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--- |
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library_name: transformers |
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language: |
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- lg |
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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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- luganda |
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- african-languages |
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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/Luganda_Monolingual_PII_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: luganda-ner-bert-v7 |
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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/Luganda_Monolingual_PII_dataset |
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type: Beijuka/Luganda_Monolingual_PII_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: 0.8317757009345794 |
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- name: Recall |
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type: recall |
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value: 0.7954319761668321 |
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- name: F1 |
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type: f1 |
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value: 0.8131979695431472 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9486124353891705 |
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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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# luganda-ner-bert-v7 |
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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/Luganda_Monolingual_PII_dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3956 |
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- Precision: 0.8318 |
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- Recall: 0.7954 |
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- F1: 0.8132 |
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- Accuracy: 0.9486 |
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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 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 | 261 | 0.4093 | 0.6488 | 0.4697 | 0.5449 | 0.8871 | |
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| 0.5031 | 2.0 | 522 | 0.3278 | 0.6709 | 0.6842 | 0.6775 | 0.9099 | |
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| 0.5031 | 3.0 | 783 | 0.2721 | 0.7816 | 0.7071 | 0.7424 | 0.9317 | |
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| 0.1642 | 4.0 | 1044 | 0.2540 | 0.7776 | 0.7776 | 0.7776 | 0.9399 | |
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| 0.1642 | 5.0 | 1305 | 0.3182 | 0.7473 | 0.7607 | 0.7539 | 0.9280 | |
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| 0.0775 | 6.0 | 1566 | 0.3038 | 0.7843 | 0.7944 | 0.7893 | 0.9409 | |
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| 0.0775 | 7.0 | 1827 | 0.3407 | 0.8504 | 0.7567 | 0.8008 | 0.9400 | |
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| 0.0362 | 8.0 | 2088 | 0.3267 | 0.7886 | 0.8004 | 0.7945 | 0.9429 | |
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| 0.0362 | 9.0 | 2349 | 0.3229 | 0.8316 | 0.7845 | 0.8074 | 0.9499 | |
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| 0.0201 | 10.0 | 2610 | 0.3521 | 0.8475 | 0.7726 | 0.8083 | 0.9476 | |
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| 0.0201 | 11.0 | 2871 | 0.3629 | 0.8126 | 0.8054 | 0.8090 | 0.9468 | |
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| 0.0095 | 12.0 | 3132 | 0.3817 | 0.8012 | 0.8083 | 0.8047 | 0.9466 | |
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| 0.0095 | 13.0 | 3393 | 0.3956 | 0.8318 | 0.7954 | 0.8132 | 0.9486 | |
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| 0.0057 | 14.0 | 3654 | 0.3798 | 0.8219 | 0.7974 | 0.8095 | 0.9458 | |
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| 0.0057 | 15.0 | 3915 | 0.4196 | 0.8166 | 0.7825 | 0.7992 | 0.9445 | |
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### Framework versions |
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- Transformers 4.53.0 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.2 |
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