---
library_name: transformers
license: apache-2.0
base_model: google/mt5-base
tags:
- named-entity-recognition
- luganda
- 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/mt5-base-luganda-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: 0.828140703517588
    - name: Recall
      type: recall
      value: 0.5567567567567567
    - name: F1
      type: f1
      value: 0.6658585858585858
    - name: Accuracy
      type: accuracy
      value: 0.9215278267616562
---

<!-- 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/mt5-base-luganda-ner-v1

This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the Beijuka/Multilingual_PII_NER_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3334
- Precision: 0.8281
- Recall: 0.5568
- F1: 0.6659
- Accuracy: 0.9215

## 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   | 261  | 1.1064          | 0.1333    | 0.0021 | 0.0041 | 0.8000   |
| 1.773         | 2.0   | 522  | 1.1335          | 0.0       | 0.0    | 0.0    | 0.7945   |
| 1.773         | 3.0   | 783  | 1.0490          | 0.1818    | 0.0021 | 0.0041 | 0.7955   |
| 1.0922        | 4.0   | 1044 | 0.9485          | 0.3810    | 0.0251 | 0.0471 | 0.8030   |
| 1.0922        | 5.0   | 1305 | 0.8640          | 0.4933    | 0.0387 | 0.0717 | 0.8093   |
| 0.9396        | 6.0   | 1566 | 0.6608          | 0.5948    | 0.1902 | 0.2882 | 0.8404   |
| 0.9396        | 7.0   | 1827 | 0.5730          | 0.6781    | 0.2685 | 0.3847 | 0.8569   |
| 0.6952        | 8.0   | 2088 | 0.4691          | 0.6998    | 0.3605 | 0.4759 | 0.8768   |
| 0.6952        | 9.0   | 2349 | 0.4007          | 0.7271    | 0.4399 | 0.5482 | 0.9004   |
| 0.5088        | 10.0  | 2610 | 0.4192          | 0.6621    | 0.5037 | 0.5721 | 0.8947   |
| 0.5088        | 11.0  | 2871 | 0.4036          | 0.6886    | 0.5361 | 0.6028 | 0.9005   |
| 0.4013        | 12.0  | 3132 | 0.3698          | 0.7103    | 0.5381 | 0.6124 | 0.9093   |
| 0.4013        | 13.0  | 3393 | 0.3491          | 0.7279    | 0.5423 | 0.6216 | 0.9137   |
| 0.351         | 14.0  | 3654 | 0.3207          | 0.8056    | 0.5413 | 0.6475 | 0.9242   |
| 0.351         | 15.0  | 3915 | 0.3423          | 0.7697    | 0.5413 | 0.6356 | 0.9197   |
| 0.308         | 16.0  | 4176 | 0.3359          | 0.7783    | 0.5465 | 0.6421 | 0.9220   |
| 0.308         | 17.0  | 4437 | 0.3334          | 0.7713    | 0.5496 | 0.6419 | 0.9216   |


### Framework versions

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