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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: google/rembert |
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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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model-index: |
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- name: populism_classifier_410 |
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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_classifier_410 |
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This model is a fine-tuned version of [google/rembert](https://huggingface.co/google/rembert) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7024 |
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- Accuracy: 0.1263 |
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- 1-f1: 0.2242 |
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- 1-recall: 1.0 |
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- 1-precision: 0.1263 |
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- Balanced Acc: 0.5 |
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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: 16 |
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- eval_batch_size: 32 |
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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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- lr_scheduler_warmup_ratio: 0.06 |
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- num_epochs: 20 |
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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 | 1-f1 | 1-recall | 1-precision | Balanced Acc | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| |
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| 0.6697 | 1.0 | 50 | 0.6939 | 0.1263 | 0.2242 | 1.0 | 0.1263 | 0.5 | |
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| 0.6672 | 2.0 | 100 | 0.7424 | 0.1263 | 0.2242 | 1.0 | 0.1263 | 0.5 | |
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| 0.8553 | 3.0 | 150 | 0.8350 | 0.1263 | 0.2242 | 1.0 | 0.1263 | 0.5 | |
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| 0.6019 | 4.0 | 200 | 0.7917 | 0.1263 | 0.2242 | 1.0 | 0.1263 | 0.5 | |
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| 0.5024 | 5.0 | 250 | 0.6939 | 0.1414 | 0.2273 | 1.0 | 0.1282 | 0.5087 | |
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| 0.6982 | 6.0 | 300 | 0.7261 | 0.1364 | 0.2262 | 1.0 | 0.1276 | 0.5058 | |
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| 0.8583 | 7.0 | 350 | 0.7103 | 0.1263 | 0.2242 | 1.0 | 0.1263 | 0.5 | |
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| 0.5487 | 8.0 | 400 | 0.7149 | 0.1364 | 0.2262 | 1.0 | 0.1276 | 0.5058 | |
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| 0.5309 | 9.0 | 450 | 0.7218 | 0.1313 | 0.2252 | 1.0 | 0.1269 | 0.5029 | |
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| 0.5436 | 10.0 | 500 | 0.6868 | 0.1919 | 0.2381 | 1.0 | 0.1351 | 0.5376 | |
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| 0.5617 | 11.0 | 550 | 0.6876 | 0.1919 | 0.2381 | 1.0 | 0.1351 | 0.5376 | |
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| 0.8479 | 12.0 | 600 | 0.6812 | 0.1919 | 0.2381 | 1.0 | 0.1351 | 0.5376 | |
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| 1.4176 | 13.0 | 650 | 0.6805 | 0.1818 | 0.2358 | 1.0 | 0.1337 | 0.5318 | |
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| 0.3742 | 14.0 | 700 | 0.6920 | 0.1566 | 0.2304 | 1.0 | 0.1302 | 0.5173 | |
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| 0.7671 | 15.0 | 750 | 0.6905 | 0.1465 | 0.2283 | 1.0 | 0.1289 | 0.5116 | |
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| 0.994 | 16.0 | 800 | 0.6967 | 0.1313 | 0.2252 | 1.0 | 0.1269 | 0.5029 | |
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| 0.3561 | 17.0 | 850 | 0.6962 | 0.1414 | 0.2273 | 1.0 | 0.1282 | 0.5087 | |
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| 0.5991 | 18.0 | 900 | 0.7024 | 0.1263 | 0.2242 | 1.0 | 0.1263 | 0.5 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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