---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- named-entity-recognition
- kanuri
- african-language
- pii-detection
- token-classification
- generated_from_trainer
datasets:
- Beijuka/Multilingual_PII_NER_dataset
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: multilingual-roberta-base-kanuri-ner-v1
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: Beijuka/Multilingual_PII_NER_dataset
      type: Beijuka/Multilingual_PII_NER_dataset
      args: 'split: train+validation+test'
    metrics:
    - name: Precision
      type: precision
      value: 0.9576167076167076
    - name: Recall
      type: recall
      value: 0.9301909307875895
    - name: F1
      type: f1
      value: 0.9437046004842615
    - name: Accuracy
      type: accuracy
      value: 0.9867406100327704
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# multilingual-roberta-base-kanuri-ner-v1

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the Beijuka/Multilingual_PII_NER_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0957
- Precision: 0.9576
- Recall: 0.9302
- F1: 0.9437
- Accuracy: 0.9867

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 301  | 0.1120          | 0.8716    | 0.8372 | 0.8541 | 0.9691   |
| 0.187         | 2.0   | 602  | 0.0885          | 0.8735    | 0.9206 | 0.8964 | 0.9750   |
| 0.187         | 3.0   | 903  | 0.0975          | 0.8666    | 0.8911 | 0.8787 | 0.9742   |
| 0.0664        | 4.0   | 1204 | 0.0992          | 0.8715    | 0.9194 | 0.8948 | 0.9764   |
| 0.0458        | 5.0   | 1505 | 0.0900          | 0.9008    | 0.9228 | 0.9116 | 0.9767   |
| 0.0458        | 6.0   | 1806 | 0.0900          | 0.9050    | 0.9267 | 0.9157 | 0.9800   |
| 0.0311        | 7.0   | 2107 | 0.1075          | 0.8921    | 0.9328 | 0.9120 | 0.9787   |
| 0.0311        | 8.0   | 2408 | 0.1353          | 0.8920    | 0.9311 | 0.9111 | 0.9791   |
| 0.0215        | 9.0   | 2709 | 0.1167          | 0.9090    | 0.9267 | 0.9177 | 0.9792   |
| 0.0109        | 10.0  | 3010 | 0.1201          | 0.9082    | 0.9289 | 0.9184 | 0.9807   |
| 0.0109        | 11.0  | 3311 | 0.1304          | 0.9110    | 0.9272 | 0.9191 | 0.9810   |
| 0.0064        | 12.0  | 3612 | 0.1823          | 0.8918    | 0.9344 | 0.9126 | 0.9788   |
| 0.0064        | 13.0  | 3913 | 0.1507          | 0.9038    | 0.9289 | 0.9162 | 0.9803   |
| 0.0042        | 14.0  | 4214 | 0.1763          | 0.8990    | 0.935  | 0.9167 | 0.9807   |


### Framework versions

- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4