README.md

metadata

library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
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-google-bert/bert-base-multilingual-cased-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: 0.9705014749262537
          - name: Recall
            type: recall
            value: 0.9529326574945691
          - name: F1
            type: f1
            value: 0.9616368286445013
          - name: Accuracy
            type: accuracy
            value: 0.9603766182816791

multilingual-google-bert/bert-base-multilingual-cased-lumasaba-ner-v1

This model is a fine-tuned version of google-bert/bert-base-multilingual-cased on the Beijuka/Multilingual_PII_NER_dataset dataset. It achieves the following results on the evaluation set:

• Loss: 0.3203
• Precision: 0.9705
• Recall: 0.9529
• F1: 0.9616
• Accuracy: 0.9604

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	0.6266	0.8401	0.8225	0.8312	0.8062
1.0576	2.0	796	0.3751	0.9033	0.8891	0.8962	0.8859
0.3626	3.0	1194	0.3664	0.9336	0.9273	0.9305	0.9163
0.1629	4.0	1592	0.4134	0.9381	0.9303	0.9342	0.9244
0.1629	5.0	1990	0.3573	0.9497	0.9476	0.9486	0.9417
0.0925	6.0	2388	0.4060	0.9501	0.9416	0.9458	0.9434
0.0516	7.0	2786	0.3767	0.9371	0.9483	0.9427	0.9377
0.0409	8.0	3184	0.4152	0.9450	0.9528	0.9489	0.9409
0.0389	9.0	3582	0.3901	0.9624	0.9386	0.9503	0.9458
0.0389	10.0	3980	0.4474	0.9388	0.9536	0.9461	0.9426
0.0212	11.0	4378	0.3165	0.9591	0.9663	0.9627	0.9547
0.0167	12.0	4776	0.3941	0.9590	0.9633	0.9611	0.9543
0.0199	13.0	5174	0.4243	0.9496	0.9588	0.9542	0.9478
0.0156	14.0	5572	0.4842	0.9539	0.9618	0.9579	0.9494

Framework versions

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