---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- named-entity-recognition
- lumasaba
- 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-lumasaba-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: 0.9801980198019802
    - name: Recall
      type: recall
      value: 0.945859872611465
    - name: F1
      type: f1
      value: 0.9627228525121556
    - name: Accuracy
      type: accuracy
      value: 0.9528795811518325
---

<!-- 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-lumasaba-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: 0.3746
- Precision: 0.9802
- Recall: 0.9459
- F1: 0.9627
- Accuracy: 0.9529

## 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   | 398  | 0.7058          | 0.7983    | 0.7704 | 0.7841 | 0.7637   |
| 1.0932        | 2.0   | 796  | 0.4247          | 0.8727    | 0.8933 | 0.8829 | 0.8807   |
| 0.3981        | 3.0   | 1194 | 0.4242          | 0.8830    | 0.9218 | 0.9020 | 0.9055   |
| 0.2187        | 4.0   | 1592 | 0.4187          | 0.9194    | 0.9194 | 0.9194 | 0.9190   |
| 0.2187        | 5.0   | 1990 | 0.3810          | 0.9433    | 0.9487 | 0.9460 | 0.9383   |
| 0.108         | 6.0   | 2388 | 0.4557          | 0.9701    | 0.9251 | 0.9471 | 0.9338   |
| 0.0769        | 7.0   | 2786 | 0.4815          | 0.9330    | 0.9406 | 0.9367 | 0.9293   |
| 0.0401        | 8.0   | 3184 | 0.4978          | 0.9602    | 0.9430 | 0.9515 | 0.9401   |
| 0.0384        | 9.0   | 3582 | 0.5352          | 0.9437    | 0.9422 | 0.9430 | 0.9356   |
| 0.0384        | 10.0  | 3980 | 0.5006          | 0.9436    | 0.9536 | 0.9486 | 0.9374   |
| 0.0181        | 11.0  | 4378 | 0.5544          | 0.9481    | 0.9528 | 0.9504 | 0.9388   |


### Framework versions

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