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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: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: PhilippinesPoliBERT |
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results: [] |
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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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# PhilippinesPoliBERT |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2115 |
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- Regionalism F1: 0.9786 |
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- Regionalism Accuracy: 0.9775 |
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- Clientelism F1: 0.9601 |
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- Clientelism Accuracy: 0.961 |
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- Economic Policy F1: 0.9521 |
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- Economic Policy Accuracy: 0.952 |
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- Security F1: 0.9602 |
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- Security Accuracy: 0.962 |
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- Discipline Among Poor F1: 0.9767 |
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- Discipline Among Poor Accuracy: 0.9775 |
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- Populism F1: 0.9020 |
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- Populism Accuracy: 0.9015 |
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- Marcos Duterte Alliance F1: 0.9447 |
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- Marcos Duterte Alliance Accuracy: 0.9485 |
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- Uniteam Positive Campaign F1: 0.8936 |
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- Uniteam Positive Campaign Accuracy: 0.894 |
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- Overall F1: 0.9460 |
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- Overall Accuracy: 0.9467 |
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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: 7e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 16 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Regionalism F1 | Regionalism Accuracy | Clientelism F1 | Clientelism Accuracy | Economic Policy F1 | Economic Policy Accuracy | Security F1 | Security Accuracy | Discipline Among Poor F1 | Discipline Among Poor Accuracy | Populism F1 | Populism Accuracy | Marcos Duterte Alliance F1 | Marcos Duterte Alliance Accuracy | Uniteam Positive Campaign F1 | Uniteam Positive Campaign Accuracy | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------:|:--------------------:|:--------------:|:--------------------:|:------------------:|:------------------------:|:-----------:|:-----------------:|:------------------------:|:------------------------------:|:-----------:|:-----------------:|:--------------------------:|:--------------------------------:|:----------------------------:|:----------------------------------:|:----------:|:----------------:| |
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| 0.6703 | 1.0 | 250 | 0.4869 | 0.9487 | 0.9635 | 0.8803 | 0.883 | 0.8468 | 0.856 | 0.8181 | 0.8405 | 0.9647 | 0.9695 | 0.5545 | 0.6345 | 0.8339 | 0.839 | 0.6386 | 0.701 | 0.8107 | 0.8359 | |
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| 0.2993 | 2.0 | 500 | 0.2892 | 0.9746 | 0.977 | 0.9420 | 0.9465 | 0.9424 | 0.9435 | 0.9245 | 0.9335 | 0.9713 | 0.975 | 0.7850 | 0.792 | 0.8923 | 0.905 | 0.8420 | 0.854 | 0.9092 | 0.9158 | |
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| 0.2011 | 3.0 | 750 | 0.2276 | 0.9692 | 0.9705 | 0.9513 | 0.9535 | 0.9488 | 0.949 | 0.9504 | 0.9535 | 0.9743 | 0.976 | 0.8702 | 0.8705 | 0.9290 | 0.9355 | 0.8991 | 0.9 | 0.9366 | 0.9386 | |
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| 0.143 | 4.0 | 1000 | 0.2217 | 0.9803 | 0.9805 | 0.9568 | 0.9575 | 0.9496 | 0.9495 | 0.9555 | 0.9575 | 0.9722 | 0.9725 | 0.8834 | 0.884 | 0.9308 | 0.9365 | 0.8906 | 0.8905 | 0.9399 | 0.9411 | |
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| 0.1029 | 5.0 | 1250 | 0.2258 | 0.9781 | 0.9785 | 0.9579 | 0.9595 | 0.9505 | 0.9515 | 0.9483 | 0.952 | 0.9769 | 0.978 | 0.8945 | 0.894 | 0.9355 | 0.9415 | 0.8867 | 0.8885 | 0.9410 | 0.9429 | |
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| 0.0865 | 6.0 | 1500 | 0.2201 | 0.9795 | 0.98 | 0.9477 | 0.9475 | 0.9468 | 0.9455 | 0.9559 | 0.958 | 0.9776 | 0.978 | 0.9078 | 0.9075 | 0.9255 | 0.9305 | 0.8936 | 0.892 | 0.9418 | 0.9424 | |
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| 0.0796 | 7.0 | 1750 | 0.2157 | 0.9771 | 0.976 | 0.9605 | 0.961 | 0.9579 | 0.958 | 0.9559 | 0.9575 | 0.9715 | 0.9745 | 0.9116 | 0.9115 | 0.9422 | 0.9465 | 0.8929 | 0.8935 | 0.9462 | 0.9473 | |
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| 0.0702 | 8.0 | 2000 | 0.2149 | 0.9797 | 0.9795 | 0.9559 | 0.9565 | 0.9429 | 0.9405 | 0.9545 | 0.9565 | 0.9717 | 0.9735 | 0.8992 | 0.8985 | 0.9403 | 0.9445 | 0.9023 | 0.903 | 0.9433 | 0.9441 | |
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| 0.0685 | 9.0 | 2250 | 0.2115 | 0.9786 | 0.9775 | 0.9601 | 0.961 | 0.9521 | 0.952 | 0.9602 | 0.962 | 0.9767 | 0.9775 | 0.9020 | 0.9015 | 0.9447 | 0.9485 | 0.8936 | 0.894 | 0.9460 | 0.9467 | |
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### Framework versions |
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- Transformers 4.52.4 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 2.14.4 |
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- Tokenizers 0.21.1 |
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