---
library_name: transformers
license: mit
base_model: Davlan/afro-xlmr-base
tags:
- named-entity-recognition
- hausa
- 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-hausa-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: 0.9298021697511167
    - name: Recall
      type: recall
      value: 0.9256670902160101
    - name: F1
      type: f1
      value: 0.9277300222858963
    - name: Accuracy
      type: accuracy
      value: 0.9811780190852254
---

<!-- 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-hausa-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: 0.1152
- Precision: 0.9298
- Recall: 0.9257
- F1: 0.9277
- Accuracy: 0.9812

## 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  | 0.1139          | 0.8862    | 0.8862 | 0.8862 | 0.9694   |
| 0.2008        | 2.0   | 602  | 0.0925          | 0.8741    | 0.9155 | 0.8944 | 0.9729   |
| 0.2008        | 3.0   | 903  | 0.0910          | 0.8901    | 0.9125 | 0.9012 | 0.9747   |
| 0.0686        | 4.0   | 1204 | 0.1056          | 0.8947    | 0.9263 | 0.9102 | 0.9753   |
| 0.0501        | 5.0   | 1505 | 0.0921          | 0.9071    | 0.9305 | 0.9187 | 0.9775   |
| 0.0501        | 6.0   | 1806 | 0.0939          | 0.9062    | 0.9377 | 0.9217 | 0.9789   |
| 0.036         | 7.0   | 2107 | 0.1034          | 0.8926    | 0.9359 | 0.9137 | 0.9769   |
| 0.036         | 8.0   | 2408 | 0.1305          | 0.9019    | 0.9425 | 0.9218 | 0.9779   |
| 0.0219        | 9.0   | 2709 | 0.1320          | 0.9037    | 0.9335 | 0.9184 | 0.9778   |
| 0.0089        | 10.0  | 3010 | 0.1241          | 0.9271    | 0.9065 | 0.9167 | 0.9781   |
| 0.0089        | 11.0  | 3311 | 0.1386          | 0.9184    | 0.9311 | 0.9247 | 0.9791   |
| 0.0056        | 12.0  | 3612 | 0.1482          | 0.9094    | 0.9377 | 0.9233 | 0.9788   |
| 0.0056        | 13.0  | 3913 | 0.1550          | 0.9109    | 0.9311 | 0.9209 | 0.9783   |
| 0.0032        | 14.0  | 4214 | 0.1631          | 0.9078    | 0.9377 | 0.9225 | 0.9792   |


### Framework versions

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