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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: 0.8336713995943205 |
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- name: Recall |
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type: recall |
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value: 0.8162859980139027 |
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- name: F1 |
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type: f1 |
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value: 0.8248871048670346 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9459025442866462 |
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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: 0.3195 |
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- Precision: 0.8337 |
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- Recall: 0.8163 |
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- F1: 0.8249 |
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- Accuracy: 0.9459 |
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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: 10 |
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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 | 0.4382 | 0.5831 | 0.5124 | 0.5455 | 0.8761 | |
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| 0.4959 | 2.0 | 522 | 0.2885 | 0.7147 | 0.7289 | 0.7217 | 0.9171 | |
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| 0.4959 | 3.0 | 783 | 0.2778 | 0.7592 | 0.7200 | 0.7390 | 0.9308 | |
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| 0.1547 | 4.0 | 1044 | 0.2552 | 0.8016 | 0.7865 | 0.7940 | 0.9389 | |
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| 0.1547 | 5.0 | 1305 | 0.2718 | 0.7908 | 0.7885 | 0.7897 | 0.9389 | |
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| 0.061 | 6.0 | 1566 | 0.2881 | 0.8360 | 0.7795 | 0.8068 | 0.9426 | |
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| 0.061 | 7.0 | 1827 | 0.2908 | 0.8008 | 0.8143 | 0.8075 | 0.9446 | |
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| 0.023 | 8.0 | 2088 | 0.3195 | 0.8337 | 0.8163 | 0.8249 | 0.9459 | |
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| 0.023 | 9.0 | 2349 | 0.3190 | 0.8208 | 0.8143 | 0.8175 | 0.9476 | |
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| 0.0088 | 10.0 | 2610 | 0.3318 | 0.8354 | 0.7964 | 0.8155 | 0.9458 | |
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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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