| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: AnonymousCS/populism_multilingual_bert_uncased_v2 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: populism_classifier_175 |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # populism_classifier_175 |
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| This model is a fine-tuned version of [AnonymousCS/populism_multilingual_bert_uncased_v2](https://huggingface.co/AnonymousCS/populism_multilingual_bert_uncased_v2) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1557 |
| - Accuracy: 0.9740 |
| - 1-f1: 0.6818 |
| - 1-recall: 0.7895 |
| - 1-precision: 0.6 |
| - Balanced Acc: 0.8851 |
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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: |
| - learning_rate: 1e-05 |
| - train_batch_size: 128 |
| - eval_batch_size: 128 |
| - seed: 42 |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 20 |
| - mixed_precision_training: Native AMP |
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|
| ### Training results |
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|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| |
| | 0.1233 | 1.0 | 17 | 0.0913 | 0.9703 | 0.6923 | 0.9474 | 0.5455 | 0.9592 | |
| | 0.2358 | 2.0 | 34 | 0.1491 | 0.9758 | 0.7111 | 0.8421 | 0.6154 | 0.9114 | |
| | 0.0301 | 3.0 | 51 | 0.1557 | 0.9740 | 0.6818 | 0.7895 | 0.6 | 0.8851 | |
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| ### Framework versions |
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| - Transformers 4.46.3 |
| - Pytorch 2.4.1+cu121 |
| - Datasets 3.1.0 |
| - Tokenizers 0.20.3 |
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