---
library_name: transformers
license: mit
base_model: Davlan/afro-xlmr-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-Davlan/afro-xlmr-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: 0.9328358208955224
    - name: Recall
      type: recall
      value: 0.9529860228716646
    - name: F1
      type: f1
      value: 0.9428032683846638
    - name: Accuracy
      type: accuracy
      value: 0.9857189865087199
---

<!-- 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-Davlan/afro-xlmr-base-kanuri-ner-v1

This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the Beijuka/Multilingual_PII_NER_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0769
- Precision: 0.9328
- Recall: 0.9530
- F1: 0.9428
- Accuracy: 0.9857

## 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  | 0.1158          | 0.8646    | 0.8610 | 0.8628 | 0.9683   |
| 0.2058        | 2.0   | 602  | 0.0876          | 0.8848    | 0.9431 | 0.9130 | 0.9751   |
| 0.2058        | 3.0   | 903  | 0.0854          | 0.9078    | 0.9143 | 0.9110 | 0.9783   |
| 0.0658        | 4.0   | 1204 | 0.1092          | 0.8847    | 0.9383 | 0.9107 | 0.9755   |
| 0.0491        | 5.0   | 1505 | 0.0881          | 0.9046    | 0.9431 | 0.9234 | 0.9782   |
| 0.0491        | 6.0   | 1806 | 0.1227          | 0.9015    | 0.9323 | 0.9166 | 0.9770   |
| 0.0298        | 7.0   | 2107 | 0.1005          | 0.9218    | 0.9461 | 0.9338 | 0.9805   |
| 0.0298        | 8.0   | 2408 | 0.1454          | 0.8970    | 0.9395 | 0.9178 | 0.9774   |
| 0.0164        | 9.0   | 2709 | 0.1301          | 0.9146    | 0.9305 | 0.9225 | 0.9789   |
| 0.0089        | 10.0  | 3010 | 0.1297          | 0.9215    | 0.9425 | 0.9319 | 0.9806   |


### Framework versions

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