc{ner_distilbert_base_cased,
2 title={ner-distilbert-base-cased},
3 author={OpenCHS Team},
4 year={2025},
5 publisher={Hugging Face},
6 url={https://huggingface.co/marlonbino/ner-distilbert-base-cased}
7}