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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: google-bert/bert-base-multilingual-cased
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+ tags:
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+ - named-entity-recognition
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+ - lumasaba
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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-google-bert/bert-base-multilingual-cased-lumasaba-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: 0.9705014749262537
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+ - name: Recall
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+ type: recall
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+ value: 0.9529326574945691
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+ - name: F1
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+ type: f1
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+ value: 0.9616368286445013
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9603766182816791
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+ ---
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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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+
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+ # multilingual-google-bert/bert-base-multilingual-cased-lumasaba-ner-v1
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+
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+ This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) 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: 0.3203
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+ - Precision: 0.9705
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+ - Recall: 0.9529
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+ - F1: 0.9616
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+ - Accuracy: 0.9604
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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 | 398 | 0.6266 | 0.8401 | 0.8225 | 0.8312 | 0.8062 |
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+ | 1.0576 | 2.0 | 796 | 0.3751 | 0.9033 | 0.8891 | 0.8962 | 0.8859 |
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+ | 0.3626 | 3.0 | 1194 | 0.3664 | 0.9336 | 0.9273 | 0.9305 | 0.9163 |
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+ | 0.1629 | 4.0 | 1592 | 0.4134 | 0.9381 | 0.9303 | 0.9342 | 0.9244 |
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+ | 0.1629 | 5.0 | 1990 | 0.3573 | 0.9497 | 0.9476 | 0.9486 | 0.9417 |
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+ | 0.0925 | 6.0 | 2388 | 0.4060 | 0.9501 | 0.9416 | 0.9458 | 0.9434 |
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+ | 0.0516 | 7.0 | 2786 | 0.3767 | 0.9371 | 0.9483 | 0.9427 | 0.9377 |
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+ | 0.0409 | 8.0 | 3184 | 0.4152 | 0.9450 | 0.9528 | 0.9489 | 0.9409 |
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+ | 0.0389 | 9.0 | 3582 | 0.3901 | 0.9624 | 0.9386 | 0.9503 | 0.9458 |
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+ | 0.0389 | 10.0 | 3980 | 0.4474 | 0.9388 | 0.9536 | 0.9461 | 0.9426 |
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+ | 0.0212 | 11.0 | 4378 | 0.3165 | 0.9591 | 0.9663 | 0.9627 | 0.9547 |
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+ | 0.0167 | 12.0 | 4776 | 0.3941 | 0.9590 | 0.9633 | 0.9611 | 0.9543 |
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+ | 0.0199 | 13.0 | 5174 | 0.4243 | 0.9496 | 0.9588 | 0.9542 | 0.9478 |
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+ | 0.0156 | 14.0 | 5572 | 0.4842 | 0.9539 | 0.9618 | 0.9579 | 0.9494 |
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+
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+
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+ ### Framework versions
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+
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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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