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Beijuka
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mt5-base-hausa-ner-v1

Token Classification
Transformers
Safetensors
mt5
named-entity-recognition
hausa
african-language
pii-detection
Generated from Trainer
Eval Results (legacy)
Model card Files
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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/mt5-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-google/mt5-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: 0.9484978540772532
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+ - name: Recall
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+ type: recall
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+ value: 0.8977657413676371
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+ - name: F1
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+ type: f1
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+ value: 0.9224347826086956
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9826865281588919
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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/mt5-base-hausa-ner-v1
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+
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+ 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.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1053
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+ - Precision: 0.9485
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+ - Recall: 0.8978
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+ - F1: 0.9224
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+ - Accuracy: 0.9827
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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 | 301 | 0.2665 | 0.7902 | 0.7083 | 0.7470 | 0.9413 |
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+ | 0.9875 | 2.0 | 602 | 0.2028 | 0.8688 | 0.8055 | 0.8360 | 0.9588 |
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+ | 0.9875 | 3.0 | 903 | 0.1779 | 0.8880 | 0.8120 | 0.8483 | 0.9613 |
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+ | 0.2051 | 4.0 | 1204 | 0.2090 | 0.8725 | 0.8506 | 0.8614 | 0.9638 |
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+ | 0.157 | 5.0 | 1505 | 0.1443 | 0.9138 | 0.8397 | 0.8752 | 0.9677 |
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+ | 0.157 | 6.0 | 1806 | 0.1434 | 0.9186 | 0.8571 | 0.8867 | 0.9706 |
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+ | 0.1285 | 7.0 | 2107 | 0.1401 | 0.9102 | 0.8680 | 0.8886 | 0.9713 |
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+ | 0.1285 | 8.0 | 2408 | 0.1483 | 0.9079 | 0.8693 | 0.8882 | 0.9706 |
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+ | 0.1101 | 9.0 | 2709 | 0.1349 | 0.9274 | 0.8635 | 0.8943 | 0.9732 |
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+ | 0.0985 | 10.0 | 3010 | 0.1217 | 0.9232 | 0.8667 | 0.8941 | 0.9734 |
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+ | 0.0985 | 11.0 | 3311 | 0.1501 | 0.9190 | 0.8764 | 0.8972 | 0.9734 |
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+ | 0.0879 | 12.0 | 3612 | 0.1474 | 0.9047 | 0.8802 | 0.8923 | 0.9727 |
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+ | 0.0879 | 13.0 | 3913 | 0.1323 | 0.9245 | 0.8828 | 0.9032 | 0.9746 |
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+ | 0.0786 | 14.0 | 4214 | 0.1496 | 0.9155 | 0.8796 | 0.8972 | 0.9743 |
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+ | 0.0721 | 15.0 | 4515 | 0.1489 | 0.9238 | 0.8815 | 0.9021 | 0.9754 |
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+ | 0.0721 | 16.0 | 4816 | 0.1399 | 0.9188 | 0.8892 | 0.9038 | 0.9759 |
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+ | 0.0652 | 17.0 | 5117 | 0.1377 | 0.9149 | 0.8925 | 0.9035 | 0.9763 |
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+ | 0.0652 | 18.0 | 5418 | 0.1476 | 0.9158 | 0.8970 | 0.9063 | 0.9772 |
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+ | 0.0539 | 19.0 | 5719 | 0.1403 | 0.9189 | 0.8976 | 0.9081 | 0.9774 |
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+ | 0.0534 | 20.0 | 6020 | 0.1404 | 0.9190 | 0.8989 | 0.9089 | 0.9776 |
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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