File size: 3,144 Bytes
215017d 85a9e9d 215017d 85a9e9d 215017d 85a9e9d 215017d 85a9e9d 215017d 85a9e9d 215017d 85a9e9d 215017d 85a9e9d 215017d 85a9e9d 215017d 85a9e9d 215017d 85a9e9d 215017d 85a9e9d 215017d 85a9e9d 215017d 85a9e9d 215017d 85a9e9d 215017d 85a9e9d 215017d 85a9e9d 215017d 85a9e9d 215017d 85a9e9d 215017d d64a36a 215017d 85a9e9d 215017d 85a9e9d 215017d 85a9e9d 215017d 85a9e9d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | ---
datasets:
- ner_dataset_2.jsonl
language:
- en
license: apache-2.0
model-index:
- name: ner-distilbert-base-cased
results:
- dataset:
name: ner_dataset_2.jsonl
type: ner_dataset_2.jsonl
metrics:
- name: Eval Loss
type: eval_loss
value: 0.0216
- name: Eval Accuracy
type: eval_accuracy
value: 0.993
- name: Eval F1
type: eval_f1
value: 0.9929
- name: Eval Recall
type: eval_recall
value: 0.993
- name: Eval Precision
type: eval_precision
value: 0.9933
task:
name: Ner
type: token-classification
tags:
- ner
- sklearn
- mlflow
- transformers
- openchs
---
# ner-distilbert-base-cased
This model performs ner trained using MLflow and deployed on Hugging Face.
## Model Details
- **Model Name:** ner-distilbert-base-cased
- **Version:** 4
- **Task:** Ner
- **Languages:** en
- **Framework:** sklearn
- **License:** apache-2.0
## Intended Uses & Limitations
### Intended Uses
- Ner tasks
- Research and development
- Child helpline services support
### Limitations
- Performance may vary on out-of-distribution data
- Should be evaluated on your specific use case before production deployment
- Designed for child helpline contexts, may need adaptation for other domains
## Training Data
- **Dataset:** ner_dataset_2.jsonl
- **Size:** Not specified
- **Languages:** en
## Training Configuration
| 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 |
## Performance Metrics
### Evaluation Results
| 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 |
## Usage
### Installation
```bash
pip install transformers torch
```
### Named Entity Recognition Example
```python
from transformers import pipeline
ner = pipeline("ner", model="openchs/ner_distillbert_v1", aggregation_strategy="simple")
text = "John Smith works at OpenCHS in Nairobi and can be reached at john@email.com"
entities = ner(text)
for entity in entities:
print(f"{entity['entity_group']}: {entity['word']} (score: {entity['score']:.2f})")
```
## MLflow Tracking
- **Experiment:** NER_Distilbert/marlon
- **Run ID:** `10d2648a456a4f6ab74022a9e45c9f40`
- **Training Date:** 2025-10-30 11:58:48
- **Tracking URI:** http://192.168.10.6:5000
## Training Metrics Visualization
View detailed training metrics and TensorBoard logs in the [Training metrics](https://huggingface.co/openchs/ner_distillbert_v1/tensorboard) tab.
## Citation
```bibtex
@misc{ner_distilbert_base_cased,
title={ner-distilbert-base-cased},
author={OpenCHS Team},
year={2025},
publisher={Hugging Face},
url={https://huggingface.co/openchs/ner_distillbert_v1}
}
```
## Contact
info@bitz-itc.com
---
*Model card auto-generated from MLflow*
|