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--- |
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library_name: transformers |
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license: mit |
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base_model: roberta-base |
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tags: |
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- named-entity-recognition |
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- kanuri |
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- african-language |
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- pii-detection |
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- token-classification |
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- generated_from_trainer |
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datasets: |
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- Beijuka/Multilingual_PII_NER_dataset |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: multilingual-roberta-base-kanuri-ner-v1 |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: Beijuka/Multilingual_PII_NER_dataset |
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type: Beijuka/Multilingual_PII_NER_dataset |
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args: 'split: train+validation+test' |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9576167076167076 |
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- name: Recall |
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type: recall |
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value: 0.9301909307875895 |
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- name: F1 |
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type: f1 |
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value: 0.9437046004842615 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9867406100327704 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# multilingual-roberta-base-kanuri-ner-v1 |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the Beijuka/Multilingual_PII_NER_dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0957 |
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- Precision: 0.9576 |
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- Recall: 0.9302 |
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- F1: 0.9437 |
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- Accuracy: 0.9867 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 301 | 0.1120 | 0.8716 | 0.8372 | 0.8541 | 0.9691 | |
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| 0.187 | 2.0 | 602 | 0.0885 | 0.8735 | 0.9206 | 0.8964 | 0.9750 | |
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| 0.187 | 3.0 | 903 | 0.0975 | 0.8666 | 0.8911 | 0.8787 | 0.9742 | |
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| 0.0664 | 4.0 | 1204 | 0.0992 | 0.8715 | 0.9194 | 0.8948 | 0.9764 | |
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| 0.0458 | 5.0 | 1505 | 0.0900 | 0.9008 | 0.9228 | 0.9116 | 0.9767 | |
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| 0.0458 | 6.0 | 1806 | 0.0900 | 0.9050 | 0.9267 | 0.9157 | 0.9800 | |
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| 0.0311 | 7.0 | 2107 | 0.1075 | 0.8921 | 0.9328 | 0.9120 | 0.9787 | |
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| 0.0311 | 8.0 | 2408 | 0.1353 | 0.8920 | 0.9311 | 0.9111 | 0.9791 | |
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| 0.0215 | 9.0 | 2709 | 0.1167 | 0.9090 | 0.9267 | 0.9177 | 0.9792 | |
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| 0.0109 | 10.0 | 3010 | 0.1201 | 0.9082 | 0.9289 | 0.9184 | 0.9807 | |
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| 0.0109 | 11.0 | 3311 | 0.1304 | 0.9110 | 0.9272 | 0.9191 | 0.9810 | |
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| 0.0064 | 12.0 | 3612 | 0.1823 | 0.8918 | 0.9344 | 0.9126 | 0.9788 | |
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| 0.0064 | 13.0 | 3913 | 0.1507 | 0.9038 | 0.9289 | 0.9162 | 0.9803 | |
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| 0.0042 | 14.0 | 4214 | 0.1763 | 0.8990 | 0.935 | 0.9167 | 0.9807 | |
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### Framework versions |
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- Transformers 4.55.4 |
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- Pytorch 2.8.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.21.4 |
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