---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-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-microsoft/deberta-v3-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: 0.9415322580645161
    - name: Recall
      type: recall
      value: 0.9415322580645161
    - name: F1
      type: f1
      value: 0.9415322580645161
    - name: Accuracy
      type: accuracy
      value: 0.9854707843260583
---

<!-- 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-microsoft/deberta-v3-base-kanuri-ner-v1

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the Beijuka/Multilingual_PII_NER_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0800
- Precision: 0.9415
- Recall: 0.9415
- F1: 0.9415
- Accuracy: 0.9855

## 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  | 0.0974          | 0.8852    | 0.8857 | 0.8854 | 0.9718   |
| 0.1637        | 2.0   | 602  | 0.0877          | 0.8894    | 0.9194 | 0.9042 | 0.9756   |
| 0.1637        | 3.0   | 903  | 0.0788          | 0.8860    | 0.9276 | 0.9063 | 0.9758   |
| 0.0643        | 4.0   | 1204 | 0.1024          | 0.8899    | 0.9238 | 0.9065 | 0.9772   |
| 0.0463        | 5.0   | 1505 | 0.0785          | 0.9248    | 0.9130 | 0.9188 | 0.9774   |
| 0.0463        | 6.0   | 1806 | 0.0940          | 0.9132    | 0.9289 | 0.9210 | 0.9795   |
| 0.0316        | 7.0   | 2107 | 0.1033          | 0.8974    | 0.9276 | 0.9123 | 0.9770   |
| 0.0316        | 8.0   | 2408 | 0.1152          | 0.8884    | 0.9302 | 0.9088 | 0.9781   |
| 0.0179        | 9.0   | 2709 | 0.1308          | 0.8975    | 0.9289 | 0.9129 | 0.9782   |


### Framework versions

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