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library_name: transformers
license: apache-2.0
base_model: AnonymousCS/populism_english_bert_base_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_348
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_348
This model is a fine-tuned version of [AnonymousCS/populism_english_bert_base_uncased](https://huggingface.co/AnonymousCS/populism_english_bert_base_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6292
- Accuracy: 0.9752
- 1-f1: 0.5714
- 1-recall: 0.4828
- 1-precision: 0.7
- Balanced Acc: 0.7377
## 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: 64
- eval_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.6498 | 1.0 | 53 | 0.2639 | 0.9741 | 0.5769 | 0.5172 | 0.6522 | 0.7537 |
| 0.1127 | 2.0 | 106 | 0.1869 | 0.9422 | 0.5149 | 0.8966 | 0.3611 | 0.9202 |
| 0.1483 | 3.0 | 159 | 0.4548 | 0.9717 | 0.5556 | 0.5172 | 0.6 | 0.7525 |
| 0.0249 | 4.0 | 212 | 0.6187 | 0.9705 | 0.4681 | 0.3793 | 0.6111 | 0.6854 |
| 0.0134 | 5.0 | 265 | 0.6292 | 0.9752 | 0.5714 | 0.4828 | 0.7 | 0.7377 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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