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library_name: transformers
license: apache-2.0
base_model: AnonymousCS/populism_english_bert_large_cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_320
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_320
This model is a fine-tuned version of [AnonymousCS/populism_english_bert_large_cased](https://huggingface.co/AnonymousCS/populism_english_bert_large_cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2639
- Accuracy: 0.9570
- 1-f1: 0.6341
- 1-recall: 0.4815
- 1-precision: 0.9286
- Balanced Acc: 0.7392
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.3596 | 1.0 | 22 | 0.1814 | 0.9542 | 0.75 | 0.8889 | 0.6486 | 0.9243 |
| 0.0678 | 2.0 | 44 | 0.1569 | 0.9312 | 0.6571 | 0.8519 | 0.5349 | 0.8949 |
| 0.0053 | 3.0 | 66 | 0.2965 | 0.9570 | 0.7273 | 0.7407 | 0.7143 | 0.8579 |
| 0.0034 | 4.0 | 88 | 0.6768 | 0.9656 | 0.7273 | 0.5926 | 0.9412 | 0.7947 |
| 0.001 | 5.0 | 110 | 1.2639 | 0.9570 | 0.6341 | 0.4815 | 0.9286 | 0.7392 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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