---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: populism_model2
  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_model2

This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4895
- Accuracy: 0.9149
- F1: 0.4051
- Recall: 0.5479
- Precision: 0.3213

## 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: 128
- eval_batch_size: 128
- 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.6178        | 1.0   | 87   | 0.4434          | 0.7889   | 0.2829 | 0.7877 | 0.1724    |
| 0.4174        | 2.0   | 174  | 0.4149          | 0.7589   | 0.2885 | 0.9247 | 0.1709    |
| 0.3132        | 3.0   | 261  | 0.4303          | 0.9008   | 0.3744 | 0.5616 | 0.2808    |
| 0.2642        | 4.0   | 348  | 0.4344          | 0.9015   | 0.4138 | 0.6575 | 0.3019    |
| 0.2153        | 5.0   | 435  | 0.4895          | 0.9149   | 0.4051 | 0.5479 | 0.3213    |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0