---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_212
  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_212

This model is a fine-tuned version of [AnonymousCS/populism_xlmr_large](https://huggingface.co/AnonymousCS/populism_xlmr_large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2752
- Accuracy: 0.9801
- 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-06
- train_batch_size: 16
- eval_batch_size: 16
- 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: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:--------:|:-----------:|:------------:|
| 0.6258        | 1.0   | 29   | 0.3913          | 0.9801   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.6476        | 2.0   | 58   | 0.3397          | 0.9801   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.5355        | 3.0   | 87   | 0.3180          | 0.9801   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.6818        | 4.0   | 116  | 0.3074          | 0.9801   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.4494        | 5.0   | 145  | 0.2971          | 0.9801   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.652         | 6.0   | 174  | 0.2886          | 0.9801   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.8645        | 7.0   | 203  | 0.2775          | 0.9801   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.7489        | 8.0   | 232  | 0.2806          | 0.9801   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.5238        | 9.0   | 261  | 0.2815          | 0.9801   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.5113        | 10.0  | 290  | 0.2797          | 0.9801   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.5769        | 11.0  | 319  | 0.2755          | 0.9801   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.592         | 12.0  | 348  | 0.2747          | 0.9801   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.513         | 13.0  | 377  | 0.2757          | 0.9801   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.4696        | 14.0  | 406  | 0.2750          | 0.9801   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.5106        | 15.0  | 435  | 0.2752          | 0.9801   | 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