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library_name: transformers
license: apache-2.0
base_model: google/rembert
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_407
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_407
This model is a fine-tuned version of [google/rembert](https://huggingface.co/google/rembert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7175
- Accuracy: 0.9424
- 1-f1: 0.0
- 1-recall: 0.0
- 1-precision: 0.0
- Balanced Acc: 0.5
## 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: 16
- 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
- lr_scheduler_warmup_ratio: 0.06
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:--------:|:-----------:|:------------:|
| 0.34 | 1.0 | 122 | 0.7042 | 0.9424 | 0.0 | 0.0 | 0.0 | 0.5 |
| 1.2546 | 2.0 | 244 | 0.6342 | 0.9424 | 0.0 | 0.0 | 0.0 | 0.5 |
| 2.3291 | 3.0 | 366 | 0.9843 | 0.9403 | 0.0 | 0.0 | 0.0 | 0.4989 |
| 0.7034 | 4.0 | 488 | 1.0438 | 0.9424 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.5225 | 5.0 | 610 | 0.8167 | 0.9424 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.7212 | 6.0 | 732 | 0.7319 | 0.9424 | 0.0 | 0.0 | 0.0 | 0.5 |
| 1.3857 | 7.0 | 854 | 0.7175 | 0.9424 | 0.0 | 0.0 | 0.0 | 0.5 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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