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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_bsample_158
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_bsample_158
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: 1.5657
- Accuracy: 0.5803
- 1-f1: 0.1233
- 1-recall: 1.0
- 1-precision: 0.0657
- Balanced Acc: 0.7838
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.1124 | 1.0 | 4 | 0.8614 | 0.7082 | 0.1682 | 1.0 | 0.0918 | 0.8497 |
| 0.0806 | 2.0 | 8 | 0.8140 | 0.7279 | 0.1782 | 1.0 | 0.0978 | 0.8598 |
| 0.0244 | 3.0 | 12 | 1.1187 | 0.6426 | 0.1417 | 1.0 | 0.0763 | 0.8159 |
| 0.0819 | 4.0 | 16 | 0.8136 | 0.7443 | 0.1522 | 0.7778 | 0.0843 | 0.7605 |
| 0.0985 | 5.0 | 20 | 1.0607 | 0.6951 | 0.1622 | 1.0 | 0.0882 | 0.8429 |
| 0.0429 | 6.0 | 24 | 1.5657 | 0.5803 | 0.1233 | 1.0 | 0.0657 | 0.7838 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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