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--- |
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library_name: transformers |
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license: mit |
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base_model: AnonymousCS/populism_multilingual_roberta_base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: populism_model70 |
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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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# populism_model70 |
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This model is a fine-tuned version of [AnonymousCS/populism_multilingual_roberta_base](https://huggingface.co/AnonymousCS/populism_multilingual_roberta_base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4714 |
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- Accuracy: 0.8687 |
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- F1: 0.5938 |
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- Recall: 0.7037 |
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- Precision: 0.5135 |
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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: 1e-05 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- optimizer: Use 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: 5 |
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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 | Accuracy | F1 | Recall | Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
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| No log | 1.0 | 7 | 0.3759 | 0.7626 | 0.5155 | 0.9259 | 0.3571 | |
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| No log | 2.0 | 14 | 0.6082 | 0.8838 | 0.6102 | 0.6667 | 0.5625 | |
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| No log | 3.0 | 21 | 0.3812 | 0.8434 | 0.6076 | 0.8889 | 0.4615 | |
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| No log | 4.0 | 28 | 0.3627 | 0.8182 | 0.5714 | 0.8889 | 0.4211 | |
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| No log | 5.0 | 35 | 0.4714 | 0.8687 | 0.5938 | 0.7037 | 0.5135 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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