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--- |
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library_name: transformers |
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license: mit |
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base_model: Davlan/afro-xlmr-base |
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tags: |
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- named-entity-recognition |
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- hausa |
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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-Davlan/afro-xlmr-base-hausa-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: 0.9298021697511167 |
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- name: Recall |
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type: recall |
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value: 0.9256670902160101 |
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- name: F1 |
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type: f1 |
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value: 0.9277300222858963 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9811780190852254 |
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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-Davlan/afro-xlmr-base-hausa-ner-v1 |
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This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-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: 0.1152 |
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- Precision: 0.9298 |
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- Recall: 0.9257 |
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- F1: 0.9277 |
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- Accuracy: 0.9812 |
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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 | 0.1139 | 0.8862 | 0.8862 | 0.8862 | 0.9694 | |
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| 0.2008 | 2.0 | 602 | 0.0925 | 0.8741 | 0.9155 | 0.8944 | 0.9729 | |
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| 0.2008 | 3.0 | 903 | 0.0910 | 0.8901 | 0.9125 | 0.9012 | 0.9747 | |
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| 0.0686 | 4.0 | 1204 | 0.1056 | 0.8947 | 0.9263 | 0.9102 | 0.9753 | |
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| 0.0501 | 5.0 | 1505 | 0.0921 | 0.9071 | 0.9305 | 0.9187 | 0.9775 | |
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| 0.0501 | 6.0 | 1806 | 0.0939 | 0.9062 | 0.9377 | 0.9217 | 0.9789 | |
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| 0.036 | 7.0 | 2107 | 0.1034 | 0.8926 | 0.9359 | 0.9137 | 0.9769 | |
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| 0.036 | 8.0 | 2408 | 0.1305 | 0.9019 | 0.9425 | 0.9218 | 0.9779 | |
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| 0.0219 | 9.0 | 2709 | 0.1320 | 0.9037 | 0.9335 | 0.9184 | 0.9778 | |
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| 0.0089 | 10.0 | 3010 | 0.1241 | 0.9271 | 0.9065 | 0.9167 | 0.9781 | |
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| 0.0089 | 11.0 | 3311 | 0.1386 | 0.9184 | 0.9311 | 0.9247 | 0.9791 | |
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| 0.0056 | 12.0 | 3612 | 0.1482 | 0.9094 | 0.9377 | 0.9233 | 0.9788 | |
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| 0.0056 | 13.0 | 3913 | 0.1550 | 0.9109 | 0.9311 | 0.9209 | 0.9783 | |
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| 0.0032 | 14.0 | 4214 | 0.1631 | 0.9078 | 0.9377 | 0.9225 | 0.9792 | |
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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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