---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
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-google-bert/bert-base-multilingual-cased-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: 0.9529745042492918
    - name: Recall
      type: recall
      value: 0.9236683141131247
    - name: F1
      type: f1
      value: 0.9380925822643614
    - name: Accuracy
      type: accuracy
      value: 0.9788954787029192
---

<!-- 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-google-bert/bert-base-multilingual-cased-hausa-ner-v1

This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the Beijuka/Multilingual_PII_NER_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1237
- Precision: 0.9530
- Recall: 0.9237
- F1: 0.9381
- Accuracy: 0.9789

## 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  | 0.1502          | 0.8451    | 0.8843 | 0.8643 | 0.9526   |
| 0.2112        | 2.0   | 602  | 0.1347          | 0.8573    | 0.9393 | 0.8964 | 0.9604   |
| 0.2112        | 3.0   | 903  | 0.1241          | 0.8813    | 0.9398 | 0.9096 | 0.9668   |
| 0.0847        | 4.0   | 1204 | 0.1770          | 0.8589    | 0.9460 | 0.9004 | 0.9640   |
| 0.0619        | 5.0   | 1505 | 0.1295          | 0.9012    | 0.9146 | 0.9078 | 0.9673   |
| 0.0619        | 6.0   | 1806 | 0.1502          | 0.9018    | 0.9254 | 0.9134 | 0.9683   |
| 0.0394        | 7.0   | 2107 | 0.1801          | 0.8729    | 0.9506 | 0.9101 | 0.9661   |
| 0.0394        | 8.0   | 2408 | 0.1807          | 0.9119    | 0.9321 | 0.9219 | 0.9705   |
| 0.0236        | 9.0   | 2709 | 0.1660          | 0.9259    | 0.9187 | 0.9223 | 0.9719   |
| 0.0124        | 10.0  | 3010 | 0.1878          | 0.8939    | 0.9496 | 0.9209 | 0.9705   |
| 0.0124        | 11.0  | 3311 | 0.2095          | 0.8874    | 0.9486 | 0.9170 | 0.9693   |
| 0.01          | 12.0  | 3612 | 0.2370          | 0.8814    | 0.9480 | 0.9135 | 0.9664   |


### Framework versions

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